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Journal articles on the topic 'MRI mathematical analysis'
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Özcan, Alpay, Kenneth H. Wong, Linda Larson-Prior, Zang-Hee Cho, and Seong K. Mun. "Background and mathematical analysis of diffusion MRI methods." International Journal of Imaging Systems and Technology 22, no. 1 (February 14, 2012): 44–52. http://dx.doi.org/10.1002/ima.22001.
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BOUCHET, A., F. BENALCÁZAR PALACIOS, M. BRUN, and V. L. BALLARIN. "PERFORMANCE ANALYSIS OF FUZZY MATHEMATICAL MORPHOLOGY OPERATORS ON NOISY MRI." Latin American Applied Research - An international journal 44, no. 3 (July 31, 2014): 231–36. http://dx.doi.org/10.52292/j.laar.2014.446.
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Despite a large amount of publications on Fuzzy Mathematical Morphology, little effort was done on systematic evaluation of the performance of this technique. The goal of this work is to compare the robustness against noise of Fuzzy and non Fuzzy Morphological operators when applied to noisy images. Magnetic Resonance Images (MRI) of the brain are a kind of images containing some characteristics that make fuzzy operators an interesting choice, because of their intrinsic noise and imprecision. The robustness was evaluated as the degree in which the results of the operators are not affected by artificial noise in the images. In the analysis we compared different implementation of Fuzzy Mathematical Morphology, and observed that in most of the cases they show higher robustness against noise than the classical morphological operators.
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Yufang, Bao. "Mathematical Analysis of SMASH-Based Reconstruction Methods for Parallel MRI." International Journal of Intelligent Computing in Medical Sciences & Image Processing 4, no. 1 (January 2011): 65–76. http://dx.doi.org/10.1080/1931308x.2011.10644183.
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Noyel, Guillaume, Jesus Angulo, Dominique Jeulin, Daniel Balvay, and Charles-André Cuenod. "MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES." Image Analysis & Stereology 34, no. 1 (May 30, 2014): 1. http://dx.doi.org/10.5566/ias.1109.
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We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.
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KP, Dr Uma Anand, and Dr Justin Moses. "Mathematical analysis of femoral version controversies in MRI and axial oblique CT measurement." International Journal of Orthopaedics Sciences 6, no. 2 (April 1, 2020): 24–26. http://dx.doi.org/10.22271/ortho.2020.v6.i2a.2012.
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Borbély, Katalin, Miklós Emri, István Kenessey, Márton Tóth, Júlia Singer, Péter Barsi, Zsolt Vajda, et al. "PET/MRI in the Presurgical Evaluation of Patients with Epilepsy: A Concordance Analysis." Biomedicines 10, no. 5 (April 20, 2022): 949. http://dx.doi.org/10.3390/biomedicines10050949.
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The aim of our prospective study was to evaluate the clinical impact of hybrid [18F]-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ([18F]-FDG PET/MRI) on the decision workflow of epileptic patients with discordant electroclinical and MRI data. A novel mathematical model was introduced for a clinical concordance calculation supporting the classification of our patients by subgroups of clinical decisions. Fifty-nine epileptic patients with discordant clinical and diagnostic results or MRI negativity were included in this study. The diagnostic value of the PET/MRI was compared to other modalities of presurgical evaluation (e.g., electroclinical data, PET, and MRI). The results of the population-level statistical analysis of the introduced data fusion technique and concordance analysis demonstrated that this model could be the basis for the development of a more accurate clinical decision support parameter in the future. Therefore, making the establishment of “invasive” (operable and implantable) and “not eligible for any further invasive procedures” groups could be much more exact. Our results confirmed the relevance of PET/MRI with the diagnostic algorithm of presurgical evaluation. The introduction of a concordance analysis could be of high importance in clinical and surgical decision-making in the management of epileptic patients. Our study corroborated previous findings regarding the advantages of hybrid PET/MRI technology over MRI and electroclinical data.
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Truszkiewicz, Adrian, David Aebisher, Zuzanna Bober, Łukasz Ożóg, and Dorota Bartusik-Aebisher. "Radio Frequency MRI coils." European Journal of Clinical and Experimental Medicine 18, no. 1 (2020): 24–27. http://dx.doi.org/10.15584/ejcem.2020.1.5.
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Introduction. Magnetic Resonance Imaging (MRI) coils technology is a powerful improvement for clinical diagnostics. This includes opportunities for mathematical and physical research into coil design. Aim. Here we present the method applied to MRI coil array designs. Material and methods. Analysis of literature and self-research. Results. The coils that emit the radiofrequency pulses are designed similarly. As much as possible, they deliver the same strength of radiofrequency to all voxels within their imaging volume. Surface coils on the other hand are usually not embedded in cylindrical surfaces relatively close to the surface of the body. Conclusion. The presented here results relates to the art of magnetic resonance imaging (MRI) and RF coils design. It finds particular application of RF coils in conjunction with bore type MRI scanners.
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Enyagina, Irina M., Andrey N. Polyakov, Alexey A. Poyda, and Vadim L. Ushakov. "System for Automatic Processing and Analysis of MRI/fMRI Data on the Kurchatov Institute Supercomputer." EPJ Web of Conferences 226 (2020): 03006. http://dx.doi.org/10.1051/epjconf/202022603006.
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This paper presents the Computer Model of the System for Automatic Processing and Analysis of MRI/fMRI tomography data, obtained at the Kurchatov Institute Resource Center “Cognimed”. The System is based on the “Digital Lab” IT-Platform, involving the Kurchatov Institute Supercomputer Cluster HPC4, which allows speeding up the processing of data for groups (2–350 subjects) by parallelization of computations on the supercomputer nodes (1 subject – 1 node). The proposed System allows scientists to remotely use the installed on the supercomputer specialized software to process and analyze MRI/fMRI data; organizes a unified data storage; permits the work with data by web a interface. The System also enables the use of program modules developed by KI researchers which implement mathematical methods to improve data analysis results. As an example of the realization of this Computer Model, the Module “MRI FS” is presented that provides automatic processing and analysis of MRI data using the open specialized software FreeSurfer v.6.0.
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Shevchenko, Olha S., Liliia D. Todoriko, Iryna A. Ovcharenko, Olga O. Pogorelova, and Ihor O. Semianiv. "A MATHEMATICAL MODEL FOR PREDICTING THE OUTCOME OF TREATMENT OF MULTIDRUD-RESISTANT TUBERCULOSIS." Wiadomości Lekarskie 74, no. 7 (2021): 1649–54. http://dx.doi.org/10.36740/wlek202107117.
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The aim: Predicting the effectiveness of treatment for MRI of the lungs by developing a mathematical model to predict treatment outcomes. Materials and methods: 84 patients with MRI of the lungs: group 1 (n = 56) – with signs of effective TB treatment at the end of the intensive phase; group 2 (n = 28) – patients with signs of ineffective treatment. We used the multivariate discriminant analysis method using the statistical environment STATISTICA 13. Results: During the discriminant analysis, the parameters of the clinical blood analysis (monocytes, stab leukocytes, erythrocytes) were selected, which were associated with high (r> 0.5) statistically significant correlations with the levels of MMP-9, TIMP-1, oxyproline and its fractions and aldosterone in the formation of the prognosis. The mathematical model allows, in the form of comparing the results of solving two linear equations and comparing their results, to predict the outcome of treatment: “1” effective treatment, “2” – ineffective treatment. Early prediction of treatment effectiveness is promising, as it allows the use of the developed mathematical model as an additional criterion for the selection of patients for whom surgical treatment is recommended, in order to increase the effectiveness of treatment. Conclusions: An additional criterion for predicting ineffective MRI treatment, along with the criteria provided for by WHO recommendations, is a mathematical model that takes into account probably strong correlation (r = 0.5, p <0.05) between the factors of connective tissue destruction, collagen destruction, aldosterone , and indicators of a clinical blood test (between levels of OBZ and monocytes (r = 0.82, p = 0.00001), OB and monocytes (r = 0.92, p = 0.000001) OB and stab leukocytes (r = – 0.87, p = 0.0003) OBZ and stab leukocytes (r = – 0.53, p = 0.017), aldosterone and ESR.
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Bonizzoni, Francesca, Davide Pradovera, and Michele Ruggeri. "Rational-approximation-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots." Mathematics in Engineering 5, no. 4 (2023): 1–38. http://dx.doi.org/10.3934/mine.2023074.
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<abstract><p>We introduce several spatially adaptive model order reduction approaches tailored to non-coercive elliptic boundary value problems, specifically, parametric-in-frequency Helmholtz problems. The offline information is computed by means of adaptive finite elements, so that each snapshot lives in a different discrete space that resolves the local singularities of the analytical solution and is adjusted to the considered frequency value. A rational surrogate is then assembled adopting either a least-squares or an interpolatory approach, yielding a function-valued version of the the standard rational interpolation method ($ \mathcal{V} $-SRI) and the minimal rational interpolation method (MRI). In the context of building an approximation for linear or quadratic functionals of the Helmholtz solution, we perform several numerical experiments to compare the proposed methodologies. Our simulations show that, for interior resonant problems (whose singularities are encoded by poles on the real axis), the spatially adaptive $ \mathcal{V} $-SRI and MRI work comparably well. Instead, when dealing with exterior scattering problems, whose frequency response is mostly smooth, the $ \mathcal{V} $-SRI method seems to be the best-performing one.</p></abstract>
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Tsirmpas, Charalampos, Kostas Giokas, Dimitra Iliopoulou, and Dimitris Koutsouris. "Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy Cloud Computing Framework." International Journal of Reliable and Quality E-Healthcare 1, no. 4 (October 2012): 1–12. http://dx.doi.org/10.4018/ijrqeh.2012100101.
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Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are two non-invasive techniques that are increasingly being used to identify and quantify biochemical markers associated with certain diseases, e.g., choline in the case of cancer. The associating of MRI/MRS images, patient’s electronic health record, genome information, and environmental factors increase the precision of diagnosis and treatment. The authors present a collaboration framework based on Cloud Computing which allows analysis of MRI/MRS data based on advanced mathematical tools, advanced combination, and link discovery between different data types, so as to increase the precision and consequently avoid non-appropriate therapy and treatment plans.
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Rao, Fan. "APPLICATION OF MAGNETIC RESONANCE IMAGING IN EVALUATING ANKLE MOTION INJURY." Revista Brasileira de Medicina do Esporte 27, no. 3 (September 2021): 253–56. http://dx.doi.org/10.1590/1517-8692202127032021_0130.
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ABSTRACT Introduction Discuss the application of magnetic resonance imaging in evaluating ankle motion injury. Objective Verify the influencing factors of magnetic resource imaging (MRI) diagnosis based on the linear regression algorithm model. Methods The experimental group was diagnosed by MRI, while the control group was diagnosed by plain X-ray. After that, the mathematical model of the linear regression algorithm was constructed. Results It could be concluded that the MRI detection rate was 85.71%, and the X-ray plain film detection rate was 77.14%. The linear regression model analysis showed that the P-value of cartilage injury, tendon fracture, bone contusion, and soft tissue swelling was greater than 0.05. Conclusions MRI has more advantages in the application of ankle joint diagnosis. And ligament injury and joint effusion are the influencing factors of MRI diagnosis, which can highly indicate the authenticity of the injury in the ankle joint. Level of evidence II; Therapeutic studies - investigation of treatment results.
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Yusuf, S. I., Y. M. Aiyesimi, M. Jiya, and O. M. Dada. "Mathematical analysis of discontinuities in the flow field of gas in a cylindrical pipe using diffusion MRI." Nigerian Journal of Technological Research 14, no. 2 (July 15, 2019): 67. http://dx.doi.org/10.4314/njtr.v14i2.9.
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El Harchaoui, Nour-Eddine, Mounir Ait Kerroum, Ahmed Hammouch, Mohamed Ouadou, and Driss Aboutajdine. "Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI." Computational Intelligence and Neuroscience 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/435497.
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The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.
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Ye, Hui, Zaiming Liu, Long Zhou, and Qiang Cai. "Dynamic Observation of the Effect of L-Theanine on Cerebral Ischemia-Reperfusion Injury Using Magnetic Resonance Imaging under Mathematical Model Analysis." Journal of Healthcare Engineering 2021 (October 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/5679665.
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This study was to use the partial differential mathematical model to analyze the magnetic resonance imaging (MRI) images of cerebral ischemia-reperfusion injury (CIRI) and to dynamically observe the role of L-theanine in CIRI based on this. 30 patients with cerebral ischemia in a hospital in a certain area were selected and divided into a cerebral ischemia group and a L-theanine treatment group. The two groups of patients were examined by MRI within 48 hours, and the relative apparent diffusion coefficient (rADC) of the cerebral ischemic part of the patients was determined. The partial differential mathematical model was used for data processing to obtain the function of cerebral ischemia time and infarct area, and the data of patients in the cerebral ischemia group and L-theanine treatment group were compared and analyzed. The results showed that the partial differential mathematical model could effectively analyze the linear relationship between the rADC value and time in the treatment of CIRI using L-theanine. The rADC values of the four points of interest in the L-theanine treatment group all increased with time, and there was a positive correlation between the variables X and Y. In observing the efficacy indicators of L-theanine, the L-theanine treatment group showed a significant advantage in the neurospecific enolase (NSE) content compared with the cerebral ischemia group ( P < 0.01 ), and the neurological function score of the L-theanine treatment group gradually decreased and showed a statistically obvious difference on the 7th day of treatment ( P < 0.05 ). In summary, it was verified in this study that the role of L-theanine in the treatment of CIRI was of a great and positive significance for the subsequent treatment of patients with cerebral ischemia, providing reliable theoretical basis and data basis for clinical treatment of CIRI.
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Meyer-Wiethe, K., G. Seidel, T. Aach, and C. Kier. "Ultrasound Cerebral Perfusion Analysis Based on a Mathematical Model for Diminution Harmonic Imaging." Methods of Information in Medicine 46, no. 03 (2007): 308–13. http://dx.doi.org/10.1160/me9048.
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Summary Objectives: Cerebral vascular diseases are detectable by CT/MRI-based methods. Drawbacks of these methods are that they are expensive, time-consuming and intolerable to critically ill patients. Ultrasound, as an inexpensive bedside method, promises to become an alternative. Among other harmonic imaging methods, the diminution harmonic imaging (DHI) method is known, which determines perfusion-related parameters by analyzing ultrasound contrast agent (UCA) diminution kinetics based on constant UCA infusion. The shortcoming of DHI is that the used mathematical model can only determine these parameters by least squares fitting the model onto the data. Methods: In this work, the underlying mathematical model is further developed such that it becomes possible to directly calculate the parameters from the image data. Furthermore, the new model offers an improved way to estimate the spatial distribution of the destruction coefficient necessary for accurately determining the destruction power of the ultrasound pulse on the contrast agent. Results: The direct calculation of the perfusion coefficient is much faster thanthe former fitting of the model. Perfusion as well as destruction coefficients are displayed as color-coded images. In an example, a region with perfusion deficits (as shown in a MR image of the same patient) is clearly identifiable. Conclusions: Displaying the parameters as color-coded images facilitates result interpretation for the diagnosing physician. The results are preliminary and still have to be validated, but they suggest that the new DHI model improves the significance of ultrasound as a diagnostic help.
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Grishko, P. Yu, A. V. Mishchenko, O. V. Ivko, D. V. Samsonov, and A. M. Karachun. "Treatment monitoring of locally advanced rectal cancer based on multiparametric magnetic resonance tomography." Pelvic Surgery and Oncology 10, no. 1 (August 29, 2020): 20–27. http://dx.doi.org/10.17650/2686-9594-2020-10-1-20-27.
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Objective: to determine the predicting factors for the effectiveness of neoadjuvant treatment in colorectal cancer based on the analysis of overall and relapse-free survival, as well as the possibility of multiparametric magnetic resonance imaging (MRI) in stratifying patients into groups with favorable and unfavorable clinical course.Materials and methods. 112 patients who received preoperative chemoradiotherapy (n = 85) and chemoradiotherapy supplemented with neoadjuvant polychemotherapy (n = 27) followed by surgery were enrolled in retrospective study. To determine the most significant predicting factors and criteria for evaluating the effectiveness of treatment that affect overall and relapse-free survival, Kaplan–Meier estimator and Cox regression were used.Results. The relapse-free survival was significantly affected by the presence or absence of extramural venous invasion according to MRI (mrEMVI) (p = 0.0001), circumferential resection margin status according to pathomorphological data (pCRM) (p = 0.031), change in volume of tumor (mrVolumetric analysis) (p = 0.015), tumor regression grade according to MRI (mrTRG) (p = 0.017) and pathomorphological data (pTRG) (p = 0.038). Independent predictors of overall survival were: extramural venous invasion according to MRI (mrEMVI) (p = 0.0001), posttreatment N staging (p = 0.047) and tumor regression grade according to MRI (mrTRG) (p = 0.059). Based on the most significant MR criteria, a mathematical model was developed to predict the risk of relapse after neoadjuvant treatment.Conclusions. MRI allows stratifying patients into groups with a favorable and unfavorable prognosis at the preoperative stage and optimizing the management of patients after surgery taking into account pathomorphological data.
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Stahn, Alexander, Elmarie Terblanche, and Günther Strobel. "Modeling upper and lower limb muscle volume by bioelectrical impedance analysis." Journal of Applied Physiology 103, no. 4 (October 2007): 1428–35. http://dx.doi.org/10.1152/japplphysiol.01163.2006.
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Most studies employing bioelectrical impedance analysis (BIA) for estimating appendicular skeletal muscle mass using descriptive BIA models rely on statistical rather than biophysical principles. The aim of the present study was to evaluate the feasibility of estimating arm and leg muscle volume (MV) based on multiple bioimpedance measurements and using a recently proposed mathematical model and to compare this technique to conventional segmental BIA at high and low frequencies. MV of the arm and leg, respectively, was determined in 15 young, healthy, active men [age 22 ± 2 (SD) yr, total body fat 15.6 ± 5.1%] by magnetic resonance imaging (MRI) and BIA using a conventional and new bioimpedance model. MRI-determined MV for leg and arm was 6,268 ± 1,099 and 1,173 ± 172 cm3, respectively. Estimated MV by the new BIA model [leg: 6,294 ± 1,155 cm3 (50 kHz), 6,278 ± 1,103 cm3 (500 kHz); arm: 1,216 ± 172 cm3 (50 kHz), 1,155 ± 157 cm3 (500 kHz)] was not statistically different from MRI-determined MV (leg: P= 0.958; arm: P= 0.188). The new BIA model was superior to conventional BIA and performed best at 500 kHz for estimating leg MV as indicated by the lower relative total error [new: 3.6% (500 kHz), 5.2% (50 kHz); conventional: 7.6% (500 kHz) and 8.3% (50 kHz)]. In contrast, the new BIA model, both at 50 and 500 kHz, did not improve the accuracy for estimating arm MV [new: 10.8% (500 kHz), 10.6% (50 kHz); conventional: 11.8% (500 kHz), 11.4% (50 kHz)]. It was concluded that modeling of multiple BIA measurements has advantages for the determination of lower limb muscle volume in healthy, active adult men.
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Jain, Manju, C. S. Rai, and Jai Jain. "A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers." Computational and Mathematical Methods in Medicine 2021 (August 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/7965677.
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We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.
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Joseph, Sushitha Susan, and Aju Dennisan. "Three Dimensional Reconstruction Models for Medical Modalities: A Comprehensive Investigation and Analysis." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 6 (July 27, 2020): 653–68. http://dx.doi.org/10.2174/1573405615666190124165855.
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Background: Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field. Discussion: This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed. Conclusion: The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.
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Avola, Danilo, Luigi Cinque, and Giuseppe Placidi. "Customized First and Second Order Statistics Based Operators to Support Advanced Texture Analysis of MRI Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/213901.
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Texture analysis is the process of highlighting key characteristics thus providing an exhaustive and unambiguous mathematical description of any object represented in a digital image. Each characteristic is connected to a specific property of the object. In some cases the mentioned properties represent aspects visually perceptible which can be detected by developing operators based on Computer Vision techniques. In other cases these properties are not visually perceptible and their computation is obtained by developing operators based on Image Understanding approaches. Pixels composing high quality medical images can be considered the result of a stochastic process since they represent morphological or physiological processes. Empirical observations have shown that these images have visually perceptible and hidden significant aspects. For these reasons, the operators can be developed by means of a statistical approach. In this paper we present a set of customized first and second order statistics based operators to perform advanced texture analysis of Magnetic Resonance Imaging (MRI) images. In particular, we specify the main rules defining the role of an operator and its relationship with other operators. Extensive experiments carried out on a wide dataset of MRI images of different body regions demonstrating usefulness and accuracy of the proposed approach are also reported.
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Salomo, Salomo, Nova Lestari, and Muhammad Hamdi. "ANALISA PENGARUH GAYA ELEKTROSTATIK PADA SPEKTRUM PENCITRAAN RESONANSI MAGNETIK (MRI) DALAM JARINGAN BIOLOGI." Komunikasi Fisika Indonesia 16, no. 1 (April 30, 2019): 8. http://dx.doi.org/10.31258/jkfi.16.1.8-11.
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A study of magnetic core resonance imaging modeling of biological tissue has been carried out in analyzing the effect of electrostatic forces with computational approach. This analysis aims to look at the effect of electric and magnetic force on the spectrum of breast cancer tissue. Physical parameters were determined using the modeled wave equation with the application of mathematical wolfram software 9. Computational or modeling results obtained 6 variations of the MRI spectrum showing the peak magnitude of the electric and magnetic spectrum changes by varying the resolution and distance. This is evidenced from the maximum resolution range ie the peak of the electric field spectrum at amplitude 25 a.u is at a concentration of 5 ppm. Resolution of spectrum peak medium is at concentration of 3-4 ppm whereas minimum resolution has 4 peak spectrum that is at concentration 1-2 ppm, 2-3ppm, 3-4ppm and 4ppm. the result of MRI spektrum for distance variation resulted in spectrum change, further reduced the distance then the mri spectrum in magnetic and electric field approaching spin 1.
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El abbadi, Nidhal Khdhair, and Zahraa Faisal Shoman. "Detection and recognition of brain tumor based on DWT, PCA and ANN." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 56. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp56-63.
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Brain tumor is one of more dangerous diesis that affected more than 100 persons every day. The challenge is how to detect and recognise benign and malignant tumor without surgery. In this paper, initially, brain images are filtered to remove unwanted particles, then a new method for automatic segmentation of lesion area is carried out based on mean and standard deviation. Combining both solidity property and morphological operation used to detect only the tumor from segmented image. Mathematical morphology such as close used to join narrow breaks regions in an object, fill the small holes and remove small objects. Features extracted from image by using wavelet transform, followed by applying principle component analysis (PCA) to reduce the dimensions of features. Classification of tumor based on neural network, where the inputs to the network are thirteen statistical features and textural features. The algorithm is trained with 20 of brain MRI images and tested with 45 brain MRI images. Accuracy for this method was encourage and reach near 100% in identifying normal and abnormal tissues from MRI images.
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Yao, Jingting, Muhammad Ali Raza Anjum, Anshuman Swain, and David A. Reiter. "Analytical and Numerical Connections between Fractional Fickian and Intravoxel Incoherent Motion Models of Diffusion MRI." Mathematics 9, no. 16 (August 17, 2021): 1963. http://dx.doi.org/10.3390/math9161963.
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Impaired tissue perfusion underlies many chronic disease states and aging. Diffusion-weighted imaging (DWI) is a noninvasive MRI technique that has been widely used to characterize tissue perfusion. Parametric models based on DWI measurements can characterize microvascular perfusion modulated by functional and microstructural alterations in the skeletal muscle. The intravoxel incoherent motion (IVIM) model uses a biexponential form to quantify the incoherent motion of water molecules in the microvasculature at low b-values of DWI measurements. The fractional Fickian diffusion (FFD) model is a parsimonious representation of anomalous superdiffusion that uses the stretched exponential form and can be used to quantify the microvascular volume of skeletal muscle. Both models are established measures of perfusion based on DWI, and the prognostic value of model parameters for identifying pathophysiological processes has been studied. Although the mathematical properties of individual models have been previously reported, quantitative connections between IVIM and FFD models have not been examined. This work provides a mathematical framework for obtaining a direct, one-way transformation of the parameters of the stretched exponential model to those of the biexponential model. Numerical simulations are implemented, and the results corroborate analytical results. Additionally, analysis of in vivo DWI measurements in skeletal muscle using both biexponential and stretched exponential models is shown and compared with analytical and numerical models. These results demonstrate the difficulty of model selection based on goodness of fit to experimental data. This analysis provides a framework for better interpreting and harmonizing perfusion parameters from experimental results using these two different models.
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M Devendrappa, Deepak, Karthik Pilani, and Deepak N Ananth. "Sparse and Incomplete Signal Dictionaries for Reconstruction of MR Images." International journal of electrical and computer engineering systems 13, no. 3 (April 19, 2022): 165–74. http://dx.doi.org/10.32985/ijeces.13.3.1.
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Compressed Sensing(CS) is a mathematical approach for data acquisition in which the signals are compressible and sparse w.r.t. to an orthonormal basis. These sparse signals are reconstructed from very less measurements. CS technique Is widely used in Magnetic Resonance Imaging (MRI) where the doctors suggest the patients to undergo MRI scans for diagnosing their body parts. During the prolonged MRI Scan, the exact slice of the MRI cannot be achieved due to the difficulties faced by the patient or irregular changes in the body position of the patient. The idea is to reduce the exposure time of the patient’s body against the MRI scan by considering only fewer samples. Is it possible to Reconstruct the signal by making use of a fewer number of samples that are less than the Nyquist rate? Yes, it is possible to reconstruct the signal by making use of the Compressed Sensing or sampling Technique. Compressed sensing is a new framework for signal acquisition and representation in a compressible manner less below the Nyquist sampling rate. In this article, Sampling and reconstruction are dealt here thoroughly as part of the research activity. Compressive Sensing Matching pursuit (CoSaMP) is a novel technique for optimization. It is an iterative approximation method for sparse and incomplete signal recovery. CoSaMP method along with Different transform techniques is used for reconstruction. The FFT_CoSaMP, DCT_CoSaMP and DWT_CoSaMP are proposed methods for MR Image Reconstruction, where DWT-based CoSaMP along with different wavelet families give the best results when compared to other CS-based techniques w.r.t. PSNR, SSIM and RMSE analysis.
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Li, Zhigang, Aimei Dong, and Jing Zhou. "Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis." Computational and Mathematical Methods in Medicine 2020 (March 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/5294840.
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As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer’s disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer’s disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method.
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Longatti, Pierluigi, Alessandro Fiorindi, Paolo Peruzzo, Luca Basaldella, and Francesca Maria Susin. "Form follows function: estimation of CSF flow in the third ventricle–aqueduct–fourth ventricle complex modeled as a diffuser/nozzle pump." Journal of Neurosurgery 133, no. 3 (September 2020): 894–901. http://dx.doi.org/10.3171/2019.5.jns19276.
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OBJECTIVEIn the last 20 years, researchers have debated cerebrospinal fluid (CSF) dynamics theories, commonly based on the classic bulk flow perspective. New hypotheses do not consider a possible hydraulic impact of the ventricular morphology. The present study investigates, by means of a mathematical model, the eventual role played by the geometric shape of the “third ventricle–aqueduct–fourth ventricle” complex in CSF circulation under the assumption that the complex behaves like a diffuser/nozzle (DN) pump.METHODSDN pumps are quite recent devices introduced as valveless micropumps in various industrial applications given their property of driving net flow when subjected to rhythmic pulsations. A novel peculiar DN pump configuration was adopted in this study to mimic the ventricular complex, with two reservoirs (the ventricles) and one tube provided with a conical reach (the aqueduct–proximal fourth ventricle). The flow was modeled according to the classic equations of laminar flow, and the external rhythmic pulsations forcing the system were reproduced as a pulsatile pressure gradient between the chambers. Several physiological scenarios were implemented with the integration of data acquired by MRI in 10 patients with no known pathology of CSF dynamics, and a quantitative analysis of the effect of geometric and hydraulic parameters (diverging angle, sizes, frequency of pulsations) on the CSF net flow was performed.RESULTSThe results showed a craniocaudal net flow in all the given values, consistent with the findings of cine MRI studies. Moreover, the net flow estimated for the analyzed cohort of patients ranged from 0.221 to 0.505 ml/min, remarkably close to the values found on phase contrast cine MRI in healthy subjects. Sensitivity analysis underlines the pivotal role of the DN configuration, as well as of the frequency of forcing pressure, which promotes a relevant net flow considering both the heart and respiration rate.CONCLUSIONSThis work suggests that the geometry of the third ventricle–aqueduct–fourth ventricle complex, which resembles a diverter, appears to be functional in the generation of a net craniocaudal flow and potentially has an impact on CSF dynamics. These conclusions can be drawn by observing the analogies between the shape of the ventricles and the geometry of DN pumps and by recognizing the basis of the mathematical model of the simplified third ventricle–aqueduct–fourth ventricle complex proposed.
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Zakariaee, Seyed Salman, Mohammad Ali Oghabian, Kavous Firouznia, Guive Sharifi, Farshid Arbabi, and Farhad Samiei. "Assessment of the Agreement between Cerebral Hemodynamic Indices Quantified Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced Perfusion Magnetic Resonance Imagings." Journal of Clinical Imaging Science 8 (January 22, 2018): 2. http://dx.doi.org/10.4103/jcis.jcis_74_17.
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Background: Brain tumor is one of the most common tumors. A successful treatment might be achieved with an early identification. Pathological investigation as the gold standard method for tumor identification has some limitations. Noninvasive assessment of tumor specifications may be possible using perfusion-weighted magnetic resonance imaging (MRI). Cerebral blood volume (CBV) and cerebral blood flow (CBF) could be calculated based on dynamic contrast-enhanced MRI (DCE-MRI) in addition to dynamic susceptibility contrast MRI (DSC-MRI) modality. Each category of the cerebral hemodynamic and permeability indices revealed the specific tumor characteristics and their collection could help for better identification of the tumor. Some mathematical methods were developed to determine both cerebral hemodynamic and permeability indices based on a single-dose DCE perfusion MRI. There are only a few studies available on the comparison of DSC- and DCE-derived cerebral hemodynamic indices such as CBF and CBV. Aim: The objective of the study was to validate first-pass perfusion parameters derived from T1-based DCE method in comparison to the routine T2*-based DSC protocol. Materials and Methods: Twenty-nine patients with brain tumor underwent DCE- and DSC-MRIs to evaluate the agreement between DSC- and DCE-derived cerebral hemodynamic parameters. Agreement between DSC- and DCE-derived cerebral hemodynamic indices was determined using the statistical method described by Bland and Altman. The reliability between DSC- and DCE-derived cerebral hemodynamic indices was measured using the intraclass correlation analysis. Results: The achieved magnitudes for DCE-derived CBV (gray matter [GM]: 5.01 ± 1.40 mL/100 g vs. white matter [WM]: 1.84 ± 0.74 mL/100 g) and DCE-derived CBF (GM: 60.53 ± 12.70 mL/100 g/min vs. WM: 32.00 ± 6.00 mL/100 g/min) were in good agreement with other studies. The intraclass correlation coefficients showed that the cerebral hemodynamic indices could accurately be estimated based on the DCE-MRI using a single-compartment model (>0.87), and DCE-derived cerebral hemodynamic indices are significantly similar to the magnitudes achieved based on the DSC-MRI (P < 0.001). Furthermore, an acceptable agreement was observed between DSC- and DCE-derived cerebral hemodynamic indices. Conclusion: Based on the measurement of the cerebral hemodynamic and blood–brain barrier permeability using DCE-MRI, a more comprehensive collection of the physiological parameters cloud be achieved for tumor evaluations.
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Young, Christina B., Sarah S. Wu, and Vinod Menon. "The Neurodevelopmental Basis of Math Anxiety." Psychological Science 23, no. 5 (March 20, 2012): 492–501. http://dx.doi.org/10.1177/0956797611429134.
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Math anxiety is a negative emotional reaction to situations involving mathematical problem solving. Math anxiety has a detrimental impact on an individual’s long-term professional success, but its neurodevelopmental origins are unknown. In a functional MRI study on 7- to 9-year-old children, we showed that math anxiety was associated with hyperactivity in right amygdala regions that are important for processing negative emotions. In addition, we found that math anxiety was associated with reduced activity in posterior parietal and dorsolateral prefrontal cortex regions involved in mathematical reasoning. Multivariate classification analysis revealed distinct multivoxel activity patterns, which were independent of overall activation levels in the right amygdala. Furthermore, effective connectivity between the amygdala and ventromedial prefrontal cortex regions that regulate negative emotions was elevated in children with math anxiety. These effects were specific to math anxiety and unrelated to general anxiety, intelligence, working memory, or reading ability. Our study identified the neural correlates of math anxiety for the first time, and our findings have significant implications for its early identification and treatment.
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De Celis Alonso, Benito, Javier M. Hernández López, José G. Suárez García, and Eduardo Moreno Barbosa. "A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD." International Journal of Basic and Applied Sciences 6, no. 3 (August 24, 2017): 57. http://dx.doi.org/10.14419/ijbas.v6i3.8034.
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Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychological disorders in pediatric patients. The actual golden standard of ADHD diagnosis is based on conclusions derived from clinical questionnaires. Nowadays, there is no quantitative measurement performed with any imaging system (MRI, PET, EEG, etc.) that can be considered as a golden standard for this diagnosis. This issue, is highlighted by the existence of international competitions focused on the production of a technological (quantitative) solution capable of complementing ADHD diagnosis (ADHD-200 Global Competition). Wavelet analysis, on the other hand, is a flexible mathematical tool that can be used for information and data processing. Its advantage over other types of mathematical transformations is its ability to decompose a signal into two parameters (frequency and time). Based on the prevalence of ADHD and the extra functionality of wavelet tools, this review will try to answer the following question: How have wavelet analyses been used to complement diagnosis and characterization of ADHD? It will be shown that applications were not casual and limited to time-frequency decomposition, noise removal or down sampling of signals, but were pivotal for construction of learning networks, specific parameterization of signals or calculations of connectivity between brain nodes.
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Uzun Ozsahin, Dilber, Efe Precious Onakpojeruo, Berna Uzun, Mubarak Taiwo Mustapha, and Ilker Ozsahin. "Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis." Diagnostics 13, no. 4 (February 8, 2023): 618. http://dx.doi.org/10.3390/diagnostics13040618.
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The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.
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Zafar, Raheel, Muhammad Javvad ur Rehman, Sheraz Alam, Muhammad Arslan Khan, Asad Hussain, Rana Fayyaz Ahmad, Faruque Reza, and Rifat Jahan. "A Cumulants-Based Human Brain Decoding." Computational Intelligence and Neuroscience 2022 (July 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/6474515.
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Human cognition is influenced by the way the nervous system processes information and is linked to this mechanical explanation of the human body’s cognitive function. Accuracy is the key emphasis in neuroscience which may be enhanced by utilising new hardware, mathematical, statistical, and computational methodologies. Feature extraction and feature selection also play a crucial function in gaining improved accuracy since the proper characteristics can identify brain states efficiently. However, both feature extraction and selection procedures are dependent on mathematical and statistical techniques which implies that mathematical and statistical techniques have a direct or indirect influence on prediction accuracy. The forthcoming challenges of the brain-computer interface necessitate a thorough critical understanding of the complicated structure and uncertain behavior of the brain. It is impossible to upgrade hardware periodically, and thus, an option is necessary to collect maximum information from the brain against varied actions. The mathematical and statistical combination could be the ideal answer for neuroscientists which can be utilised for feature extraction, feature selection, and classification. That is why in this research a statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy. A score fusion function is changed utilising an enhanced cumulants-driven likelihood ratio test employing multivariate pattern analysis. Functional MRI data were acquired from 12 patients versus a visual test that comprises of pictures from five distinct categories. After cleaning the data, feature extraction and selection were done using mathematical approaches, and lastly, the best match of the projected class was established using the likelihood ratio test. To validate the suggested approach, it is compared with the current methods reported in recent research.
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Guzmán, Carlos, Rubén Soria-Martínez, and Julián Urresta. "New Strategies for Potential Contrast Agents’ Synthons Highly Active to MRI Based on Gd3+, Eu3+, and Tb3+." Applied Sciences 12, no. 19 (October 4, 2022): 9969. http://dx.doi.org/10.3390/app12199969.
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The library of new smart contrast agents based on Gd3+, Eu3+, and Tb3+ used as biomarkers is in continuous development due to its applications in diagnostic imaging. The search for safer and more efficient contrast agents has focused on the design of compounds that exhibit high relaxivity. Herein, we present alternative synthetic strategies for the development of theoretically high-relaxivity synthons based on lanthanides using the Solomon–Bloembergen–Morgan equations through click chemistry and direct addition. Special attention has been devoted to the analysis of the different aspects interfering with the successful acquisition of these complexes and their troubleshooting during their synthesis. Our preliminary results showed that not only the mathematical background needs to be considered, but also the synthetic strategy and the use of procedures free of metallic ions favor the total synthesis of these challenging complexes.
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Joshi, Sarang C., Michael I. Miller, and Ulf Grenander. "On the Geometry and Shape of Brain Sub-Manifolds." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 08 (December 1997): 1317–43. http://dx.doi.org/10.1142/s0218001497000615.
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This paper develops mathematical representations for neuro-anatomically significant substructures of the brain and their variability in a population. The focus of the paper is on the neuro-anatomical variation of the geometry and the "shape" of two-dimensional surfaces in the brain. As examples, we focus on the cortical and hippocampal surfaces in an ensemble of Macaque monkeys and human MRI brains. The "shapes" of the substructures are quantified via the construction of templates; the variations are represented by defining probabilistic deformations of the template. Methods for empirically estimating probability measures on these deformations are developed by representing the deformations as Gaussian random vector fields on the embedded sub-manifolds. The Gaussian random vector fields are constructed as quadratic mean limits using complete orthonormal bases on the sub-manifolds. The complete orthonormal bases are generated using modes of vibrations of the geometries of the brain sub-manifolds. The covariances are empirically estimated from an ensemble of brain data. Principal component analysis is presented for characterizing the "eigen-shape" of the hippocampus in an ensemble of MRI-MPRAGE whole brain images. Clustering based on eigen-shape is presented for two sub-populations of normal and schizophrenic.
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Aarthi, E., S. Jana, W. Gracy Theresa, M. Krishnamurthy, A. S. Prakaash, C. Senthilkumar, and S. Gopalakrishnan. "Detection and Classification of MRI Brain Tumors using S3-DRLSTM Based Deep Learning Model." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 597–603. http://dx.doi.org/10.37391/ijeer.100331.
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Developing an automated brain tumor diagnosis system is a highly challenging task in current days, due to the complex structure of nervous system. The Magnetic Resonance Imaging (MRIs) are extensively used by the medical experts for earlier disease identification and diagnosis. In the conventional works, the different types of medical image processing techniques are developed for designing an automated tumor detection system. Still, it remains with the problems of reduced learning rate, complexity in mathematical operations, and high time consumption for training. Therefore, the proposed work intends to implement a novel segmentation-based classification system for developing an automated brain tumor detection system. In this framework, a Convoluted Gaussian Filtering (CGF) technique is used for normalizing the medical images by eliminating the noise artifacts. Then, the Sparse Space Segmentation (S3) algorithm is implemented for segmenting the pre-processed image into the non-overlapping regions. Moreover, the multi-feature extraction model is used for extracting the contrast, correlation, mean, and entropy features from the segmented portions. The Deep Recurrent Long-Short Term Memory (DRLSTM) technique is utilized for predicting the classified label as normal of disease affected. During results analysis, the performance of the proposed system is tested and compared by using various evaluation measures.
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Corwin, David, Russell Rockne, Maciej M. Mrugala, Jason K. Rockhill, and Kristin Swanson. "Training and validation cohort analysis for predicting radiation therapy response in human glioblastoma." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e13018-e13018. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e13018.
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e13018 Background: Glioblastomas comprise 50% of primary brain tumors and are the most fatal, with a median survival time of 14 months despite aggressive treatment. Due to disease heterogeneity and the presence of subclinical disease, there is a large variability in treatment response among patients, making it difficult to assess treatment efficacy and confounding clinical decision making. Methods: A patient-specific, mathematical model of glioblastoma has been shown to predict untreated growth as well as the effect of radiation therapy. We expand on the technique presented in Rockne 2010 to determine patient-specific radiosensitivity and post therapy radial growth using only pretreatment data. From a potential cohort of 44 patients we randomly chose a training set of 30 to compute the relationship for the radiosensitivity parameter (alpha) versus the net proliferation rate. We then used that relationship to compute an individualized alpha for the remaining 14 test patients based only on pretreatment information. For each of the test patients we compared the observed T1Gd visible radius at the first post radiotherapy timepoint with the simulated prediction. Results: Half of the patients demonstrated average absolute differences between the observed and simulated post radiotherapy T1Gd radii of less than 5 mm, within the measurement error associated with contouring the tumor on MRI, with a maximum difference of 2.5 cm. The largest errors were observed in patients with significant resection. Conclusions: This patient-specific, mathematical model of glioma growth and response to radiotherapy can potentially predict radioresponse in vivo, prior to the commencement of therapy. As a clinical tool, this can have prognostic applications and separate responders from non-responders before deciding to treat with radiotherapy. The larger error for patients with more extensive resections is not unexpected given the difficulties in contouring and assessing tumor burden post surgery. This can be improved by including other imaging modalities such as pre-gadolinium T1 and improving the quantification of extent of resection.
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Aoki, Kosuke, Takashi Yamamoto, Hiromichi Suzuki, Sachi Maeda, Melissa Ranjit, Kazuya Motomura, Hideo Nakamura, et al. "TMOD-33. AN INTEGRATED APPROACH COMBINING MATHEMATICAL AND GENOMIC METHODS TO REVEAL THE OPTIMAL TIMING OF THERAPEUTIC INTERVENTION IN WHO GRADE II DIFFUSE GLIOMA." Neuro-Oncology 21, Supplement_6 (November 2019): vi270. http://dx.doi.org/10.1093/neuonc/noz175.1132.
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Abstract BACKGROUND In WHO grade II diffuse gliomas (low-grade gliomas, hereafter called LGGs), chemotherapy and radiotherapy contribute to prolonged survival but could induce somatic mutations. The optimal timing of treatment in LGGs remain poorly understood. To delineate this, we designed a mathematical model for tumor growth and investigate the association among the treatment, malignant transformation (MT), and the accumulation of somatic mutations revealed by whole exome sequencing (WES) in LGGs. METHODS Totally, 290 patients with LGGs between 1990 and 2018 were analyzed. We assessed the statuses of IDH mutation and 1p19q co-deletion in all tumors. Among all, 114 patients (39%) underwent MT during follow-up periods (mean: 82.6 months). Tumor volume was evaluated with FLAIR and/or T2-weighted MRI. MT was evaluated with contrast-enhanced MRI and/or pathological diagnosis. To investigate the number of somatic mutations in a cohort of LGGs and their patient matched recurrence, WES was performed on 88 serial samples collected at least two time-points from 39 patients. RESULTS Oligodendroglioma, IDH-mutant and 1p/19q-codeleted (OD) showed longer transformation-free survival compared to other subtypes. An exponential model was chosen to estimate growth rate in LGGs, since the exponential model provided a better fit to our data as compared to a linear model. The growth rate significantly decreased in the middle of chemotherapy and after radiotherapy. By contrast, these treatments increased the number of somatic mutations identified by WES and the rate of MT in each subtype. The increasing number of mutations in recurrent tumors showed strong correlation with the rise in MT rate. Based on the growth rate and the risk of MT, optimal timing of treatments could be calculated for each genetic subtype. CONCLUSIONS The mathematical model and WES analysis delineates the optimal timing of treatments in each subtype, which will help to decide the treatment for LGGs.
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Lim, K. S., P. Chan, R. Dinniwell, A. Fyles, M. Haider, Y. Cho, D. Jaffray, et al. "Cervix cancer regression measured using weekly MR imaging during fractionated radiotherapy: Radiobiologic modeling and correlation with tumor hypoxia." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 5547. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.5547.
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5547 Background: To utilize cervix cancer volumetry, as measured with MR imaging during definitive chemoirradiation (RT-CT), to derive radiobiological parameters using a mathematical model of tumor regression, and compare them to pre-treatment measurements of tumor hypoxia. Methods: Twenty-eight patients receiving RT-CT for cervix cancer underwent weekly magnetic resonance imaging (MRI) scans. Tumor volume was assessed on each of these scans and the rate of regression plotted. A mathematical model of tumor regression was formulated to simulate the relationship between three independent radiobiological parameters, 1) surviving fraction of cells after 2 Gy, SF2, 2) the cell clearance constant Tc, and 3) the cellular proliferation constant Tp. Non-linear regression analysis was applied to fit the MR-derived tumor volumes to the mathematical model and to derive SF2 and Tc values for each patient. These were compared to pre-treatment hypoxia measurements. Results: Initial tumor volume ranged between 8 and 209 cm3. Relative reduction in volume during treatment was 0.02 to 0.79. Simulations using representative values of the independent biologic variables derived from the literature showed SF2 and Tc to strongly influence the shape of the volume response curves. Non-linear regression analysis yielded a median SF2 of 0.71 and median Tc of 10 days. Radioresistant tumors (SF2 >0.71) were significantly more hypoxic at diagnosis (p=0.02). Conclusion: Based on serial MR imaging during treatment, a marked variation in cervix tumor regression is seen from patient to patient. Through our radiobiological model, tumors can be classified as radioresistant or radiosensitive, which correlates with hypoxia [Table: see text] No significant financial relationships to disclose.
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Sandor, Zoltan, Gabor Kristof Rathonyi, and Elek Dinya. "Assessment of Lumbar Lordosis Distribution with a Novel Mathematical Approach and Its Adaptation for Lumbar Intervertebral Disc Degeneration." Computational and Mathematical Methods in Medicine 2020 (April 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/7312125.
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Introduction. Low back pain and disc degeneration could be linked to global spinal geometry. Our study aimed to develop a reliable new mathematical method to assess the local distribution of total lumbar lordosis with a single numeric parameter and compare it with lumbar intervertebral disc degeneration using routine MRI scans. Methods. An online, open access, easy-to-use platform for measurements was developed based on a novel mathematical approach using MRIs of 60 patients. Our Spinalyze Software can be used online with uploaded MRIs. Several new parameters were introduced and assessed to describe variation in segmental lordosis distribution with a single numerical value. The Pfirrmann grading system was used for the classification of lumbar intervertebral disc degeneration. Relationships were investigated between the grade categories of L1-S1 lumbar discs and the MRI morphological parameters with correlation analysis. Results. Results confirm that the determination of measurement points and calculated parameters are reliable (ICCs and Pearson r values > 0.90), and these parameters were independent of gender. The digression percentage (K%), one of our new parameters, did not show a statistical relationship with the Cobb-angle. According to our results, the maximum deflection breaking-point of lumbar lordosis and its location can be different with the same Cobb-angle and the distribution of global lordosis is uneven because the shape of the lumbar lordosis is shifted downward and centered around the L4 lumbar vertebra. The interobserver reliability of the Pfirrmann grades reading was in the excellent agreement category (88.33% agreement percentage, 0.84 kappa), and digression percentage (K%) showed a significant negative correlation with all L1-S1 disc grades with increasing r correlation values. This means that the smaller the value of digression percentage (K%), the more the number of worn discs in the lower lumbar sections. Conclusions. Spinalyze Software based on a novel mathematical approach provides a free, easy-to-use, reliable, and online measurement tool using standard MRIs to approximate the curvature of lumbar lordosis. The new reliable K% (digression percentage) is one single quantitative parameter to assess the local distribution of total lumbar lordosis. The results indicate that digression percentage (K%) may possibly be associated with the development of lumbar intervertebral disc degeneration. Further evaluation is needed to assess its behavior and advantage.
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Gavrilov, D. A., E. I. Zakirov, E. V. Gameeva, V. Yu Semenov, and O. Yu Aleksandrova. "Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network." Research'n Practical Medicine Journal 5, no. 3 (September 9, 2018): 110–16. http://dx.doi.org/10.17709/2409-2231-2018-5-3-11.
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In the last 10 years there has been a revolu on in the fi eld of computer image analysis and pa ern recogni on. Modern algorithms of computer vision equaled and even in some problems surpassed human capabili es. This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. Among the most promising applica ons: automated recogni on and classifi ca on of skin diseases, detec on of pathologies on X-ray, CT, MRI, ultrasound imaging. In the proposed project, we will focusour a en on on the diagnosis of human skin diseases.At the moment, melanoma is one of the most dangerous types of malignant tumors of the skin with a lot of deaths due to rapid metastasis, which is difficult to treat. The development of computer vision technology has allowed the development of technical vision systems that allow detec on and classifi ca on of skin diseases with a quality that is comparable and in some cases exceeds the values a ained by man.In this paper, the authors propose an algorithm for the primary diagnosis of skin melanoma based on deep neural networks, achieving an accuracy of 91% for melanoma in dermatoscopic images. At the moment, the algorithm is implemented programma cally and is used in the test version of the online system for detec ng and classifying skin diseases, available at skincheckup.online.Thanks to this development, the prospect of a signifi cantincrease in the propor on of people subjected to preven ve examina on for the presence of skin diseases opens up. Along with this, an addi onal source of informa on for specialized professionals can also play a role in seng the right diagnosis.
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TRACQUI, PHILIPPE, and MAHIDINE MENDJELI. "MODELLING THREE-DIMENSIONAL GROWTH OF BRAIN TUMOURS FROM TIME SERIES OF SCANS." Mathematical Models and Methods in Applied Sciences 09, no. 04 (June 1999): 581–98. http://dx.doi.org/10.1142/s0218202599000300.
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The development of brain tumours, after diagnosis, is routinely recorded by different medical imaging techniques like computerised tomography (CT) or magnetic resonance imaging (MRI). However, it is only through the formulation of mathematical models that an analysis of the spatio-temporal tumour growth revealed on each patient serial scans can lead to a quantification of parameters characterising the proliferative and expensive dynamic of the brain tumour. This paper reviews some of the results and limitations encountered in modelling the different stages of a brain tumour growth, namely before and after diagnosis and therapy. It extends an original two-dimensional approach by considering three-dimensional growth of brain tumours submitted to the spatial constraints exerted by the skull and ventricles boundaries. Considering the dynamic of both the pre- and post-diagnosis stages, the tumour growth patterns obtained with various combinations of nonlinear growth rates and cellular diffusion laws are considered and compared to real MRI scans taken in a patient with a glioblastoma and having undergone radiotherapy. From these simulations, we characterise the effects of different therapies on survival durations, with special attention to the effect of cell diffusion inside the resected brain region when surgical resection of the tumour is carried out.
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DiCarlo, Julie C., Angela M. Jarrett, Anum S. Kazerouni, John Virostko, Anna G. Sorace, Kalina P. Slavkova, Debra Patt, Boone W. Goodgame, Sarah Avery, and Thomas E. Yankeelov. "Abstract P3-02-09: Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams." Cancer Research 82, no. 4_Supplement (February 15, 2022): P3–02–09—P3–02–09. http://dx.doi.org/10.1158/1538-7445.sabcs21-p3-02-09.
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Abstract Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-02-09.
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Alshammari, Qurain, Mohammed Salih, Moawia Gameraddin, Bushra Abdelmalik, Sultan Alshoabi, Elfadil Elnour, and Elgeili Yousif. "Assessment of Brain Lesions in Type 2 Diabetes Mellitus and Hypertension using Magnetic Resonance imaging." International Journal of Biomedicine 10, no. 4 (December 10, 2020): 382–86. http://dx.doi.org/10.21103/article10(4)_oa10.
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Background: Type 2 diabetes mellitus (T2DM) and hypertension (HTN) are risk factors for the spectrum of brain lesions. In this paper, we studied the impact of T2DM and HTN on the incidence of several brain lesions diagnosed with magnetic resonance imaging (MRI). Methods and Results: This retrospective, single-center study was conducted at Royal Care International Hospital (Khartoum, Sudan) from January 2016 to December 2016 and included 80 patients (40 male and 40 female, aged between 20 years and 90 years) with suspected brain disorders. MRI brain examinations were conducted on a 1.5 Tesla MRI system (Toshiba Medical Systems, Tokyo, Japan). The following sequences were analyzed: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI). Brain lesions were characterized by magnetic imaging spectroscopy and histopathological analysis. Binary logistic regression analysis was used to establish a mathematical model of the relationship between T2DM/HTN and the prevalence of brain lesions. Among 80 patients, HTN, T2D, and the combination of T2D and HTN were identified in 18(22.5%), 9(11.2%), and 11(13.8%) patients, respectively. Brain lesions were found in 48(60%) patients and were most prevalent in the age group of 66-80 years. The brain lesions included ischemic brain infarction (IBI) (22.5%), brain tumors (11.2%), cerebral hemorrhages (6.2%), brain atrophy (BA) (1.2 %), IBI with BA (16.2%), and brain metastases (2.5%). Regression analysis showed that HTN and T2DM were associated with significantly higher ORs for brain lesions ([OR=2.459, 95% CI: 1.673–3.614, P<0.001] and [OR=1.507, 95% CI: 1.067–2.128, P= 0.042], and [OR=1.078, 95% CI:1.033–1.124, P=0.001], respectively). HTN was associated with significantly higher OR for ischemic brain infarction (OR=7.404, 95% CI: 2.600–21.081, P<0.001). Conclusion: The study showed a significant interaction between HTN and T2DM on the prevalence of brain lesions, especially ischemic brain infarction and brain atrophy.
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Müller, Manfred J., Wiebke Braun, Maryam Pourhassan, Corinna Geisler, and Anja Bosy-Westphal. "Application of standards and models in body composition analysis." Proceedings of the Nutrition Society 75, no. 2 (November 6, 2015): 181–87. http://dx.doi.org/10.1017/s0029665115004206.
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The aim of this review is to extend present concepts of body composition and to integrate it into physiology. In vivo body composition analysis (BCA) has a sound theoretical and methodological basis. Present methods used for BCA are reliable and valid. Individual data on body components, organs and tissues are included into different models, e.g. a 2-, 3-, 4- or multi-component model. Today the so-called 4-compartment model as well as whole body MRI (or computed tomography) scans are considered as gold standards of BCA. In practice the use of the appropriate method depends on the question of interest and the accuracy needed to address it. Body composition data are descriptive and used for normative analyses (e.g. generating normal values, centiles and cut offs). Advanced models of BCA go beyond description and normative approaches. The concept of functional body composition (FBC) takes into account the relationships between individual body components, organs and tissues and related metabolic and physical functions. FBC can be further extended to the model of healthy body composition (HBC) based on horizontal (i.e. structural) and vertical (e.g. metabolism and its neuroendocrine control) relationships between individual components as well as between component and body functions using mathematical modelling with a hierarchical multi-level multi-scale approach at the software level. HBC integrates into whole body systems of cardiovascular, respiratory, hepatic and renal functions. To conclude BCA is a prerequisite for detailed phenotyping of individuals providing a sound basis for in depth biomedical research and clinical decision making.
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Dykan, Iryna, B. Tarasyuk, and I. Andrushchenko. "Scientific research of the Institute of Nuclear Medicine and Diagnostic Radiology of the National Academy of Medical Sciences of Ukraine in 2020: hepatology, oncology and intellectual data processing." Radiation Diagnostics, Radiation Therapy 12, no. 1 (2021): 7–12. http://dx.doi.org/10.37336/2707-0700-2021-1-1.
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The main results of the scientific activity of the Institute in 2020 are presented: the study of the structural features of diagnostic images based on their heterogeneity for the purpose of differential diagnosis of malignant neoplasms; development of radiation diagnostic criteria for differential diagnosis of congenital and acquired liver diseases in children; development of urgent detailed ultrasound examinations for traumatic injuries of the human body, complex radiation examination of the wound channel in case of a gunshot injury; development of methodological foundations for the use of multiparametric high-field MRI of the brain to assess the state of the leading tracts and the functional capacity of the cerebral cortex. Methods for textural analysis of computed tomographic images, echograms of normal and tumor tissues based on the use of coefficients of one-dimensional heterogeneity, anisotropy coefficient, and verticality coefficient have been developed. For the first time in Ukraine, a mathematical system that provides in 93-98% of cases of recognition of fibrous (precision) liver changes based on ultrasound images (utility model patent No. 139916) has been developed; significant differences between the values of mathematical criteria for normal and damaged liver based on computer processing of echographic images allow solving the problem of recognizing diffuse liver diseases.
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VanderWeele, David James, Stephanie McCann, Xiaobing Fan, Tatjana Antic, Yulei Jiang, and Aytekin Oto. "Radiogenomics of prostate cancer: Association between qunatitative multiparametric MRI features and PTEN." Journal of Clinical Oncology 33, no. 7_suppl (March 1, 2015): 126. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.126.
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126 Background: Better methods are needed to assess prior to prostatectomy the risk of aggressive prostate cancer. Radiogenomics is a promising new paradigm that aims to gain molecular and genomic insights from clinical images. Loss of PTEN expression correlates with clinically aggressive disease and is associated with a 7-fold increase in the risk of prostate cancer death. Methods: From 38 patients who had undergone multi-parametric prostate MRI prior to prostatectomy, a pathologist and a radiologist simultaneously identified 45 peripheral-zone cancer regions of interest (ROIs). Histologic sections of the cancer foci underwent immunohistochemical analysis and were scored according to percent of tumor cells expressing PTEN as: negative (0-20%), mixed (20-80%), or positive (80-100%). From the MRI ROIs, the average and 10th percentile ADC values, T2-weighted signal-intensity histogram skewness, and quantitative perfusion parameters were calculated. Both dynamic perfusion two-compartment model and an empirical mathematical model (EMM) were used to fit the average contrast concentration curves within the ROIs as a function of time. Associations between the quantitative image features and PTEN expression were analyzed with Pearson's correlation coefficient (r). Results: The PTEN scores were: positive (n = 21, 47%), mixed (n = 12, 27%), and negative (n = 12, 27%). Two perfusion imaging contrast uptake parameters obtained from EMM correlated with PTEN scores (r = 0.25, p < 0.1 and r = 0.43, p < 0.01), and T2-weighted signal-intensity skewness also showed some correlation tendency (r = −0.25, p < 0.1). No correlation was seen between mean ADC and 10th percentile ADC values and PTEN score. Conclusions: This preliminary study of radiogenomics of prostate cancer suggests that fast contrast uptake of cancer on DCE-MR imaging and a T2-weighted imaging feature are potentially associated with prostate cancer PTEN expression, which warrants further studies and validation.
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Rucco, Matteo, Giovanna Viticchi, and Lorenzo Falsetti. "Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning." Mathematics 8, no. 5 (May 11, 2020): 770. http://dx.doi.org/10.3390/math8050770.
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Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor, which tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors. Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI, a fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumor representation. Fast computer-aided detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. This study has three main novelties for demonstrating the role of topological features as new set of radiomics features which can be used as pillars of a personalized diagnostic systems of GBM analysis from FLAIR. For the first time topological data analysis is used for analyzing GBM from three complementary perspectives—tumor growth at cell level, temporal evolution of GBM in follow-up period and eventually GBM detection. The second novelty is represented by the definition of a new Shannon-like topological entropy, the so-called Generator Entropy. The third novelty is the combination of topological and textural features for training automatic interpretable machine learning. These novelties are demonstrated by three numerical experiments. Topological Data Analysis of a simplified 2D tumor growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumor growth—the higher the concentration of chemical nutrients the more virulent the process. Topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we developed a novel methodology based on topological and textural features and automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to 97%.
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Nicolas, Renaud, Igor Sibon, and Bassem Hiba. "Accuracies and Contrasts of Models of the Diffusion-weighted-dependent Attenuation of the Mri Signal at Intermediate B-values." Magnetic Resonance Insights 8 (January 2015): MRI.S25301. http://dx.doi.org/10.4137/mri.s25301.
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The diffusion-weighted-dependent attenuation of the MRI signal E( b) is extremely sensitive to microstructural features. The aim of this study was to determine which mathematical model of the E( b) signal most accurately describes it in the brain. The models compared were the monoexponential model, the stretched exponential model, the truncated cumulant expansion (TCE) model, the biexponential model, and the triexponential model. Acquisition was performed with nine b-values up to 2500 s/mm2 in 12 healthy volunteers. The goodness-of-fit was studied with F-tests and with the Akaike information criterion. Tissue contrasts were differentiated with a multiple comparison corrected nonparametric analysis of variance. F-test showed that the TCE model was better than the biexponential model in gray and white matter. Corrected Akaike information criterion showed that the TCE model has the best accuracy and produced the most reliable contrasts in white matter among all models studied. In conclusion, the TCE model was found to be the best model to infer the microstructural properties of brain tissue.
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Kara, Sadık, Mehmet Albayram, Şükrü Okkesim, and Mustafa Yıldırım. "Evaluation of Spontaneous Spinal Cerebrospinal Fluid Leaks Disease by Computerized Image Processing." Methods of Information in Medicine 55, no. 03 (2016): 215–22. http://dx.doi.org/10.3414/me15-01-0148.
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SummaryBackground: Spontaneous Spinal Cerebro -spinal Fluid Leaks (SSCFL) is a disease based on tears on the dura mater. Due to widespread symptoms and low frequency of the disease, diagnosis is problematic. Diagnostic lumbar puncture is commonly used for diagnosing SSCFL, though it is invasive and may cause pain, inflammation or new leakages. T2-weighted MR imaging is also used for diagnosis; however, the literature on T2-weighted MRI states that findings for diagnosis of SSCFL could be erroneous when differentiating the diseased and control. One another technique for diagnosis is CT-myelography, but this has been suggested to be less successful than T2-weighted MRI and it needs an initial lumbar puncture.Objectives: This study aimed to develop an objective, computerized numerical analysis method using noninvasive routine Magnetic Resonance Images that can be used in the evaluation and diagnosis of SSCFL disease.Methods: Brain boundaries were automatically detected using methods of mathematical morphology, and a distance transform was employed. According to normalized distances, average densities of certain sites were proportioned and a numerical criterion related to cerebrospinal fluid distribution was calculated.Results: The developed method was able to differentiate between 14 patients and 14 control subjects significantly with p = 0.0088 and d = 0.958. Also, the pre and post-treatment MRI of four patients was obtained and analyzed. The results were differentiated statistically (p = 0.0320, d = 0.853).Conclusions: An original, noninvasive and objective diagnostic test based on computerized image processing has been developed for evaluation of SSCFL. To our knowledge, this is the first computerized image processing method for evaluation of the disease. Discrimination between patients and controls shows the validity of the method. Also, post-treatment changes observed in four patients support this verdict.
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Mostad, Petter, Andreas Schmeling, and Fredrik Tamsen. "Mathematically optimal decisions in forensic age assessment." International Journal of Legal Medicine 136, no. 3 (December 15, 2021): 765–76. http://dx.doi.org/10.1007/s00414-021-02749-y.
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AbstractForensic age estimation generally involves considerable amounts of uncertainty. Forensic age indicators such as teeth or skeleton images predict age only approximately, and this is likely to remain true even for future forensic age indicators. Thus, forensic age assessment should aim to make the best possible decisions under uncertainty. In this paper, we apply mathematical theory to make statistically optimal decisions to age assessment. Such an application is fairly straightforward assuming there is a standardized procedure for obtaining age indicator information from individuals, assuming we have data from the application of this procedure to a group of persons with known ages, and assuming the starting point for each individual is a probability distribution describing prior knowledge about the persons age. The main problem is then to obtain such a prior. Our analysis indicates that individual priors rather than a common prior for all persons may be necessary. We suggest that caseworkers, based on individual case information, may select a prior from a menu of priors. We show how information may then be collected over time to gradually increase the robustness of the decision procedure. We also show how replacing individual prior distributions for age with individual prior odds for being above an age limit cannot be recommended as a general method. Our theoretical framework is applied to data where the maturity of the distal femur and the third molar is observed using MRI. As part of this analysis we observe a weak positive conditional correlation between maturity of the two body parts.