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1. MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Author

Goette, Matthew, Lei, Yang, Mao, Hui, Bradley, Jeffrey, Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, Yang, Xiaofeng, Gao, Yuan, Chang, Chih-Wei, Mandava, Sagar, Marants, Raanan, and Scholey, Jessica

Subjects
Magnetic Resonance Imaging, Deep Learning, Phantoms, Imaging, Proton Therapy, Humans, Animals, Swine, Radiotherapy Planning, Computer-Assisted, Tomography, X-Ray Computed, Radiotherapy Dosage
Abstract

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Published
2024

4. Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Author

Chang, Chih-Wei, Marants, Raanan, Gao, Yuan, Goette, Matthew, Scholey, Jessica, Bradley, Jeffrey, Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, and Yang, Xiaofeng

Subjects
Humans, Proton Therapy, Image Processing, Computer-Assisted, Retrospective Studies, Deep Learning, Protons, Tomography, X-Ray Computed, Multimodal Imaging, Magnetic Resonance Imaging, Obesity
Abstract

OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. METHODS: Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. RESULTS: For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. CONCLUSION: The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. ADVANCES IN KNOWLEDGE: PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.

Published
2023

5. Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms.

Author

Marants, Raanan, Tattenberg, Sebastian, Scholey, Jessica, Kaza, Evangelia, Miao, Xin, Benkert, Thomas, Magneson, Olivia, Fischer, Jade, Vinas, Luciano, Niepel, Katharina, Bortfeld, Thomas, Landry, Guillaume, Parodi, Katia, Verburg, Joost, and Sudhyadhom, Atchar

Subjects
MRI, proton therapy, range uncertainty, stopping power ratio, Animals, Swine, Protons, Tomography, X-Ray Computed, Proton Therapy, Phantoms, Imaging, Magnetic Resonance Imaging, Calibration, Brain Neoplasms, Radiotherapy Planning, Computer-Assisted
Abstract

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had

Published
2023

6. MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy

Author

Gao, Yuan, Chang, Chih-Wei, Mandava, Sagar, Marants, Raanan, Scholey, Jessica E., Goette, Matthew, Lei, Yang, Mao, Hui, Bradley, Jeffrey D., Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, and Yang, Xiaofeng

Subjects
Physics - Medical Physics
Abstract

Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment planning, because of its superior soft tissue contrast used for tumor and soft tissue delineation versus computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps required for proton radiotherapy dose calculation. To demonstrate the feasibility of MRI-only based mass density and RSP estimation using deep learning (DL) for proton radiotherapy. A DL-based framework was developed to discover underlying voxel-wise correlation between MR images and mass density and RSP. Five tissue substitute phantoms including skin, muscle, adipose, 45% hydroxyapatite (HA), and spongiosa bone were customized for MRI scanning based on material composition information from ICRP reports. Two animal tissue phantoms made of pig brain and liver were prepared for DL training. In the phantom study, two DL models were trained: one containing clinical T1 and T2 MRIs and another incorporating zero echo time (ZTE) MRIs as input. In the patient application study, two DL models were trained: one including T1 and T2 MRIs as input, and one incorporating synthetic dual-energy computed tomography (sDECT) images to provide bone tissue information. In the phantom study, DL model based on T1 and T2 MRI demonstrated higher accuracy mass density and RSP estimation in skin, muscle, adipose, brain, and liver with mean absolute percentage errors (MAPE) of 0.42%, 0.14%, 0.19%, 0.78% and 0.26% for mass density and 0.30%, 0.11%, 0.16%, 0.61% and 0.23% for RSP, respectively. DL model incorporating ZTE MRI improved the accuracy of mass density and RSP estimation in 45% HA and spongiosa bone with MAPE at 0.23% and 0.09% for mass density and 0.19% and 0.07% for RSP, respectively. Results show feasibility of MRI-only based mass density and RSP estimation for proton therapy treatment planning using DL method.

Published
2022

7. MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Author

Yuan Gao, Chih-Wei Chang, Sagar Mandava, Raanan Marants, Jessica E. Scholey, Matthew Goette, Yang Lei, Hui Mao, Jeffrey D. Bradley, Tian Liu, Jun Zhou, Atchar Sudhyadhom, and Xiaofeng Yang

Subjects
Medicine, Science
Abstract

Abstract Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Published
2024
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8. Multimodal Imaging-based Material Mass Density Estimation for Proton Therapy Using Physics-Constrained Deep Learning

Author

Chang, Chih-Wei, Marants, Raanan, Gao, Yuan, Goette, Matthew, Scholey, Jessica E., Bradley, Jeffrey D., Liu, Tian, Zhou, Jun, Sudhyadhom, Atchar, and Yang, Xiaofeng

Subjects
Physics - Medical Physics
Abstract

Mapping computed tomography (CT) number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, magnetic resonance imaging (MRI), and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. Seven tissue substitute MRI phantoms were used for PDMI-based material calibration. The training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold and tin filters. The feasibility investigation included an empirical DECT correlation and four residual networks (ResNet) derived from different training inputs and strategies by the PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet trained with and without a physics constraint using MRI and DECT images. PRN-DE and RN-DE represent ResNet trained with and without a physics constraint using DECT-only images. For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%, 2.62%, -3.58% for adipose; -0.03%, -0.61%, and -0.18% for muscle; -0.58%, -1.36%, and -4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The physics-constrained training can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the PDMI framework has the potential to improve proton range uncertainty with accurate patient mass density maps.

Published
2022

17. Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Author

CHIH-WEI CHANG, MARANTS, RAANAN, YUAN GAO, GOETTE, MATTHEW, SCHOLEY, JESSICA E., BRADLEY, JEFFREY D., TIAN LIU, JUN ZHOU, SUDHYADHOM, ATCHAR, and XIAOFENG YANG

Subjects
*DEEP learning, *PROTON therapy, *IMAGING phantoms, *CONSTRAINTS (Physics), *SUPERVISED learning, *MAGNETIC resonance imaging, *BONE density
Abstract

Objective Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. Methods: Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRNMR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. Results: For the tissue surrogate study, PRNMR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: −0.72%/2.62%/−3.58% for adipose; −0.03%/−0.61%/−0.18% for muscle; −0.58%/−1.36%/−4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. Conclusion The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. Advances in knowledge: PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Laboratory Courses on Laser Spectroscopy and Atom Trapping

Author

Hermina C. Beica, Shoshana Winter, Carson Mok, Brynle Barrett, Rob Berthiaume, Andrejs Vorozcovs, Fadi Yachoua, Nima Afkhami-Jeddi, Monika Aggarwal, Gehrig Carlse, Alex Pouliot, Kevin B. Borsos, Thomas Vacheresse, Raanan Marants, and A. Kumarakrishnan

Subjects
laser spectroscopy, atom trapping, advanced laboratory courses, applications of lasers, laser-cooling and trapping, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

We present an overview of experiments covered in two semester-length laboratory courses dedicated to laser spectroscopy and atom trapping. These courses constitute a powerful approach for teaching experimental physics in a manner that is both contemporary and capable of providing the background and skills relevant to a variety of research laboratories. The courses are designed to be accessible for all undergraduate streams in physics and applied physics as well as incoming graduate students. In the introductory course, students carry out several experiments in atomic and laser physics. In a follow up course, students trap atoms in a magneto-optical trap and carry out preliminary investigations of the properties of laser cooled atoms based on the expertise acquired in the first course. We discuss details of experiments, impact, possible course formats, budgetary requirements, and challenges related to long-term maintenance.

Published
2020
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21. Validation of prompt gamma-ray spectroscopy for proton range verification in tissue-mimicking and porcine samples

Author

Sebastian Tattenberg, Raanan Marants, Katharina Niepel, Thomas Bortfeld, Atchar Sudhyadhom, Guillaume Landry, Katia Parodi, and Joost Verburg

Subjects
Radiological and Ultrasound Technology, Phantoms, Imaging, Swine, Radiotherapy Planning, Computer-Assisted, Spectrum Analysis, Proton Therapy, Animals, Reproducibility of Results, Water, Radiology, Nuclear Medicine and imaging, Protons
Abstract

Objective. Proton therapy of cancer improves dose conformality to the target and sparing of surrounding healthy tissues compared to conventional photon treatments. However, proton therapy’s advantage could be even larger if proton range uncertainties were reduced. Sources of range uncertainties include computed tomography treatment planning images and variations in patient anatomy and setup. To reduce range uncertainties, we have developed a system for real-time in vivo range monitoring. The system is based on spectroscopy of prompt gamma-rays emitted through proton-nuclear interactions during irradiation. We validated the performance of our prompt gamma-ray spectroscopy detector prototype using tissue-mimicking and porcine samples. Approach. Measurements were performed in water, four tissue-mimicking samples (spongiosa, muscle, adipose tissue, and cortical bone), and two porcine samples (liver and brain). A dose of 0.9 Gy was delivered to a target at a depth of 12.5–17.5 cm. Multi-layer ionization chamber measurements were performed to determine stopping power ratios relative to water and ground truth proton ranges. Ground truth elemental compositions were determined using combustion analysis. Proton ranges and elemental compositions measured using prompt gamma-ray spectroscopy were compared to the ground truth. Main results. For all samples, the mean measured range over all pencil-beam spots differed from the ground truth by less than 1.2 mm. The mean standard deviation was 0.9 mm (range: 0.4–1.6 mm). The mean difference between ground truth and measured elemental compositions was 0.06 g cm 3 (range: 0.00 g cm 3 to 0.12 g cm 3 ). Significance. We verified the performance of our prompt gamma-ray spectroscopy detector prototype for proton range verification using tissue-mimicking and porcine samples. Measured proton ranges and elemental sample compositions were in good agreement with the ground truth. These measurements confirm the system’s reliability for a variety of tissues and bridge the gap between previously-reported experiments and ongoing in vivo patient measurements.

Published
2022

24. An Effective Approach for Teaching Laboratory Courses on Laser Spectroscopy and Atom Trapping

Author

Kumarakrishnan, A, primary, Pouliot, Alexander, primary, Beica, Hermina, primary, Carlse, Gehrig, primary, Winter, Shoshana, primary, Mok, Carson, primary, Barrett, Brynle, primary, Berthiaume, Robert, primary, Vorozcovs, Andrew, primary, Yachoua, Fadi, primary, Afkhami-Jeddi, Nima, primary, Aggarwal, Monika, primary, Borsos, Kevin, primary, Vacheresse, Thomas, primary, and Marants, Raanan, primary

Published
2022
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28. Datalignment

Author

Evgeny Marants, Daniel Deutch, and Yuval Moskovitch

Subjects
Containment (computer programming), Information retrieval, Association rule learning, Relation (database), Computer science, General Engineering, Ontology, Context (language use), computer, Ontology schema, Datalog, computer.programming_language, Focus (linguistics)
Abstract

We focus on the problem of aligning ontology relations, namely finding relation names that correspond to the same or related concepts. Such alignment is a prerequisite to the integration of the multiple available Knowledge Bases many of which include similar concepts, differently termed. We propose a novel approach for this problem, by leveraging association rules - originally mined in order to enrich the ontological content. Here, we treat the rules as Datalog programs and look for bounded-depth sub-programs that are contained in (or equivalent to) each other. Heads of such programs intuitively correspond to related concepts, and we propose them as candidates for alignment. The candidate alignments require further verification by experts; to this end we accompany each aligned pair with explanations based on the provenance of each relation according to its sub-program. We have implemented our novel solution in a system called Datalignment. We propose to demonstrate Datalignment, presenting the aligned pairs that it finds, and the computed explanations, in context of real-life Knowledge Bases.

Published
2019
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29. Renal Perfusion during Hemodialysis: Intradialytic Blood Flow Decline and Effects of Dialysate Cooling

Author

Ting-Yim Lee, Raanan Marants, Christopher W. McIntyre, Claire J. Grant, and Elena Qirjazi

Subjects
Adult, Male, medicine.medical_specialty, Time Factors, medicine.medical_treatment, 030232 urology & nephrology, Renal function, 030204 cardiovascular system & hematology, Kidney, Kidney Function Tests, Severity of Illness Index, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Renal Dialysis, Dialysis Solutions, Internal medicine, medicine, Humans, Computed Tomography Perfusion Imaging, Renal Insufficiency, Chronic, Dialysis, Aged, Retrospective Studies, Aged, 80 and over, Analysis of Variance, Myocardial stunning, Cross-Over Studies, Renal ischemia, business.industry, General Medicine, Middle Aged, medicine.disease, Cold Temperature, Perfusion, Regional Blood Flow, Nephrology, Circulatory system, Cardiology, Female, Hemodialysis, Tomography, X-Ray Computed, business, Follow-Up Studies
Abstract

BACKGROUND: Residual renal function (RRF) confers survival in patients with ESRD but declines after initiating hemodialysis. Previous research shows that dialysate cooling reduces hemodialysis-induced circulatory stress and protects the brain and heart from ischemic injury. Whether hemodialysis-induced circulatory stress affects renal perfusion, and if it can be ameliorated with dialysate cooling to potentially reduce RRF loss, is unknown. METHODS: We used renal computed tomography perfusion imaging to scan 29 patients undergoing continuous dialysis under standard (36.5°C dialysate temperature) conditions; we also scanned another 15 patients under both standard and cooled (35.0°C) conditions. Imaging was performed immediately before, 3 hours into, and 15 minutes after hemodialysis sessions. We used perfusion maps to quantify renal perfusion. To provide a reference to another organ vulnerable to hemodialysis-induced ischemic injury, we also used echocardiography to assess intradialytic myocardial stunning. RESULTS: During standard hemodialysis, renal perfusion decreased 18.4% (P

Published
2019
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30. Exploring the Link Between Hepatic Perfusion and Endotoxemia in Hemodialysis

Author

Raanan Marants, Elena Qirjazi, Ka-Bik Lai, Cheuk-Chun Szeto, Fiona Li, Philip Kam-Tao Li, Ting-Yim Lee, and Christopher W. McIntyre

Subjects
medicine.medical_specialty, medicine.medical_treatment, hepatic function, 030232 urology & nephrology, Hemodynamics, 030204 cardiovascular system & hematology, Hepatic function, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Clinical Research, Internal medicine, medicine, dialysate cooling, hepatic perfusion, Dialysis, hemodialysis, business.industry, endotoxemia, Crossover study, chemistry, Nephrology, Circulatory system, Cardiology, Hemodialysis, computed tomography perfusion, business, Perfusion, Indocyanine green
Abstract

Introduction The liver receives gut-derived endotoxin via the portal vein, clearing it before it enters systemic circulation. Hemodialysis negatively impacts the perfusion and function of multiple organs systems. Dialysate cooling reduces hemodialysis-induced circulatory stress and protects organs from ischemic injury. This study examined how hemodialysis disrupts liver hemodynamics and function, its effect on endotoxemia, and the potential protective effect of dialysate cooling. Methods Fifteen patients were randomized to receive either standard (36.5°C dialysate temperature) or cooled (35.0°C) hemodialysis first in a two-visit crossover trial. We applied computed tomography (CT) liver perfusion imaging to patients before, 3 hours into and after each hemodialysis session. We measured hepatic perfusion and perfusion heterogeneity. Hepatic function was measured by indocyanine green (ICG) clearance. Endotoxin levels in blood throughout dialysis were also measured. Results During hemodialysis, overall liver perfusion did not significantly change, but portal vein perfusion trended towards increasing (P = 0.14) and perfusion heterogeneity significantly increased (P = 0.038). In addition, ICG clearance decreased significantly during hemodialysis (P = 0.016), and endotoxin levels trended towards increasing during hemodialysis (P = 0.15) and increased significantly after hemodialysis (P = 0.037). Applying dialysate cooling trended towards abrogating these changes but did not reach statistical significance compared to standard hemodialysis. Conclusion Hemodialysis redistributes liver perfusion, attenuates hepatic function, and results in endotoxemia. Higher endotoxin levels in end-stage renal disease (ESRD) patients may result from the combination of decreased hepatic clearance function and increasing fraction of liver perfusion coming from toxin-laden portal vein during hemodialysis. The protective potential of dialysate cooling should be explored further in future research studies., Graphical abstract

Published
2020

31. Exploring the Effects of Hemodialysis on Renal and Hepatic Blood Flow and Function using CT Perfusion Imaging

Author

Marants, Raanan

Subjects
renal perfusion, glomerular filtration rate, hemodialysis, Medical Biophysics, computed tomography perfusion, hepatic perfusion, dialysate cooling
Abstract

Hemodialysis (HD) is the most common form of renal replacement therapy for end-stage renal disease. However, patients develop complications that are driven by HD-induced circulatory stress from rapidly removing large fluid volumes during HD, making various vascular beds vulnerable to ischemia. By assessing how HD-induced circulatory stress affects different organs, it may be possible to characterize the mechanisms behind these complications and evaluate therapeutic interventions. This thesis aims to explore how HD affects renal and hepatic blood flow and function using CT perfusion imaging. For this work, patients received either standard or cooled HD first in a two-visit, crossover study design, where imaging was performed before, during and after each HD session. Residual renal function is linked to improved clinical outcomes, yet characteristically declines upon HD initiation. In the first thesis project, we determined that renal perfusion deceases during HD, which could be an early manifestation of HD-mediated residual renal function loss. Although the liver normally clears endotoxin, increased circulating endotoxin levels have been found in HD patients. In the second thesis project, we showed that concurrent hepatic perfusion redistribution and decreased liver function during HD are likely responsible for increased circulating toxin levels. Dialysate cooling is a low-cost, feasible intervention that ameliorates HD-induced circulatory stress. In the first and second thesis projects, we found that cooling trended towards mitigating the drop in renal perfusion during HD and ameliorating the changes in liver perfusion and function during HD. If it were possible to accurately assess glomerular filtration rate (GFR) in HD patients, HD prescriptions could be adjusted in accordance with residual renal function to preserve remaining function. In the third thesis project, we extended the CT perfusion technique to measure GFR in HD patients, yielding physiologically realistic GFR values, thus demonstrating the feasibility of this approach in terms of reliability and accuracy. These findings help explain residual renal function loss and endotoxemia in HD patients, and showcases the protective potential of dialysate cooling. In addition, this work demonstrates the benefit of using CT perfusion as a functional imaging technique to further characterize and evaluate therapies for end-stage renal disease pathologies.

Published
2020

32. Laboratory Courses on Laser Spectroscopy and Atom Trapping

Author

Kevin B. Borsos, C. Mok, Shoshana Winter, Raanan Marants, Monika Aggarwal, A. Kumarakrishnan, Thomas Vacheresse, Gehrig Carlse, N. Afkhami-Jeddi, H. C. Beica, Brynle Barrett, Fadi Yachoua, A. Vorozcovs, A. Pouliot, and Rob Berthiaume

Subjects
Nuclear and High Energy Physics, Applied physics, applications of lasers, laser-cooling and trapping, Condensed Matter Physics, Laser, 01 natural sciences, Engineering physics, Atomic and Molecular Physics, and Optics, 010305 fluids & plasmas, law.invention, Trap (computing), Experimental physics, Graduate students, law, 0103 physical sciences, atom trapping, Atom trapping, ComputingMilieux_COMPUTERSANDEDUCATION, laser spectroscopy, lcsh:QC770-798, lcsh:Nuclear and particle physics. Atomic energy. Radioactivity, advanced laboratory courses, 010306 general physics, Spectroscopy
Abstract

We present an overview of experiments covered in two semester-length laboratory courses dedicated to laser spectroscopy and atom trapping. These courses constitute a powerful approach for teaching experimental physics in a manner that is both contemporary and capable of providing the background and skills relevant to a variety of research laboratories. The courses are designed to be accessible for all undergraduate streams in physics and applied physics as well as incoming graduate students. In the introductory course, students carry out several experiments in atomic and laser physics. In a follow up course, students trap atoms in a magneto-optical trap and carry out preliminary investigations of the properties of laser cooled atoms based on the expertise acquired in the first course. We discuss details of experiments, impact, possible course formats, budgetary requirements, and challenges related to long-term maintenance.

Published
2020

33. Evaluation of the 4D RADPOS dosimetry system for dose and position quality assurance of CyberKnife

Author

Joanna E. Cygler, Raanan Marants, and Eric Vandervoort

Subjects
Physics, business.industry, Dose profile, General Medicine, Imaging phantom, Standard deviation, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Cyberknife, 030220 oncology & carcinogenesis, Dosimetry, business, Fiducial marker, Radiation treatment planning, Quality assurance, Biomedical engineering
Abstract

Purpose: The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations. Methods: Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation. Results: For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5 mm for all directions. For the 10 mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2 mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8 mm for sinusoidal motion but increased to ≤2.6 mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system. Conclusions: RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

Published
2018
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34. Laboratory Courses on Laser Spectroscopy and Atom Trapping

Author

Beica, Hermina C., primary, Winter, Shoshana, additional, Mok, Carson, additional, Barrett, Brynle, additional, Berthiaume, Rob, additional, Vorozcovs, Andrejs, additional, Yachoua, Fadi, additional, Afkhami-Jeddi, Nima, additional, Aggarwal, Monika, additional, Carlse, Gehrig, additional, Pouliot, Alex, additional, Borsos, Kevin B., additional, Vacheresse, Thomas, additional, Marants, Raanan, additional, and Kumarakrishnan, A., additional

Published
2020
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35. CT Perfusion Techniques and Applications in Stroke and Cancer

Author

Raanan Marants, Dae Myoung Yang, Fiona Li, and Ting-Yim Lee

Subjects
medicine.medical_specialty, business.industry, medicine.medical_treatment, Radiation dose, Cancer, Perfusion scanning, Blood flow, Thrombolysis, medicine.disease, Functional imaging, medicine, Radiology, business, Acute ischemic stroke, Stroke
Abstract

CT perfusion is a functional imaging modality that has gained increasing use in imaging acute ischemic stroke patients to select patients for appropriate treatment with either thrombolysis or thrombectomy and in imaging cancer to confirm diagnosis and monitor progress of treatment. The advantages of CT perfusion are around-the-clock accessibility and easy implementation in most imaging departments. The discussion in this chapter will focus on four areas: (1) physiological models of contrast transport in tissue, (2) deconvolution techniques to derive functional parameters from the physiological models, (3) optimization of scanning protocols with respect to radiation dose and accuracy of derived functional parameters, and (4) application examples in stroke and cancer.

Published
2019
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37. Datalignment

Author

Deutch, Daniel, primary, Marants, Evgeny, additional, and Moskovitch, Yuval, additional

Published
2019
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39. Investigation into laser cladding of steel-titanium carbide powder mixtures on a steel substrate

Author

A. V. Marants, V. K. Narva, and Zh. A. Sentyurina

Subjects
Cladding (metalworking), Materials science, Titanium carbide, Scanning electron microscope, Metallurgy, Metals and Alloys, Radiation, Microstructure, Laser, Surfaces, Coatings and Films, Carbide, law.invention, chemistry.chemical_compound, chemistry, Mechanics of Materials, law, Powder mixture
Abstract

The influence of the main parameters of laser cladding (the laser radiation power, the scanning speed of the laser beam over the treated surface, the consumption of the cladding material (steel SPN14A7M5-titanium carbide (0–20 vol % TiC)), and titanium carbide content in the powder mixture) on geometric sizes (the height, width, and melting depth of the substrate) of the cladded single layer (bead) has been considered. The structure of the cladded material is investigated using optical and scanning electron microscopes, and the hardness distribution from the substrate to the cladded layer is determined.

Published
2014
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41. Metal Matrix Composite Material by Direct Metal Deposition

Author

Ph. Bertrand, D. Novichenko, L. Thivillon, Igor Smurov, and A. Marants

Subjects
Titanium carbide, Materials science, Carbotanium, Metal matrix composite, chemistry.chemical_element, titanium carbide, Physics and Astronomy(all), engineering.material, Microstructure, Carbide, rapid manufacturing, chemistry.chemical_compound, chemistry, Coating, laser cladding, engineering, direct metal deposition, metal matrix composite, Composite material, Powder mixture, Titanium
Abstract

Direct Metal Deposition (DMD) is a laser cladding process for producing a protective coating on the surface of a metallic part or manufacturing layer-by-layer parts in a single-step process. The objective of this work is to demonstrate the possibility to create carbide-reinforced metal matrix composite objects. Powders of steel 16NCD13 with different volume contents of titanium carbide are tested. On the base of statistical analysis, a laser cladding processing map is constructed. Relationships between the different content of titanium carbide in a powder mixture and the material microstructure are found. Mechanism of formation of various precipitated titanium carbides is investigated.

Published
2011
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42. Dose and Position Quality Assurance Using the RADPOS System for 4D Radiotherapy with CyberKnife

Author

Joanna E. Cygler, R Marants, and Eric Vandervoort

Subjects
Physics, Cyberknife, business.industry, Ionization chamber, Dose profile, Dosimetry, Radiation treatment planning, Fiducial marker, Respiratory compensation, Nuclear medicine, business, Imaging phantom
Abstract

The CyberKnife system consists of a compact LINAC mounted on a mobile robotic arm and the Synchrony Respiratory Motion Tracking System. This complex radiotherapy system needs independent performance verification to assure safe treatments. In this work, we use the RADPOS 4D dosimetry system to verify CyberKnife’s motion tracking and delivered dose. RADPOS motion measurements are compared with internal metal fiducial and external LED marker log files. Dose measurements are compared with film and treatment planning system (TPS) calculations. RADPOS and EBT3 Gaf- Chromic films were calibrated in Solid Water (5 cm depth, 80 cm source-detector distance, 60 mm cone, Exradin ion chamber, Fluke electrometer). A CT-based treatment plan was created for a Solid Water breast phantom containing fiducials and RADPOS. Dose calculations were performed using Multi- Plan TPS, Monte Carlo (MC) and ray tracing (RT) algorithms. Before treatment, film was inserted inside the breast phantom adjacent to RADPOS. The breast phantom and LED markers were positioned on the chest platform of a Quasar Motion Phantom. Position logging began for RADPOS and Synchrony, Quasar motion started, and irradiation commenced. A coordinate alignment algorithm was implemented, allowing position tracking modalities to be compared in a common coordinate system. The average standard deviation of the differences between LED and RADPOS position measurements was 0.33, 0.39, and 0.56 mm along the left/right, superior/inferior, and anterior/posterior directions, respectively. Dose percent difference values during static phantom irradiations were 0.3% (RADPOS/RT), 1.3% ( RADPOS/MC), 0.5% (Film/RT), 1.5% (Film/MC), and -0.2% (RADPOS/Film), while values during dynamic phantom irradiations were 2.0% (RADPOS/RT), 2.9% (RADPOS/MC), -1.0% (Film/RT), 0.0% (Film/MC), and 3.0% (RADPOS/Film). Average gamma results were greater than 96% for MC and RT dose calculation algorithms, and for dynamic and static treatments. Our work demonstrates that RADPOS is a useful tool for independent QA of CyberKnife treatments with Synchrony respiratory compensation.

Published
2015
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