Parallel, multi-purpose Monte Carlo code for simulation of light propagation in segmented tissues

Wojtkiewicz, Stanisław; Liebert, Adam

doi:10.1016/j.bbe.2021.03.001

Cited by 9 publications

(8 citation statements)

References 67 publications

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“…According to this figure, the most used MRI sequence type is T1-weighted, and the sequence was used for different purposes such as segmentation [ 43 ] in 2023, image restoration in 2019 [ 52 ], reconstruction both in 2021 and 2022 [ 45 , 46 ], surface mapping in 2022 [ 44 ], and feature extraction in 2019 [ 54 ]. The second most used MRI sequence type is T2-weighted, and it was used for various tasks such as pulse sequence simulation in 2023 [ 42 ], reconstruction in 2021 [ 47 ], and simulation of a human head in 2021 [ 48 ]. The T1-contrast and the FLAIR sequences were used for the purpose of segmentation.…”

Section: Discussionmentioning

confidence: 99%

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GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

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The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

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Section: Discussionmentioning

confidence: 99%

“…In the study by [ 48 ], a massive and parallel, multi-purpose Monte Carlo code was developed for the simulation of complicated structures, which are modeled with MRI images. The code was specifically created to run in parallel on a cluster of Linux-based computers that have several GPUs due to NVidia CUDA technology.…”

Section: Literature Reviewmentioning

confidence: 99%

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

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“…[ 38 ] Therefore, accurate acquisition of light transmission and distribution in biological tissues is the basis of photonic medical diagnosis and treatment. [ 39 ] In Figure , the biomedical applications of diffuse reflection spectroscopy (see Section 3), bioimaging (see Section 4), and photobiomodulation (see Section 5) between NIR and biological tissues are shown.…”

Section: Near‐infrared Windowsmentioning

confidence: 99%

Near‐Infrared Windows I and II Phosphors for Theranostic Applications: Spectroscopy, Bioimaging, and Light‐Emitting Diode Photobiomodulation

Huang

Rajendran

Chan

et al. 2022

Advanced Optical Materials

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The importance and practicality of near-infrared (NIR) light for the biomedical field has attached importance to the academe and industry with the development of technology. Luminescent materials with different doping strategies are promising candidates for covering near-infrared biological windows from 700 nm to 1 700 nm. The relationship between NIR light, nanophosphors, and biological tissue towards the function of diagnostic and therapy in different NIR windows is explored clearly, which corresponds to diffuse reflection spectroscopy with bioimaging and light-emitting diode (LED) photobiomodulation. The diseases can be monitored with specific chromophore absorption under the NIR light source, which also can be traced to the dynamic change of the biological environment by luminescent materials. Especially in brain diseases, cytochrome c oxidase in mitochondria as the crucial photoreceptor can be activated under NIR irradiation to achieve the non-invasive treatment of neuron protection and recovery. NIR light attributed to LED or nanophosphors show the potential of theranostic approaches in biomedical technology. Cr 3+ -and Ni 2+ -activated phosphors covering NIR windows I and II are presented with optical properties to explore the feasibility and practicability of non-invasive precise medical treatment in the future.

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“…DTOFs were generated with Monte Carlo (MC) simulations which used the code introduced by Wojtkiewicz et al [9]. Three MC simulations were carried out: homogeneous medium with μa = 0.01 mm -1 , 15% increase in absorption in the superficial layer only, and 15% increase in absorption in the deeper layer only.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Depth selectivity in time-domain fNIRS by analyzing moments and time windows

Sudakou

Wabnitz

Lin

et al. 2021

Optical Tomography and Spectroscopy of Tissue XIV

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Time-domain fNIRS facilitates the elimination of the influence of extracerebral, systemic effects on measured signals since it contains time-of-flight information that is related to the penetration depth. Employing perturbation and Monte-Carlo simulations, we quantitatively characterized and compared the performance of measurands based on moments and time windows of time-of-flight distributions. We extend our analysis to investigate whether higher moments and Mellin-Laplace (ML) moments promise improvements in performance. The comparison is based on spatial sensitivity profiles as well as metrics for relative contrast, contrast-to-noise ratio (CNR), depth selectivity, and the product of CNR and depth selectivity for layered absorption changes. The influence of reduced scattering coefficient, thickness of the superficial layer, and source-detector distance was analyzed. The third central moment performs similarly to variance and is worth considering for data analyzes. Higher order ML moments perform similarly to time windows and they likewise provide variable depth selectivity.

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Parallel, multi-purpose Monte Carlo code for simulation of light propagation in segmented tissues

Cited by 9 publications

References 67 publications

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Near‐Infrared Windows I and II Phosphors for Theranostic Applications: Spectroscopy, Bioimaging, and Light‐Emitting Diode Photobiomodulation

Depth selectivity in time-domain fNIRS by analyzing moments and time windows

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