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Journal articles on the topic '3D medical imaging'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Squelch, Andrew. "3D printing and medical imaging." Journal of Medical Radiation Sciences 65, no. 3 (September 2018): 171–72. http://dx.doi.org/10.1002/jmrs.300.

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2

Bhatia, Galub, and Michael Vannier. "3D surface imaging for medical applications." ACM SIGBIO Newsletter 14, no. 3 (September 1994): 7–8. http://dx.doi.org/10.1145/192602.953450.

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3

Stytz, Martin R., and Rob W. Parrott. "Using kriging for 3d medical imaging." Computerized Medical Imaging and Graphics 17, no. 6 (November 1993): 421–42. http://dx.doi.org/10.1016/0895-6111(93)90059-v.

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4

Bakalash, Reuven, and Arie Kaufman. "Medicube: A 3D medical imaging architecture." Computers & Graphics 13, no. 2 (January 1989): 151–57. http://dx.doi.org/10.1016/0097-8493(89)90057-5.

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5

Coatrieux, J. L., C. Toumoulin, C. Hamon, and L. Luo. "Future trends in 3D medical imaging." IEEE Engineering in Medicine and Biology Magazine 9, no. 4 (December 1990): 33–39. http://dx.doi.org/10.1109/51.105216.

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6

Ney, D. R., and E. K. Fishman. "Editing tools for 3D medical imaging." IEEE Computer Graphics and Applications 11, no. 6 (November 1991): 63–71. http://dx.doi.org/10.1109/38.103395.

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7

Gunn, Therese. "3. A 3D VIRTUAL MEDICAL IMAGING SUITE." Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 9, no. 1 (February 2014): 74. http://dx.doi.org/10.1097/01.sih.0000444025.17185.de.

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8

Gemmeke, H., and N. V. Ruiter. "3D ultrasound computer tomography for medical imaging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 580, no. 2 (October 2007): 1057–65. http://dx.doi.org/10.1016/j.nima.2007.06.116.

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9

Herman, G. T. "A survey of 3D medical imaging technologies." IEEE Engineering in Medicine and Biology Magazine 9, no. 4 (December 1990): 15–17. http://dx.doi.org/10.1109/51.105212.

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10

Parioti, Evgenia, Stavros Pitoglou, Arianna Filntisi, Athanasios Anastasiou, Ourania Petropoulou, and Dimitris Dionisios Koutsouris. "The Added Value of 3D Imaging and 3D Printing in Head and Neck Surgeries." International Journal of Reliable and Quality E-Healthcare 10, no. 3 (July 2021): 68–81. http://dx.doi.org/10.4018/ijrqeh.2021070105.

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3D imaging and 3D printing are two methods that have been proven very useful in medicine. The objective of 3D medical imaging is to recreate the static and functional anatomy of the inner body. The development of computational systems for image processing and multidimensional monitoring of medical data is important for diagnosis and treatment planning. The technique of 3D printing has enabled the materialization of anatomical models and surgical splints using medical imaging data. The methods of 3D imaging and 3D printing have been utilized in various medical fields such as neuroimaging, neurosurgery, dentistry, otolaryngology and facial plastic surgery. This review aims to evaluate the use of 3D imaging and 3D printing techniques in head and neck surgery and concludes that these technologies have revolutionized medicine. However, improvements in healthcare systems and further research still have to be made to establish their use in everyday medical practices.
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11

Pellegrini, Giulio, P. Roy, A. Al-Ajili, R. Bates, L. Haddad, M. Horn, K. Mathieson, et al. "Technology development of 3D detectors for medical imaging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 504, no. 1-3 (May 2003): 149–53. http://dx.doi.org/10.1016/s0168-9002(03)00811-8.

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12

Sakas, Georgios. "Trends in medical imaging: from 2D to 3D." Computers & Graphics 26, no. 4 (August 2002): 577–87. http://dx.doi.org/10.1016/s0097-8493(02)00103-6.

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13

Magalhães, Daniel S. F., Rolando L. Serra, André L. Vannucci, Alfredo B. Moreno, and Li M. Li. "Glasses-free 3D viewing systems for medical imaging." Optics & Laser Technology 44, no. 3 (April 2012): 650–55. http://dx.doi.org/10.1016/j.optlastec.2011.09.015.

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14

Parrott, R. W., M. R. Stytz, P. Amburn, and D. Robinson. "Towards statistically optimal interpolation for 3D medical imaging." IEEE Engineering in Medicine and Biology Magazine 12, no. 3 (September 1993): 49–59. http://dx.doi.org/10.1109/51.232341.

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15

Abdullah, Kamarul A., and Warren Reed. "3D printing in medical imaging and healthcare services." Journal of Medical Radiation Sciences 65, no. 3 (July 3, 2018): 237–39. http://dx.doi.org/10.1002/jmrs.292.

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16

Singh, Satya P., Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, and Balázs Gulyás. "3D Deep Learning on Medical Images: A Review." Sensors 20, no. 18 (September 7, 2020): 5097. http://dx.doi.org/10.3390/s20185097.

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The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
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17

Suryakanth, B., and S. A. Hari Prasad. "3D CNN-Residual Neural Network Based Multimodal Medical Image Classification." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (October 31, 2022): 204–14. http://dx.doi.org/10.37394/23208.2022.19.22.

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Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method uses 3D Residual Convolutional Neural Network (CNN ResNet) for the 3D image classification. Various methods are available for classifying the disease, like cluster, KNN, and ANN. Traditional techniques are not trained to classify 3D images, so an advanced approach is introduced in the proposed method to predict the 3D images. Initially, the multimodal 2D medical image data is taken. This 2D input image is turned into 3D image data because 3D images give more information than the 2D image data. Then the 3D CT and MRI images are fused and using the Guided filtering, and the combined image is filtered for the further process. The fused image is then augmented. Finally, this fused image is fed to 3DCNN ResNet for classification purposes. The 3DCNN ResNet classifies the image data and produces the output as five different stages of the disease. The proposed method achieves 98% of accuracy. Thus the designed modal has predicted the stage of the disease in an effective manner.
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18

Zaffino, Paolo, Alessio Merola, Domenico Leuzzi, Virgilio Sabatino, Carlo Cosentino, and Maria Francesca Spadea. "SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller." Bioengineering 7, no. 3 (September 11, 2020): 109. http://dx.doi.org/10.3390/bioengineering7030109.

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Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input–output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input–output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks.
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19

Sun, Zhonghua. "3D Printing in Medical Applications." Current Medical Imaging Formerly Current Medical Imaging Reviews 17, no. 7 (August 5, 2021): 811–13. http://dx.doi.org/10.2174/157340561707210702114259.

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20

Lechelek, Loubna, Sylvain Gerbaud, Rita Zrour, Mathieu Naudin, Carole Guillevin, and Sebastien Horna. "Comparative Study of 3D Reconstruction Methods for Medical Imaging." Computer-Aided Design and Applications 19, no. 5 (January 21, 2022): 1000–1014. http://dx.doi.org/10.14733/cadaps.2022.1000-1014.

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21

Becciu, Alessandro, Andrea Fuster, Mark Pottek, Bart van den Heuvel, Bart ter Haar Romeny, and Hans van Assen. "3D Winding Number: Theory and Application to Medical Imaging." International Journal of Biomedical Imaging 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/516942.

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We develop a new formulation, mathematically elegant, to detect critical points of 3D scalar images. It is based on a topological number, which is the generalization to three dimensions of the 2D winding number. We illustrate our method by considering three different biomedical applications, namely, detection and counting of ovarian follicles and neuronal cells and estimation of cardiac motion from tagged MR images. Qualitative and quantitative evaluation emphasizes the reliability of the results.
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22

Chang, Jincai, Jianzhong Cui, Shuhan Jin, Guoqing Yu, and Jingyu Song. "Development environment construction of medical imaging software 3d slicer." Journal of Complexity in Health Sciences 3, no. 1 (June 30, 2020): 43–51. http://dx.doi.org/10.21595/chs.2020.21264.

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23

Birk, Matthias, Ernst Kretzek, Peter Figuli, Marc Weber, Jurgen Becker, and Nicole V. Ruiter. "High-Speed Medical Imaging in 3D Ultrasound Computer Tomography." IEEE Transactions on Parallel and Distributed Systems 27, no. 2 (February 1, 2016): 455–67. http://dx.doi.org/10.1109/tpds.2015.2405508.

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24

Bücking, Thore M., Emma R. Hill, James L. Robertson, Efthymios Maneas, Andrew A. Plumb, and Daniil I. Nikitichev. "From medical imaging data to 3D printed anatomical models." PLOS ONE 12, no. 5 (May 31, 2017): e0178540. http://dx.doi.org/10.1371/journal.pone.0178540.

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25

Cloetens, Peter, Wolfgang Ludwig, Elodie Boller, Françoise Peyrin, Michel Chlenker, and Jose Baruchel. "3D IMAGING USING COHERENT SYNCHROTRON RADIATION." Image Analysis & Stereology 21, no. 4 (May 3, 2011): 75. http://dx.doi.org/10.5566/ias.v21.ps75-s85.

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Three dimensional imaging is becoming a standard tool for medical, scientific and industrial applications. The use of modem synchrotron radiation sources for monochromatic beam micro-tomography provides several new features. Along with enhanced signal-to-noise ratio and improved spatial resolution, these include the possibility of quantitative measurements, the easy incorporation of special sample environment devices for in-situ experiments, and a simple implementation of phase imaging. These 3D approaches overcome some of the limitations of 2D measurements. They require new tools for image analysis.
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26

Exner, Miriam, Patryk Szwargulski, Tobias Knopp, Matthias Graeser, and Peter Ludewig. "3D Printed Anatomical Model of a Rat for Medical Imaging." Current Directions in Biomedical Engineering 5, no. 1 (September 1, 2019): 187–90. http://dx.doi.org/10.1515/cdbme-2019-0048.

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AbstractFor medical research, approximately 115 million animals are needed every year. Rodents are used to test possible applications and procedures for the diagnosis of anatomical and physiological diseases. However, working with living animals increases the complexity of an experiment. Accurate experimental planning is essential in order to fulfill the 3R rules (replace, reduce and refine). Especially in tracer-based imaging modalities, such as magnetic particle imaging (MPI), where only nanoparticles give a positive contrast, the anatomical structure of the rodent is not visible without co-registration with another imaging modality. This leads to problems in the experimental planning, as parameters, such as field of view, rodent position and tracer concentration, have to be determined without visual feedback. In this work, a 3D CAD rat model is presented, which can be used to improve the experiment planning and thus reduce the number of animals required. It was determined using an anatomy atlas and 3D printed with stereolithography. The resulting model contains the most important organs and vessels as hollow cavities. By filling these with appropriate tracer materials, the phantom can be used in different imaging modalities such as MPI, magnetic resonance imaging (MRI) or computed tomography (CT). In a first MPI measurement, the phantom was filled with superparamagnetic nanoparticles. Finally, a successful visualization of all organs and vessels of the phantom was possible. This enables the planning of the experiment and the optimization of experimental parameters for a region of interest, where certain organs in a living animal are localized.
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27

Kumar, Deepak, and Jitendra Bhaskar. "A Review on Modelling of Knee Joint Using Medical Imaging Methods." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 5, no. 4 (October 5, 2020): 84–89. http://dx.doi.org/10.35121/ijapie202001146.

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The accuracy of the 3D CAD model of the knee joint is based on various factors like imaging method i.e CT scan, MRI data, modelling software and different algorithms for segmentation. For generating geometrical and CAD model techniques like CT scan, Co-ordinate Measuring Machine (CMM) and 3D laser scanner is used. So in this paper efforts have been made to study the different factors which affect the accuracy of a 3D CAD and additively manufactured knee model. Accuracy of the knee joint is important for anatomical study, implant modeling, and pre-surgical planning. The segmentation technique is another important factor that affects the accuracy of a 3D CAD model so each segmentation technique has its pros and cons therefore evaluation of segmentation technique is also studied and compared with each other.
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28

Mitsouras, Dimitris, Peter Liacouras, Amir Imanzadeh, Andreas A. Giannopoulos, Tianrun Cai, Kanako K. Kumamaru, Elizabeth George, et al. "Medical 3D Printing for the Radiologist." RadioGraphics 35, no. 7 (November 2015): 1965–88. http://dx.doi.org/10.1148/rg.2015140320.

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29

Weadock, WJ. "Quality Control in Medical 3D Printing." Academic Radiology 27, no. 5 (May 2020): 661–62. http://dx.doi.org/10.1016/j.acra.2019.11.001.

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30

Azencott, R., R. Glowinski, J. He, A. Jajoo, Y. Li, A. Martynenko, R. H. W. Hoppe, S. Benzekry, and S. H. Little. "Diffeomorphic Matching and Dynamic Deformable Surfaces in 3d Medical Imaging." Computational Methods in Applied Mathematics 10, no. 3 (2010): 235–74. http://dx.doi.org/10.2478/cmam-2010-0014.

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AbstractWe consider optimal matching of submanifolds such as curves and surfaces by a variational approach based on Hilbert spaces of diffeomorphic transformations. In an abstract setting, the optimal matching is formulated as a minimization problem involving actions of diffeomorphisms on regular Borel measures considered as supporting measures of the reference and the target submanifolds. The objective functional consists of two parts measuring the elastic energy of the dynamically deformed surfaces and the quality of the matching. To make the problem computationally accessible, we use reproducing kernel Hilbert spaces with radial kernels and weighted sums of Dirac measures which gives rise to diffeomorphic point matching and amounts to the solution of a finite dimensional minimization problem. We present a matching algorithm based on the first order necessary optimality conditions which include an initial-value problem for a dynamical system in the trajectories describing the deformation of the surfaces and a final-time problem associated with the adjoint equations. The performance of the algorithm is illustrated by numerical results for examples from medical image analysis.
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31

Fotouhi, Javad, Giacomo Taylor, Mathias Unberath, Alex Johnson, Sing Chun Lee, Greg Osgood, Mehran Armand, and Nassir Navab. "Exploring partial intrinsic and extrinsic symmetry in 3D medical imaging." Medical Image Analysis 72 (August 2021): 102127. http://dx.doi.org/10.1016/j.media.2021.102127.

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32

Park, Sang-Moo, and Jong-Hyo Kim. "Augment Reality in Volumetric Medical Imaging using Stereoscopic 3D Display." International Journal of Computer Graphics & Animation 5, no. 2 (April 30, 2015): 47–52. http://dx.doi.org/10.5121/ijcga.2015.5205.

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33

Janvier, Marie-Ange, Louis-Gilles Durand, Marie-Hélène Roy Cardinal, Isabelle Renaud, Boris Chayer, Pascal Bigras, Jacques de Guise, Gilles Soulez, and Guy Cloutier. "Performance evaluation of a medical robotic 3D-ultrasound imaging system." Medical Image Analysis 12, no. 3 (June 2008): 275–90. http://dx.doi.org/10.1016/j.media.2007.10.006.

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34

Rengier, F., A. Mehndiratta, H. von Tengg-Kobligk, C. M. Zechmann, R. Unterhinninghofen, H. U. Kauczor, and F. L. Giesel. "3D printing based on imaging data: review of medical applications." International Journal of Computer Assisted Radiology and Surgery 5, no. 4 (May 15, 2010): 335–41. http://dx.doi.org/10.1007/s11548-010-0476-x.

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35

Chervyakov, Nikolay, Pavel Lyakhov, and Nikolay Nagornov. "Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging." Applied Sciences 10, no. 4 (February 11, 2020): 1223. http://dx.doi.org/10.3390/app10041223.

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Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.
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36

González Izard, Santiago, Ramiro Sánchez Torres, Óscar Alonso Plaza, Juan Antonio Juanes Méndez, and Francisco José García-Peñalvo. "Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality." Sensors 20, no. 10 (May 23, 2020): 2962. http://dx.doi.org/10.3390/s20102962.

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The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.
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Rajšić, N. "8. 3D imaging of neurophysiological signals." Clinical Neurophysiology 124, no. 7 (July 2013): e11. http://dx.doi.org/10.1016/j.clinph.2012.12.017.

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38

Sun, Zhonghua. "Insights into 3D printing in medical applications." Quantitative Imaging in Medicine and Surgery 9, no. 1 (January 2019): 1–5. http://dx.doi.org/10.21037/qims.2019.01.03.

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Huang, Shih-Feng, Yung-Hsuan Wen, Chi-Hsiang Chu, and Chien-Chin Hsu. "A Shape Approximation for Medical Imaging Data." Sensors 20, no. 20 (October 17, 2020): 5879. http://dx.doi.org/10.3390/s20205879.

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This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson’s disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ± 0.04 classification accuracy, 0.87 ± 0.06 sensitivity, and 0.88 ± 0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification.
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40

Mühlfeld, Christian, and Douglas J. Taatjes. "Introduction: 3D imaging in lung biology." Histochemistry and Cell Biology 155, no. 2 (February 2021): 159–62. http://dx.doi.org/10.1007/s00418-021-01968-z.

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41

Sangeetha, S. K. B., V. Muthukumaran, K. Deeba, Hariharan Rajadurai, V. Maheshwari, and Gemmachis Teshite Dalu. "Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images." Computational Intelligence and Neuroscience 2022 (August 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/8722476.

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The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.
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Ravi, T., Rajesh Ranganathan, Arivazhagan Pugalendhi, and Sivasankar Arumugam. "3D Printed Patient Specific Models from Medical Imaging - A General Workflow." Materials Today: Proceedings 22 (2020): 1237–43. http://dx.doi.org/10.1016/j.matpr.2020.01.416.

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43

Sholukha, Victor, Olivier Snoeck, Fedor Moiseev, Marcel Rooze, and Serge Van Sint Jan. "STEREOPHOTOGRAMMETRY FOR SOFT TISSUE 3D RECONSTRUCTION OF DISSECTION AND MEDICAL IMAGING." Journal of Biomechanics 41 (July 2008): S223. http://dx.doi.org/10.1016/s0021-9290(08)70223-9.

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44

Yoon, Hansol, and Tai-Kyong Song. "Sparse Rectangular and Spiral Array Designs for 3D Medical Ultrasound Imaging." Sensors 20, no. 1 (December 27, 2019): 173. http://dx.doi.org/10.3390/s20010173.

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In three-dimensional (3D) medical ultrasound imaging with two-dimensional (2D) arrays, sparse 2D arrays have been studied to reduce the number of active channels. Among them, sparse 2D arrays with regular or uniform arrangements of elements have advantages of low side lobe energy and uniform field responses over the entire field of view. This paper presents two uniform sparse array models: sparse rectangular arrays (SRAs) on a rectangular grid and sparse spiral arrays (SSAs) on a sunflower grid. Both arrays can be easily implemented on the commercially available or the custom-made arrays. To suppress the overall grating lobe levels, the transmit (Tx) and receive (Rx) array pairs of both the array models are designed not to have grating lobes at the same locations in the Tx/Rx beam patterns, for which the theoretical design rules are also proposed. Computer simulation results indicate that the proposed array pairs for both the SRAs and the SSAs achieve peak grating lobe levels below –40 dB using about a quarter of the number of elements in the dense rectangular array while maintaining similar beam widths to that of the dense array pair.
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45

Povoli, M., A. Kok, O. Koybasi, M. Getz, G. O’Neill, D. Roehrich, E. Monakhov, et al. "3D silicon detectors for neutron imaging applications." Journal of Instrumentation 18, no. 01 (January 1, 2023): C01056. http://dx.doi.org/10.1088/1748-0221/18/01/c01056.

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Abstract Neutron detection is of great importance in many fields spanning from scientific research, to nuclear science, and to medical application. The development of silicon-based neutron detectors with enhanced neutron detection efficiency can offer several advantages such as spatial resolution, enhanced dynamic range and background discrimination. In this work, increased detection efficiency is pursued by fabricating high aspect ratio 3D micro-structures filled with neutron converting materials (B4C) on planar silicon detectors. An in-depth feasibility study was carried out in all aspects of the sensor fabrication technology. Passivation of the etched structures was studied in detail, to ensure good electrical performance. The conformal deposition of B4C with a newly developed process showed excellent results. Preliminary electrical characterisation of the completed devices is promising, and detectors have been mounted on dedicated boards in view of the upcoming tests with neutrons.
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46

Koryciak, Sebastian, Maciej Barszczowski, Agnieszka Dąbrowska-Boruch, and Kazimierz Wiatr. "Medical Visualizer 3D: Hardware Controller for Dmd Module." Image Processing & Communications 19, no. 2-3 (September 1, 2014): 15–23. http://dx.doi.org/10.1515/ipc-2015-0006.

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Abstract In this paper an implementation of the module responsible for the control of micro-mirror array for later use in projection is described. Existing technologies allow for projections of medical images in Digital Imaging and Communications in Medicine format only in the form of a flat 2D image. The 3D Visualizer will allow to display medical images in three dimensions using its own projection surface. The matrix controlling device has been largely developed on the basis of reverse engineering studies carried out on the functional system based on a driver from Texas Instruments. Driver is built on the FPGA with implemented soft processor from Xilinx - MicroBlaze.
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47

Hu, Chuanjie, Shuwen Xue, Yuhang Yin, Zhanlei Hao, Yangyang Zhou, and Huanyang Chen. "Acoustic super-resolution imaging based on solid immersion 3D Maxwell's fish-eye lens." Applied Physics Letters 120, no. 19 (May 9, 2022): 192202. http://dx.doi.org/10.1063/5.0093339.

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Acoustic waves have been widely applied in communications, medical treatment, military, and other aspects. In this Letter, we explore acoustic imaging properties of three-dimensional Maxwell's fish-eye lens (3D-MFEL) with elevated refractive index profile, the analytical and numerical results show that a 3D-MFEL based on solid immersion mechanism can achieve super-resolution imaging without chromatic aberration. In addition, introducing vortex waves into the 3D-MFEL, we further explore the super-resolution imaging properties in reconstructing vortex waves. The valid combination of 3D-MFEL and solid immersion mechanism provides a meaningful way for super-resolution imaging, which also paves a way forward for future designing and manufacturing in acoustic super-imaging systems.
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Galvan, Bernardo, Mohammed Ansari, Ali Akbar Arvandi, Ronnie Orozco, Carlos Morales, Kate Serralde, Ozman Ochoa, Katherine Holder, Marina Iskandir, and Scott Shurmur. "Utility of 3D printing of left atrial appendages for closure with Watchman Devices and comparison of computed tomography and transesophageal echocardiography based models." Southwest Respiratory and Critical Care Chronicles 9, no. 37 (January 28, 2021): 60–65. http://dx.doi.org/10.12746/swrccc.v9i37.789.

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Three dimensional (3D) printed cardiac models are useful for WATCHMAN device procedural planning, sizing, and complication reduction. These models also provide accurate representation of dynamic heart anatomy, helping practitioners determine their procedural approach and select proper device sizing. While the efficacy of 3D models obtained from Computed Tomography and Transesophageal Echocardiography over 2D Transesophageal Echocardiography imaging for WATCHMAN procedural planning has been demonstrated, this project aims to directly compare 3D Computed Tomography versus 3D Transesophageal Echocardiography and determine which is more favorable. Computed Tomography and Transesophageal Echocardiography 2D imaging studies from patients that underwent LAA WATCHMAN closure device implantation we used as templates for 3D cardiac models. These 3D models were scored using a 10-point Likert questionnaire. Scoring was conducted by a diverse team that included cardiologists, research specialists, medical students, and 3D printing technicians. Three dimensional models developed using Computed Tomography demonstrated favorability over 3D models by all qualitative measures. Scoring indicates that Computed Tomography based 3D models are superior tools for WATCHMAN sizing, multi-level medical education, and physician preparedness. To our knowledge, this is the only study that compares 3D models crafted from each imaging modality, and we hope that it encourages future use of 3D modeling techniques based on Computed Tomography scans.
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49

Nagornov, N. N. "Defining of the Minimum Wavelet Filter Coefficients Bit-Width for 3D Medical Imaging." INFORMACIONNYE TEHNOLOGII 27, no. 8 (August 11, 2021): 425–34. http://dx.doi.org/10.17587/it.27.425-434.

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Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).
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Min, Qiusha, Zhifeng Wang, and Neng Liu. "An Evaluation of HTML5 and WebGL for Medical Imaging Applications." Journal of Healthcare Engineering 2018 (August 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/1592821.

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Despite the fact that a large number of web applications are used in the medical community, there are still certain technological challenges that need to be addressed, for example, browser plug-ins and efficient 3D visualization. These problems make it necessary for a specific browser plug-in to be preinstalled on the client side when launching applications. Otherwise, the applications fail to run due to the lack of the required software. This paper presents the latest techniques in hypertext markup language 5 (HTML5) and web graphics library (WebGL) for solving these problems and an evaluation of the suitability of the combination of HTML5 and WebGL for the development of web-based medical imaging applications. In this study, a comprehensive medical imaging application was developed using HTML5 and WebGL. This application connects to the medical image server, runs on a standard personal computer (PC), and is easily accessible via a standard web browser. The several functions required for radiological interpretation were implemented, for example, navigation, magnification, windowing, and fly-through. The HTML5-based medical imaging application was tested on major browsers and different operating systems over a local area network (LAN) and a wide area network (WAN). The experimental results revealed that this application successfully performed two-dimensional (2D) and three-dimensional (3D) functions on different PCs over the LAN and WAN. Moreover, it demonstrated an excellent performance for remote access users, especially over a short time period for 3D visualization and a real-time fly-through navigation. The results of the study demonstrate that HTML5 and WebGL combination is suitable for the development of medical imaging applications. Moreover, the advantages and limitations of these technologies are discussed in this paper.
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