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Relevant bibliographies by topics / NIRFAST

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Academic literature on the topic 'NIRFAST'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "NIRFAST"

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Mellors, Ben O. L., and Hamid Dehghani. "A Pixel-Dependent Finite Element Model for Spatial Frequency Domain Imaging Using NIRFAST." Photonics 8, no. 8 (August 2, 2021): 310. http://dx.doi.org/10.3390/photonics8080310.

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Spatial frequency domain imaging (SFDI) utilizes the projection of spatially modulated light patterns upon biological tissues to obtain optical property maps for absorption and reduced scattering. Conventionally, both forward modeling and optical property recovery are performed using pixel-independent models, calculated via analytical solutions or Monte-Carlo-based look-up tables, both assuming a homogenous medium. The resulting recovered maps are limited for samples of high heterogeneity, where the homogenous assumption is not valid. NIRFAST, a FEM-based image modeling and reconstruction tool, simulates complex heterogeneous tissue optical interactions for single and multiwavelength systems. Based on the diffusion equation, NIRFAST has been adapted to perform pixel-dependent forward modeling for SFDI. Validation is performed within the spatially resolved domain, along with homogenous structured illumination simulations, with a recovery error of <2%. Heterogeneity is introduced through cylindrical anomalies, varying size, depth and optical property values, with recovery errors of <10%, as observed across a variety of simulations. This work demonstrates the importance of pixel-dependent light interaction modeling for SFDI and its role in quantitative accuracy. Here, a full raw image SFDI modeling tool is presented for heterogeneous samples, providing a mechanism towards a pixel-dependent SFDI image modeling and parameter recovery system.

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Dehghani, Hamid, Matthew E. Eames, Phaneendra K. Yalavarthy, Scott C. Davis, Subhadra Srinivasan, Colin M. Carpenter, Brian W. Pogue, and Keith D. Paulsen. "Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction." Communications in Numerical Methods in Engineering 25, no. 6 (June 2009): 711–32. http://dx.doi.org/10.1002/cnm.1162.

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Gatzoulis, Konstantinos A., Dimitrios Tsiachris, Petros Arsenos, Christos-Konstantinos Antoniou, Polychronis Dilaveris, Skevos Sideris, Emmanuel Kanoupakis, et al. "Arrhythmic risk stratification in post-myocardial infarction patients with preserved ejection fraction: the PRESERVE EF study." European Heart Journal 40, no. 35 (May 3, 2019): 2940–49. http://dx.doi.org/10.1093/eurheartj/ehz260.

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Abstract Aims Sudden cardiac death (SCD) annual incidence is 0.6–1% in post-myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF)≥40%. No recommendations for implantable cardioverter-defibrillator (ICD) use exist in this population. Methods and results We introduced a combined non-invasive/invasive risk stratification approach in post-MI ischaemia-free patients, with LVEF ≥ 40%, in a multicentre, prospective, observational cohort study. Patients with at least one positive electrocardiographic non-invasive risk factor (NIRF): premature ventricular complexes, non-sustained ventricular tachycardia, late potentials, prolonged QTc, increased T-wave alternans, reduced heart rate variability, abnormal deceleration capacity with abnormal turbulence, were referred for programmed ventricular stimulation (PVS), with ICDs offered to those inducible. The primary endpoint was the occurrence of a major arrhythmic event (MAE), namely sustained ventricular tachycardia/fibrillation, appropriate ICD activation or SCD. We screened and included 575 consecutive patients (mean age 57 years, LVEF 50.8%). Of them, 204 (35.5%) had at least one positive NIRF. Forty-one of 152 patients undergoing PVS (27–7.1% of total sample) were inducible. Thirty-seven (90.2%) of them received an ICD. Mean follow-up was 32 months and no SCDs were observed, while 9 ICDs (1.57% of total screened population) were appropriately activated. None patient without NIRFs or with NIRFs but negative PVS met the primary endpoint. The algorithm yielded the following: sensitivity 100%, specificity 93.8%, positive predictive value 22%, and negative predictive value 100%. Conclusion The two-step approach of the PRESERVE EF study detects a subpopulation of post-MI patients with preserved LVEF at risk for MAEs that can be effectively addressed with an ICD. Clinicaltrials.gov identifier NCT02124018

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Li, Yunlong, Qi You, Ziyang Wang, Ying Cao, Christopher J. Butch, Nida El Islem Guissi, Huiming Cai, Yiqing Wang, and Qian Lu. "A study on setting standards for near-infrared fluorescence-image guided surgery (NIRFGS) time lapse monitoring based on preoperative liver function assessment." Annals of Translational Medicine 10, no. 2 (January 2022): 96. http://dx.doi.org/10.21037/atm-21-6975.

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Zheng, Gang, Hui Li, Kathy Yang, Dana Blessington, Kai Licha, Sissel Lund-Katz, Britton Chance, and Jerry D. Glickson. "Tricarbocyanine cholesteryl laurates labeled LDL: new near infrared fluorescent probes (NIRFs) for monitoring tumors and gene therapy of familial hypercholesterolemia." Bioorganic & Medicinal Chemistry Letters 12, no. 11 (June 2002): 1485–88. http://dx.doi.org/10.1016/s0960-894x(02)00193-2.

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Zheng, Gang, Hui Li, Kathy Yang, Dana Blessington, Kai Licha, Sissel Lund-Katz, Britton Chance, and Jerry D. Glickson. "ChemInform Abstract: Tricarbocyanine Cholesteryl Laurates Labeled LDL: New Near Infrared Fluorescent Probes (NIRFs) for Monitoring Tumors and Gene Therapy of Familial hypercholesterolemia." ChemInform 33, no. 38 (May 19, 2010): no. http://dx.doi.org/10.1002/chin.200238190.

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Narihiro, Satoshi, Syunsuke Nakashima, Mutsumi Kazi, Satoshi Yoshioka, Kazuo Kitagawa, Naoki Toya, and Ken Eto. "Effectiveness of fluorescence-guided methods using near-infrared fluorescent clips of robotic colorectal surgery: a case report." Surgical Case Reports 9, no. 1 (May 17, 2023). http://dx.doi.org/10.1186/s40792-023-01666-z.

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Abstract Background This is the first report on the application of the Da Vinci-compatible near-infrared fluorescent clips (NIRFCs) as tumor markers to localize colorectal cancer lesions during robotic surgery. In laparoscopic and robotic colorectal surgeries, the accuracy of tumor marking is a critical issue that remains unresolved. This study aimed to determine the accuracy of NIRFCs in localizing tumors for intestinal resection. Indocyanine green (ICG) was also used to verify the feasibility of safely performing an anastomosis. Case presentation A patient diagnosed with rectal cancer was scheduled to undergo a robot-assisted high anterior resection. During colonoscopy 1 day prior to the surgery, four Da Vinci-compatible NIRFCs were placed intraluminally 90° around the lesion. The locations of the Da Vinci-compatible NIRFCs were confirmed using firefly technology, and ICG staining was performed before cutting the oral side of the tumor. The locations of the Da Vinci-compatible NIRFCs and the intestinal resection line were confirmed. Moreover, sufficient margins were obtained. Conclusions In robotic colorectal surgery, fluorescence guidance with firefly technology offers two advantages. First, it has an oncological advantage, because marking with the Da Vinci-compatible NIRFCs allows for real-time monitoring of the lesion location. This enables sufficient intestinal resection by grasping the lesion precisely. Second, it reduces the risk of postoperative complications, because ICG evaluation with firefly technology prevents postoperative anastomotic leakage. Fluorescence guidance in robot-assisted surgery is useful. In the future, the application of this technique should be evaluated for lower rectal cancer.

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Arsenos, P., K. Gatzoulis, I. Doundoulakis, P. Dilaveris, C. K. Antoniou, S. Sideris, and D. Tousoulis. "Arrhythmic risk stratification in heart failure mid-range ejection fraction patients with a non-invasive guiding to programmed ventricular stimulation two-step approach." European Heart Journal 41, Supplement_2 (November 1, 2020). http://dx.doi.org/10.1093/ehjci/ehaa946.0765.

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Abstract Background Although some post myocardial infarction (post-MI) and dilated cardiomyopathy (DCM) patients with mid-range ejection fraction heart failure (HFmrEF = 40–49%) face an increased risk for arrhythmic Sudden Cardiac Death (SCD), current guidelines do not recommend an implantable cardioverter-defibrillator (ICD). Purpose To assess the accuracy of a novel multifactorial two-step approach, with noninvasive risk factors (NIRFs) leading to programmed ventricular stimulation (PVS), for SCD risk stratification of hospitalized HFmrEF patients. Methods Forty-eight patients (male=83%, age = 64±14 years, LVEF = 45±5%, ischemic coronary disease = 69%) underwent a NIRF presence screening first step with ECG, SAECG, echocardiography and 24 hour ambulatory ECG (Holter). Thirty-two patients with presence of one out of three NIRFs (SAECG ≥2 positive criteria for late potentials, ventricular premature beats ≥240/24 hours, and ≥1 episode of non-sustained ventricular tachycardia on Holter) were further stratified with PVS. Patients were classified as either low (Group 1, n=16, NIRFs−), moderate (Group 2, n=18, NIRFs+ /PVS−) or high risk (Group 3, n=14, NIRFs+/PVS+). All Group 3 patients received an ICD. Results After 41±18 months, 9 out of 48 patients experienced the major arrhythmic event (MAE) endpoint (clinical ventricular tachycardia/fibrillation = 3, appropriate ICD activation = 6). The endpoint occurred more frequently in Group 3 (7/14, 50%) than in Groups 1 & 2 (2/34, 5.8%). A logistic regression model adjusted for PVS, age and LVEF revealed that PVS was an independent MAE predictor (OR: 21.152, 95% CI: 2.618–170.887, p=0.004). Kaplan Meier curves diverged significantly (p logrank <0.001) while PVS negative predictive value was 94%. Conclusion In hospitalized HFmrEF post-MI and DCM patients, a NIRFs leading to PVS two-step approach efficiently detected the subgroup at increased risk for MAEs. Funding Acknowledgement Type of funding source: None

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Uchida, Yasumi. "Abstract 2779: Two- Dimentional Imaging Of Lipids Deposited In The Coronary Plaques By Near-infrared Fluorescence Angioscopy." Circulation 116, suppl_16 (October 16, 2007). http://dx.doi.org/10.1161/circ.116.suppl_16.ii_617-a.

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Background: The lipids deposited deep in the vascular wall are hardly detectable by any available imaging modalities including IVUS and OCT. Since near-infrared (NIR) light penetrates relatively deeply into the tissues, we devised a near-infrared fluorescence angioscope (NIRFA) for two dimensional imaging of lipids deposited even in deep layers of vascular wall. Aim: To examine feasibility of NIRFA for imaging of lipids in coronary arterial wall in man. Methods: NIRFA is composed of a quartz fiberscope incorporated in 5F balloon catheter, fluorescence exciter, fluorescence emitter, ICCD and recorder. NIR exciter filter of 710nm and emitter filter of 780nm were employed since among the major 30 substances composing atherosclerotic plaques, free cholesterol and cholesteryl esters alone exhibit NIR autofluorescence while oxLDL and TG not. In vitro study: Coronary artery removed from human cadaver was perfused with saline and an angioscope was introduced into it for imaging of lipids and the obtained NIR images were compared with histology. Clinical study: During routine coronary angiography, lipid deposition in the coronary artery was surveyed by NIRFA in 7 patients with coronary artery disease. Results: In vitro study. Coronary segments without lipid deposition by histology did not exhibit autofluorescence. Those with lipid deposition by histology, the lipids deposited within 700μm in depth from luminal surface exhibited autofluorescence. Deposited lipids not forming lipid pool exhibited strong and homogenous autofluorescence. Lipid pool exhibited no or weak autofluorescence surrounded by strong ones, indicating it is filled with oxLDL and/or TG. Clinical study. Autofluorescence was frequently detected not only in yellow plaques but also in white plaques by conventional angioscopy and hard plaques by IVUS. Also, autofluorescence was frequently detected in apparently normal coronary segments. Conclusion: The results indicate that this NIRFA is feasible for two dimensional imaging of lipids deposited even in deep layers of coronary arterial wall which are not detectable by conventional angioscopy, OCT and IVUS.

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Xintarakou, A., P. Arsenos, K. Gatzoulis, G. Manis, K. Trachanas, S. Soulaidopoulos, P. Dilaveris, et al. "Prediction of programmed ventricular stimulation inducibility using machine learning in post-myocardial infarction patients at risk for sudden cardiac arrest with preserved ejection fraction ≥40%." European Heart Journal 43, Supplement_2 (October 1, 2022). http://dx.doi.org/10.1093/eurheartj/ehac544.681.

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Abstract Introduction Sudden cardiac death (SCD) in post myocardial infarction (post-MI) patients with a relatively preserved left ventricular ejection fraction (LVEF ≥40%) has 1% annual incidence. In the PRESERVE-EF study, we used a two-step SCD risk stratification approach to detect patients with a relatively preserved left ventricular ejection fraction ≥40% at risk for major arrhythmic events. Seven noninvasive risk factors (NIRFs) were extracted from ambulatory electrocardiography. Patients with at least one NIRF present were referred for invasive programmed ventricular stimulation (PVS). Inducible patients received an ICD. Purpose The present study examines the performance of machine learning technology for the prediction of the inducible patients in PRESERVE-EF study. Methods After first step screening with NIRFs, 152 out of 575 patients underwent PVS and 41 of them were inducible. For the present analysis, data from these 152 patients were analysed. We used machine learning of NIRFs to predict these inducible high risk patients. We selected as classification method the Nearest Neighbour (NN) algorithm, after experimentation with several classifiers. NN classifies each subject according to the class of the N nearest neighbours. For each subject, we created a vector with the following 7 features: SAECG Late Potentials, Ventricular Premature beats ≥30/hour, Non-sustained Ventricular Tachycardia ≥1 episode (s)/24 hours, Fredericia corrected QT interval ≥45 0ms, SDNN/HRV ≤75 ms, T Wave Alternans ≥65 μV, Combined Deceleration capacity (DC) ≤4.5 ms and Heart Rate Turbulence Onset (To) ≥0% and Heart Rate Turbulence Slope (Ts) ≤2.5 ms. Results The achieved accuracy reached up to 72.2% when N was set to 7. We had totally 144 samples, 41 of which were inducible high risk patients. Results were similar for other values of N. To ensure independence of train and test sets, we employed 10-fold cross validation. Conclusions Inducible on PVS patients in PRESERVE-EF study were predicted with machine learning classification of NIRFs. Funding Acknowledgement Type of funding sources: None.

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Dissertations / Theses on the topic "NIRFAST"

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Arumugaraj, M. "Deep learning methods for light fluence compensation in two-dimensional and three-dimensional photoacoustic imaging." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5905.

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Photoacoustic imaging (PAI) employed the special properties of light or photons to obtain detailed images of organs, tissues, cells, and even molecules. The method allowed for a non-invasive or minimally invasive examination within the body. The PAI used nearinfrared light (600 nm - 900 nm) as the scan media, which had the additional benefit of being a non-ionizing imaging modality. The PAI can be integrated with other imaging modalities, such as MRI or X-ray, to provide better information for complex diseases or researchers working on complex experiments. Photoacoustic imaging has already been widely used in pre-clinical research to image small animals. Although PAI is a multi-scale modality, it is challenging to use for clinical research and interventional applications due to the non-linear distribution of optical fluence. Quantitative Photoacoustic Imaging (QPAI) has remained problematic due to the influence of non-linear optical fluence distribution, which influences photoacoustic image representation. Non-linear optical fluence correction in PA imaging was highly ill-posed, leading to inaccurate recovery of optical absorption maps. Note that the traditional optical fluence correction method needs precise estimation of optical fluence map. Many different light transport models exist for estimating the optical fluence map when the optical properties, i.e., optical absorption and optical scattering are known. However, in reality the optical properties are unknown in advance, therefore fluence estimation becomes difficult during PA imaging. Moreover, optical light illumination at the target medium under the study is not uniform over the wavelength, the target medium introduce spectral distortion between the measured PA spectrum and the true target spectrum. Consequently, for true QPAI, the optical fluence must be simultaneously estimated and compensated. This requires not only an appropriate fluence model, but also an effective method to estimate the fluence distribution at each wavelength from PA measurements. Based on prior knowledge of the target medium’s optical properties, many different methods have been proposed for fluence compensation for a simple and homogeneous medium. Unfortunately, none translate into clinical usage. To translate to clinical usage, more complex and heterogeneous media need to be studied. And also the generated PA signal may also change dynamically based on the background tissue properties. Hence, consider complex, foreground and background non-homogeneity of the medium for accurate recovery of optical absorption coefficient. None of the research groups adapted all the above factors simultaneously for fluence compensation. This thesis study developed a deep learning-based optical fluence correction approach to solving this limitation. The main objective of this thesis was to investigate the non-linear distribution of optical fluence effect in 2D and 3D medium and compensate this effect by using deep learning (DL) models. This thesis explains the recovery of the optical absorption maps using deep learning approaches by correcting the fluence effect. In this thesis, different deep learning models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a non-homogeneous foreground and background medium. Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the non-linear optical fluence distribution. The trained deep learning models like U-Net, FD U-Net, Y-Net, FD Y-Net, Deep ResUnet, and GAN were tested to evaluate the performance of optical absorption coefficient recovery with in-silico and in-vivo dataset. The results indicated that DL-based deconvolution improves the reconstructed PAI in terms of PSNR and SSIM. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction and also able to compensate for nonlinear optical fluence distribution more effectively and improve the photoacoustic image quality.

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Book chapters on the topic "NIRFAST"

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Sidhu, Simran, and Surinder Singh Khurana. "Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews." In Handbook of Research on Emerging Trends and Applications of Machine Learning, 1–26. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9643-1.ch001.

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A large number of reviews are expressed on academic institutes using the online review portals and other social media platforms. Such reviews are a good potential source for evaluating the Indian academic institutes. This chapter aimed to collect and analyze the sentiments of the online reviews of the academic institutes and ranked the institutes on the basis of their garnered online reviews. Lexical-based sentiment analysis of their online reviews is used to rank academic institutes. Then these rankings were compared with the NIRF PR Overall University Rankings List 2017. The outcome of this work can efficiently support the overall university rankings of the NIRF ranking list to enhance NIRF's public perception parameter (PRPUB). The results showed that Panjab University achieved the highest sentiment score, which was followed by BITS-Pilani. The results highlighted that there is a significant gap between NIRF's perception rankings and the perception of the public in general regarding an academic institute as expressed in online reviews.

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Sidhu, Simran, and Surinder Singh Khurana. "Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, 555–80. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch031.

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A large number of reviews are expressed on academic institutes using the online review portals and other social media platforms. Such reviews are a good potential source for evaluating the Indian academic institutes. This chapter aimed to collect and analyze the sentiments of the online reviews of the academic institutes and ranked the institutes on the basis of their garnered online reviews. Lexical-based sentiment analysis of their online reviews is used to rank academic institutes. Then these rankings were compared with the NIRF PR Overall University Rankings List 2017. The outcome of this work can efficiently support the overall university rankings of the NIRF ranking list to enhance NIRF's public perception parameter (PRPUB). The results showed that Panjab University achieved the highest sentiment score, which was followed by BITS-Pilani. The results highlighted that there is a significant gap between NIRF's perception rankings and the perception of the public in general regarding an academic institute as expressed in online reviews.

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Conference papers on the topic "NIRFAST"

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Malinsky, Milan, Michael Jermyn, Brian W. Pogue, and Hamid Dehghani. "An Online Modeling and Image Reconstruction Tool for Optical Imaging based on NIRFAST." In Biomedical Optics. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/biomed.2010.bsud27.

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Althobaiti, Murad M., and Quing Zhu. "Evaluation of a Dual-Mesh for Reconstruction of Diffuse Optical Tomography using NIRFAST." In Cancer Imaging and Therapy. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/cancer.2016.jtu3a.9.

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Wojtkiewicz, Stanislaw, Udo M. Weigel, Turgut Durduran, and Hamid Dehghani. "Cloud-based NIRFAST server for tissue parameters recovery: laser and ultrasound co-analyser of thyroid nodules." In Diffuse Optical Spectroscopy and Imaging, edited by Hamid Dehghani and Heidrun Wabnitz. SPIE, 2019. http://dx.doi.org/10.1117/12.2527018.

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Srinivasan, Subhadra, Hamid R. Ghadyani, and Michael Jeremyn. "BEM-NIRFAST: Open source software for 3D Image-guided near-infrared spectroscopy using boundary element method." In European Conference on Biomedical Optics. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/ecbo.2011.80881t.

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Srinivasan, Subhadra, Hamid R. Ghadyani, and Michael Jeremyn. "BEM-NIRFAST: open source software for 3D image-guided near infrared spectroscopy using boundary element method." In European Conferences on Biomedical Optics, edited by Andreas H. Hielscher and Paola Taroni. SPIE, 2011. http://dx.doi.org/10.1117/12.891276.

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