Ting Gong, Merlin J. Fair, Kawin Setsompop, Hui Zhang
We present a microstructure imaging technique for estimating compartment-specific T2 and T2* simultaneously in the human brain. Microstructure imaging with diffusion MRI (dMRI) has enabled the modelling of intra-neurite and extra-neurite diffusion signals separately allowing for the estimation of compartment-specific tissue properties. These compartment-specific properties have been widely used in clinical studies. However, conventional dMRI cannot disentangle differences in relaxations between tissue compartments, causing biased estimates of diffusion measures which also change with TE. To solve the problem, combined relaxometry-diffusion imaging methods have been developed in recent years, providing compartmental T2-diffusion or T2*-diffusion imaging respectively, but not T2 and T2* together. As they provide complementary information, a technique that can estimate both jointly with diffusion is appealing to neuroimaging studies. The aim of this work is to develop a method to map compartmental T2-T2*-diffusion simultaneously. Using an advanced MRI acquisition called diffusion-PEPTIDE, a novel microstructure model is proposed and a multi-step fitting method is developed to estimate parameters of interest. We demonstrate for the first time that compartmental T2, T2* can be estimated simultaneously from in vivo data. we further show the accuracy and precision of parameter estimation with simulation.
{"title":"Compartment-specific estimation of T2 and T2* with diffusion-PEPTIDE MRI","authors":"Ting Gong, Merlin J. Fair, Kawin Setsompop, Hui Zhang","doi":"arxiv-2408.10432","DOIUrl":"https://doi.org/arxiv-2408.10432","url":null,"abstract":"We present a microstructure imaging technique for estimating\u0000compartment-specific T2 and T2* simultaneously in the human brain.\u0000Microstructure imaging with diffusion MRI (dMRI) has enabled the modelling of\u0000intra-neurite and extra-neurite diffusion signals separately allowing for the\u0000estimation of compartment-specific tissue properties. These\u0000compartment-specific properties have been widely used in clinical studies.\u0000However, conventional dMRI cannot disentangle differences in relaxations\u0000between tissue compartments, causing biased estimates of diffusion measures\u0000which also change with TE. To solve the problem, combined relaxometry-diffusion\u0000imaging methods have been developed in recent years, providing compartmental\u0000T2-diffusion or T2*-diffusion imaging respectively, but not T2 and T2*\u0000together. As they provide complementary information, a technique that can\u0000estimate both jointly with diffusion is appealing to neuroimaging studies. The\u0000aim of this work is to develop a method to map compartmental T2-T2*-diffusion\u0000simultaneously. Using an advanced MRI acquisition called diffusion-PEPTIDE, a\u0000novel microstructure model is proposed and a multi-step fitting method is\u0000developed to estimate parameters of interest. We demonstrate for the first time\u0000that compartmental T2, T2* can be estimated simultaneously from in vivo data.\u0000we further show the accuracy and precision of parameter estimation with\u0000simulation.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Muntasir Rahman, Aysha Mann, Amirtaha Taebi
Seismocardiogram (SCG) signals can play a crucial role in remote cardiac monitoring, capturing important events such as aortic valve opening (AO) and mitral valve closure (MC). However, existing SCG methods for detecting AO and MC typically rely on electrocardiogram (ECG) data. In this study, we propose an innovative approach to identify AO and MC events in SCG signals without the need for ECG information. Our method utilized a template bank, which consists of signal templates extracted from SCG waveforms of 5 healthy subjects. These templates represent characteristic features of a heart cycle. When analyzing new, unseen SCG signals from another group of 6 healthy subjects, we employ these templates to accurately detect cardiac cycles and subsequently pinpoint AO and MC events. Our results demonstrate the effectiveness of the proposed template bank approach in achieving ECG-independent AO and MC detection, laying the groundwork for more convenient remote cardiovascular assessment.
地震心动图（SCG）信号可在远程心脏监测中发挥重要作用，捕捉主动脉瓣开放（AO）和半月瓣关闭（MC）等重要事件。然而，用于检测 AO 和 MC 的现有 SCG 方法通常依赖于心电图（ECG）数据。在这项研究中，我们提出了一种创新方法，无需心电图信息即可识别 SCG 信号中的 AO 和 MC 事件。我们的方法利用了一个模板库，该模板库由从 5 名健康受试者的 SCG 波形中提取的信号模板组成。这些模板代表了心动周期的特征。在分析来自另一组 6 名健康受试者的新的、未见过的 SCG 信号时，我们利用这些模板来准确检测心动周期，并随后精确定位 AO 和 MC 事件。我们的研究结果证明了所提出的模板库方法在实现独立于心电图的 AO 和 MC 检测方面的有效性，为更方便的远程心血管评估奠定了基础。
{"title":"ECG-Free Assessment of Cardiac Valve Events Using Seismocardiography","authors":"Mohammad Muntasir Rahman, Aysha Mann, Amirtaha Taebi","doi":"arxiv-2408.09513","DOIUrl":"https://doi.org/arxiv-2408.09513","url":null,"abstract":"Seismocardiogram (SCG) signals can play a crucial role in remote cardiac\u0000monitoring, capturing important events such as aortic valve opening (AO) and\u0000mitral valve closure (MC). However, existing SCG methods for detecting AO and\u0000MC typically rely on electrocardiogram (ECG) data. In this study, we propose an\u0000innovative approach to identify AO and MC events in SCG signals without the\u0000need for ECG information. Our method utilized a template bank, which consists\u0000of signal templates extracted from SCG waveforms of 5 healthy subjects. These\u0000templates represent characteristic features of a heart cycle. When analyzing\u0000new, unseen SCG signals from another group of 6 healthy subjects, we employ\u0000these templates to accurately detect cardiac cycles and subsequently pinpoint\u0000AO and MC events. Our results demonstrate the effectiveness of the proposed\u0000template bank approach in achieving ECG-independent AO and MC detection, laying\u0000the groundwork for more convenient remote cardiovascular assessment.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In tissue engineering, we seek to address comprehensive tissue repair and regeneration needs. Aligned nanofibers have emerged as powerful and versatile tools, attributable to their structural and biochemical congruence with the natural extracellular matrix (ECM). This review delineates the contemporary applications of aligned nanofibers in tissue engineering, spotlighting their implementation across musculoskeletal, neural, and cardiovascular tissue domains. The influence of fiber alignment on critical cellular behaviors - cell adhesion, migration, orientation, and differentiation - is reviewed. We also discuss how nanofibers are improved by adding growth factors, peptides, and drugs to help tissues regenerate better. Comprehensive analyses of in vivo trials and clinical studies corroborate the efficacy and safety of these fibers in tissue engineering applications. The review culminates with exploring extant challenges, concurrently charting prospective avenues in aligned nanofiber-centric tissue engineering.
{"title":"Applications of aligned nanofiber for tissue engineering","authors":"Gayatri Patel, Louis-S. Bouchard","doi":"arxiv-2408.07909","DOIUrl":"https://doi.org/arxiv-2408.07909","url":null,"abstract":"In tissue engineering, we seek to address comprehensive tissue repair and\u0000regeneration needs. Aligned nanofibers have emerged as powerful and versatile\u0000tools, attributable to their structural and biochemical congruence with the\u0000natural extracellular matrix (ECM). This review delineates the contemporary\u0000applications of aligned nanofibers in tissue engineering, spotlighting their\u0000implementation across musculoskeletal, neural, and cardiovascular tissue\u0000domains. The influence of fiber alignment on critical cellular behaviors - cell\u0000adhesion, migration, orientation, and differentiation - is reviewed. We also\u0000discuss how nanofibers are improved by adding growth factors, peptides, and\u0000drugs to help tissues regenerate better. Comprehensive analyses of in vivo\u0000trials and clinical studies corroborate the efficacy and safety of these fibers\u0000in tissue engineering applications. The review culminates with exploring extant\u0000challenges, concurrently charting prospective avenues in aligned\u0000nanofiber-centric tissue engineering.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In previous publications, we have presented an alternative approach to determine essential detector properties like the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and the Detective Quantum Efficiency (DQE) based on a Monte Carlo model of the detection process. If a Monte Carlo model for the detector response to photons impinging at various locations of a pixel is available, the full statistics of the detector can be derived in a straightforward manner. The purpose of this paper is to describe the method in detail and to apply it to four types of realistic detectors: direct converting detectors using CdTe and silicon, a CdTe photon counter with additional coincidence counters and an optical counting system using LaBr3 as scintillator.
{"title":"A Monte Carlo assessment of the spectral performance of four types of photon counting detectors","authors":"Karl Stierstorfer, Martin Hupfer","doi":"arxiv-2408.07538","DOIUrl":"https://doi.org/arxiv-2408.07538","url":null,"abstract":"In previous publications, we have presented an alternative approach to\u0000determine essential detector properties like the Modulation Transfer Function\u0000(MTF), the Noise Power Spectrum (NPS) and the Detective Quantum Efficiency\u0000(DQE) based on a Monte Carlo model of the detection process. If a Monte Carlo\u0000model for the detector response to photons impinging at various locations of a\u0000pixel is available, the full statistics of the detector can be derived in a\u0000straightforward manner. The purpose of this paper is to describe the method in\u0000detail and to apply it to four types of realistic detectors: direct converting\u0000detectors using CdTe and silicon, a CdTe photon counter with additional\u0000coincidence counters and an optical counting system using LaBr3 as\u0000scintillator.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jacquelline Nyakunu, Christopher T. Piatnichouk, Henry C. Russell, Niels J. van Duijnhoven, Benjamin E. Levy
Objective. Magnetomotive ultrasound (MMUS) using magnetic nanoparticle contrast agents has shown promise for thrombosis imaging and quantitative elastometry via magnetomotive resonant acoustic spectroscopy (MRAS). Young's modulus measurements of smaller, stiffer thrombi require an MRAS system capable of generating forces at higher temporal frequencies. Solenoids with fewer turns, and thus less inductance, could improve high frequency performance, but the reduced force may compromise results. In this work, a computational model capable of predicting improved MRAS magnet configurations optimized for elastometry is presented and validated. Approach. Finite element analysis (FEA) was used to model the force and inductance of MRAS systems. The simulations incorporated both solenoid electromagnets and permanent magnets in three-dimensional steady-state, frequency domain, and time domain studies. Main results. The model successfully predicted a configuration in which permanent magnets could be used to increase the force supplied by an existing MRAS system. Accordingly, the displacement measured in a magnetically labeled validation phantom increased by a factor of $2.2 pm 0.3$ when the force was predicted to increase by a factor of $2.2 pm 0.2$. The model additionally identified a new solenoid configuration consisting of four smaller coils capable of providing sufficient force at higher driving frequencies. Significance. These results indicate two methods by which MRAS systems could be designed to deliver higher frequency magnetic forces without the need for experimental trial and error. Either the number of turns within each solenoid could be reduced while permanent magnets are added at precise locations, or a larger number of smaller solenoids could be used. These findings overcome a key challenge toward the goal of thrombosis elastometry via MMUS.
{"title":"A Finite Element Analysis Model for Magnetomotive Ultrasound Elastometry Magnet Design with Experimental Validation","authors":"Jacquelline Nyakunu, Christopher T. Piatnichouk, Henry C. Russell, Niels J. van Duijnhoven, Benjamin E. Levy","doi":"arxiv-2408.07737","DOIUrl":"https://doi.org/arxiv-2408.07737","url":null,"abstract":"Objective. Magnetomotive ultrasound (MMUS) using magnetic nanoparticle\u0000contrast agents has shown promise for thrombosis imaging and quantitative\u0000elastometry via magnetomotive resonant acoustic spectroscopy (MRAS). Young's\u0000modulus measurements of smaller, stiffer thrombi require an MRAS system capable\u0000of generating forces at higher temporal frequencies. Solenoids with fewer\u0000turns, and thus less inductance, could improve high frequency performance, but\u0000the reduced force may compromise results. In this work, a computational model\u0000capable of predicting improved MRAS magnet configurations optimized for\u0000elastometry is presented and validated. Approach. Finite element analysis (FEA) was used to model the force and\u0000inductance of MRAS systems. The simulations incorporated both solenoid\u0000electromagnets and permanent magnets in three-dimensional steady-state,\u0000frequency domain, and time domain studies. Main results. The model successfully predicted a configuration in which\u0000permanent magnets could be used to increase the force supplied by an existing\u0000MRAS system. Accordingly, the displacement measured in a magnetically labeled\u0000validation phantom increased by a factor of $2.2 pm 0.3$ when the force was\u0000predicted to increase by a factor of $2.2 pm 0.2$. The model additionally\u0000identified a new solenoid configuration consisting of four smaller coils\u0000capable of providing sufficient force at higher driving frequencies. Significance. These results indicate two methods by which MRAS systems could\u0000be designed to deliver higher frequency magnetic forces without the need for\u0000experimental trial and error. Either the number of turns within each solenoid\u0000could be reduced while permanent magnets are added at precise locations, or a\u0000larger number of smaller solenoids could be used. These findings overcome a key\u0000challenge toward the goal of thrombosis elastometry via MMUS.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metal artifacts in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. The presence of high-density metallic implants results in artifacts that deteriorate image quality, manifesting in the forms of streaking, blurring, or beam hardening effects, etc. Nowadays, various deep learning-based approaches, particularly generative models, have been proposed for metal artifact reduction (MAR). However, these methods have limited perception ability in the diverse morphologies of different metal implants with artifacts, which may generate spurious anatomical structures and exhibit inferior generalization capability. To address the issues, we leverage visual-language model (VLM) to identify these morphological features and introduce them into a dual-domain CLIP-assisted residual optimization perception model (DuDoCROP) for MAR. Specifically, a dual-domain CLIP (DuDoCLIP) is fine-tuned on the image domain and sinogram domain using contrastive learning to extract semantic descriptions from anatomical structures and metal artifacts. Subsequently, a diffusion model is guided by the embeddings of DuDoCLIP, thereby enabling the dual-domain prior generation. Additionally, we design prompt engineering for more precise image-text descriptions that can enhance the model's perception capability. Then, a downstream task is devised for the one-step residual optimization and integration of dual-domain priors, while incorporating raw data fidelity. Ultimately, a new perceptual indicator is proposed to validate the model's perception and generation performance. With the assistance of DuDoCLIP, our DuDoCROP exhibits at least 63.7% higher generalization capability compared to the baseline model. Numerical experiments demonstrate that the proposed method can generate more realistic image structures and outperform other SOTA approaches both qualitatively and quantitatively.
计算机断层扫描（CT）成像中的金属伪影给准确的临床诊断带来了巨大挑战。高密度金属植入物的存在会导致伪影，从而降低图像质量，表现为条纹、模糊或光束硬化效应等形式。目前，已有多种基于深度学习的方法，特别是生成模型，被提出用于减少金属伪影（MAR）。然而，这些方法对不同金属植入物的不同形态与伪影的感知能力有限，可能会产生虚假的解剖结构，并表现出较低的泛化能力。为了解决这些问题，我们利用视觉语言模型（VLM）来识别这些形态特征，并将其引入用于 MAR 的双域 CLIP 辅助残余优化感知模型（DuDoCROP）中。具体来说，利用对比学习技术对图像域和正弦波域的双域CLIP（DuDoCLIP）进行微调，以从解剖结构和金属伪影中提取语义描述。此外，我们还为更精确的图像文本描述设计了提示工程，以增强模型的感知能力。最后，我们提出了一个新的感知指标来验证模型的感知和生成性能。在 DuDoCLIP 的帮助下，我们的 DuDoCROP 与基线模型相比至少提高了 63.7% 的泛化能力。数值实验证明，所提出的方法可以生成更逼真的图像结构，在质量和数量上都优于其他 SOTA 方法。
{"title":"DuDoCROP: Dual-Domain CLIP-Assisted Residual Optimization Perception Model for CT Metal Artifact Reduction","authors":"Xinrui Zhang, Ailong Cai, Lei Li, Bin Yan","doi":"arxiv-2408.14342","DOIUrl":"https://doi.org/arxiv-2408.14342","url":null,"abstract":"Metal artifacts in computed tomography (CT) imaging pose significant\u0000challenges to accurate clinical diagnosis. The presence of high-density\u0000metallic implants results in artifacts that deteriorate image quality,\u0000manifesting in the forms of streaking, blurring, or beam hardening effects,\u0000etc. Nowadays, various deep learning-based approaches, particularly generative\u0000models, have been proposed for metal artifact reduction (MAR). However, these\u0000methods have limited perception ability in the diverse morphologies of\u0000different metal implants with artifacts, which may generate spurious anatomical\u0000structures and exhibit inferior generalization capability. To address the\u0000issues, we leverage visual-language model (VLM) to identify these morphological\u0000features and introduce them into a dual-domain CLIP-assisted residual\u0000optimization perception model (DuDoCROP) for MAR. Specifically, a dual-domain\u0000CLIP (DuDoCLIP) is fine-tuned on the image domain and sinogram domain using\u0000contrastive learning to extract semantic descriptions from anatomical\u0000structures and metal artifacts. Subsequently, a diffusion model is guided by\u0000the embeddings of DuDoCLIP, thereby enabling the dual-domain prior generation.\u0000Additionally, we design prompt engineering for more precise image-text\u0000descriptions that can enhance the model's perception capability. Then, a\u0000downstream task is devised for the one-step residual optimization and\u0000integration of dual-domain priors, while incorporating raw data fidelity.\u0000Ultimately, a new perceptual indicator is proposed to validate the model's\u0000perception and generation performance. With the assistance of DuDoCLIP, our\u0000DuDoCROP exhibits at least 63.7% higher generalization capability compared to\u0000the baseline model. Numerical experiments demonstrate that the proposed method\u0000can generate more realistic image structures and outperform other SOTA\u0000approaches both qualitatively and quantitatively.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with existing high-end DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequentially-scanning data acquisition scheme to implement DECT may make broader impact on clinical practice because this scheme requires no specialized hardware designs. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially-scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material decomposition approaches for DECT assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification accuracy of iodine concentration. In this work, we developed a technique to achieve sequentially-scanning DECT imaging using high temporal resolution image reconstruction and temporal extrapolation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially-scanned data sets and improve iodine quantification accuracy in sequentially-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams. Results demonstrated the improvement of iodine quantification accuracy using ACCELERATION.
{"title":"ACCELERATION: Sequentially-scanning DECT Imaging Using High Temporal Resolution Image Reconstruction And Temporal Extrapolation","authors":"Qiaoxin Li, Dong Liang, Yinsheng Li","doi":"arxiv-2408.06163","DOIUrl":"https://doi.org/arxiv-2408.06163","url":null,"abstract":"Dual-energy computed tomography (DECT) has been widely used to obtain\u0000quantitative elemental composition of imaged subjects for personalized and\u0000precise medical diagnosis. Compared with existing high-end DECT leveraging\u0000advanced X-ray source and/or detector technologies, the use of the\u0000sequentially-scanning data acquisition scheme to implement DECT may make\u0000broader impact on clinical practice because this scheme requires no specialized\u0000hardware designs. However, since the concentration of iodinated contrast agent\u0000in the imaged subject varies over time, sequentially-scanned data sets acquired\u0000at two tube potentials are temporally inconsistent. As existing material\u0000decomposition approaches for DECT assume that the data sets acquired at two\u0000tube potentials are temporally consistent, the violation of this assumption\u0000results in inaccurate quantification accuracy of iodine concentration. In this\u0000work, we developed a technique to achieve sequentially-scanning DECT imaging\u0000using high temporal resolution image reconstruction and temporal extrapolation,\u0000ACCELERATION in short, to address the technical challenge induced by temporal\u0000inconsistency of sequentially-scanned data sets and improve iodine\u0000quantification accuracy in sequentially-scanning DECT. ACCELERATION has been\u0000validated and evaluated using numerical simulation data sets generated from\u0000clinical human subject exams. Results demonstrated the improvement of iodine\u0000quantification accuracy using ACCELERATION.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sara Neves Silva, Tomas Woodgate, Sarah McElroy, Michela Cleri, Kamilah St Clair, Jordina Aviles Verdera, Kelly Payette, Alena Uus, Lisa Story, David Lloyd, Mary A Rutherford, Joseph V Hajnal, Kuberan Pushparajah, Jana Hutter
Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, were trained on 167 and 71 fetal datasets across field strengths, acquisition parameters, and gestational ages and implemented in a real-time setup. Next, a phase-contrast sequence was modified to use the identified landmarks for planning. The OWL pipeline was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (gestational ages between 36+3 and 39+3 weeks). The prospective cases were additionally manually planned to enable direct comparison both qualitatively, by scoring the planning quality, and quantitatively, by comparing the indexed flow measurements. OWL enabled real-time fully automatic planning of the 2D phase-contrast scans in all but one of the prospective participants. The fetal body localization achieved an overall Dice score of 0.94+-0.05 and the cardiac landmark detection accuracy was 5.77+-2.91 mm for the descending aorta, 4.32+-2.44 mm for the spine, and 4.94+-3.82 mm for the umbilical vein. For the prospective cases, overall planning quality was 2.73/4 for the automated scans, compared to 3.0/4 for manual planning, and the flow quantitative evaluation showed a mean difference of -1.8% (range -14.2% to 14.9%) by comparing the indexed flow measurements obtained from gated automatic and manual acquisitions. Real-time automated planning of 2D phase-contrast MRI was effectively accomplished for 2 major vessels of the fetal vasculature. While demonstrated here on 0.55T, the achieved method has wider implications, and training across multiple field strengths enables generalization. OWL thereby presents an important step towards extending access to this modality beyond specialised centres.
{"title":"AutOmatic floW planning for fetaL MRI (OWL)","authors":"Sara Neves Silva, Tomas Woodgate, Sarah McElroy, Michela Cleri, Kamilah St Clair, Jordina Aviles Verdera, Kelly Payette, Alena Uus, Lisa Story, David Lloyd, Mary A Rutherford, Joseph V Hajnal, Kuberan Pushparajah, Jana Hutter","doi":"arxiv-2408.06326","DOIUrl":"https://doi.org/arxiv-2408.06326","url":null,"abstract":"Two subsequent deep learning networks, one localizing the fetal chest and one\u0000identifying a set of landmarks on a coronal whole-uterus balanced steady-state\u0000free precession scan, were trained on 167 and 71 fetal datasets across field\u0000strengths, acquisition parameters, and gestational ages and implemented in a\u0000real-time setup. Next, a phase-contrast sequence was modified to use the\u0000identified landmarks for planning. The OWL pipeline was evaluated\u0000retrospectively in 10 datasets and prospectively in 7 fetal subjects\u0000(gestational ages between 36+3 and 39+3 weeks). The prospective cases were\u0000additionally manually planned to enable direct comparison both qualitatively,\u0000by scoring the planning quality, and quantitatively, by comparing the indexed\u0000flow measurements. OWL enabled real-time fully automatic planning of the 2D\u0000phase-contrast scans in all but one of the prospective participants. The fetal\u0000body localization achieved an overall Dice score of 0.94+-0.05 and the cardiac\u0000landmark detection accuracy was 5.77+-2.91 mm for the descending aorta,\u00004.32+-2.44 mm for the spine, and 4.94+-3.82 mm for the umbilical vein. For the\u0000prospective cases, overall planning quality was 2.73/4 for the automated scans,\u0000compared to 3.0/4 for manual planning, and the flow quantitative evaluation\u0000showed a mean difference of -1.8% (range -14.2% to 14.9%) by comparing the\u0000indexed flow measurements obtained from gated automatic and manual\u0000acquisitions. Real-time automated planning of 2D phase-contrast MRI was\u0000effectively accomplished for 2 major vessels of the fetal vasculature. While\u0000demonstrated here on 0.55T, the achieved method has wider implications, and\u0000training across multiple field strengths enables generalization. OWL thereby\u0000presents an important step towards extending access to this modality beyond\u0000specialised centres.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The parametric optimization for the ultrasound computed tomography system is introduced. It is hypothesized that the pulse characteristic directly affects the information present in the reconstructed profile. The ultrasound excitation modes based on pulse-width modifications are studied to estimate the effect on reconstruction quality. Studies show that the pulse width affects the response of the transducer and, thus, the reconstruction. The ultrasound scanning parameters, mainly pulse width, are assessed and optimally set by an Artificial Intelligence driven process, according to the object without the requirement of a-priori information. The optimization study uses a novel intelligent object placement procedure to ensure repeatability of the same region of interest, a key requirement to minimize the error. Further, Kanpur Theorem 1 is implemented to evaluate the quality of the acquired projection data and discard inferior quality data. Scanning results corresponding to homogeneous and heterogeneous phantoms are presented. The image processing step involves deep learning model evaluating the dice coefficient for estimating the reconstruction quality if prior information about the inner profile is known or a classical error estimate otherwise. The models segmentation accuracy is 95.72 percentage and intersection over union score is 0.8842 on the validation dataset. The article also provides valuable insights about the development and low-level control of the system.
介绍了超声波计算机断层扫描系统的参数优化。假设脉冲特性会直接影响重建轮廓中的信息。研究了基于脉宽修正的超声激励模式，以估计其对重建质量的影响。研究表明，脉冲宽度会影响换能器的响应，从而影响重建效果。超声波扫描参数，主要是脉冲宽度，是由人工智能驱动的过程根据对象进行评估和优化设置的，不需要先验信息。优化研究采用了一种新颖的智能对象置放程序，以确保同一感兴趣区的可重复性，这是误差最小化的关键要求。此外，还采用了坎普尔定理 1 来评估所获取投影数据的质量，并舍弃劣质数据。图中展示了与同质和异质病象相对应的扫描结果。图像处理步骤包括深度学习模式评估骰子系数，以便在已知内部轮廓信息的情况下估算重建质量，或在其他情况下进行经典错误估算。在验证数据集上，模型的分割准确率为 95.72%，intersection over union 分数为 0.8842。文章还就系统的开发和底层控制提供了有价值的见解。
{"title":"Pulse excitation mode selection via AI Pipeline to Fully Automate the WUCT System","authors":"Ankur Kumar, Mayank Goswami","doi":"arxiv-2408.05401","DOIUrl":"https://doi.org/arxiv-2408.05401","url":null,"abstract":"The parametric optimization for the ultrasound computed tomography system is\u0000introduced. It is hypothesized that the pulse characteristic directly affects\u0000the information present in the reconstructed profile. The ultrasound excitation\u0000modes based on pulse-width modifications are studied to estimate the effect on\u0000reconstruction quality. Studies show that the pulse width affects the response\u0000of the transducer and, thus, the reconstruction. The ultrasound scanning\u0000parameters, mainly pulse width, are assessed and optimally set by an Artificial\u0000Intelligence driven process, according to the object without the requirement of\u0000a-priori information. The optimization study uses a novel intelligent object\u0000placement procedure to ensure repeatability of the same region of interest, a\u0000key requirement to minimize the error. Further, Kanpur Theorem 1 is implemented\u0000to evaluate the quality of the acquired projection data and discard inferior\u0000quality data. Scanning results corresponding to homogeneous and heterogeneous\u0000phantoms are presented. The image processing step involves deep learning model\u0000evaluating the dice coefficient for estimating the reconstruction quality if\u0000prior information about the inner profile is known or a classical error\u0000estimate otherwise. The models segmentation accuracy is 95.72 percentage and\u0000intersection over union score is 0.8842 on the validation dataset. The article\u0000also provides valuable insights about the development and low-level control of\u0000the system.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daphna Raz, Varun Joshi, Brian Umberger, Necmiye Ozay
Humans rely on ankle torque to maintain standing balance, particularly in the presence of small to moderate perturbations. Reductions in maximum torque (MT) production and maximum rate of torque development (MRTD) occur at the ankle during aging, diminishing stability. Ankle exoskeletons are powered orthotic devices that may assist older adults by compensating for reduced muscle force and power capabilities. They may also be able to assist with ankle strategies used for balance. However, no studies have investigated their effect on balance in older adults. Here, we model the effect these devices have on stability in physics-based models of healthy young and old adults, focusing on age-related deficits such as reduced MT and MRTD. We show that an ankle exoskeleton moderately reduces feasible stability boundaries in users who have full ankle strength. For individuals with age-related deficits, there is a trade-off. While exoskeletons augment stability in portions of the phase plane, they reduce stability in others. Our results suggest that well-established control strategies must still be experimentally validated in older adults.
{"title":"Ankle Exoskeletons May Hinder Standing Balance in Simple Models of Older and Younger Adults","authors":"Daphna Raz, Varun Joshi, Brian Umberger, Necmiye Ozay","doi":"arxiv-2408.05418","DOIUrl":"https://doi.org/arxiv-2408.05418","url":null,"abstract":"Humans rely on ankle torque to maintain standing balance, particularly in the\u0000presence of small to moderate perturbations. Reductions in maximum torque (MT)\u0000production and maximum rate of torque development (MRTD) occur at the ankle\u0000during aging, diminishing stability. Ankle exoskeletons are powered orthotic\u0000devices that may assist older adults by compensating for reduced muscle force\u0000and power capabilities. They may also be able to assist with ankle strategies\u0000used for balance. However, no studies have investigated their effect on balance\u0000in older adults. Here, we model the effect these devices have on stability in\u0000physics-based models of healthy young and old adults, focusing on age-related\u0000deficits such as reduced MT and MRTD. We show that an ankle exoskeleton\u0000moderately reduces feasible stability boundaries in users who have full ankle\u0000strength. For individuals with age-related deficits, there is a trade-off.\u0000While exoskeletons augment stability in portions of the phase plane, they\u0000reduce stability in others. Our results suggest that well-established control\u0000strategies must still be experimentally validated in older adults.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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