Physics in medicine and biology最新文献

{"title":"One scan, many stories: deep learning for signal separation in multi-tracer PET imaging.","authors":"Luigi Manco, Luca Urso, Luca Filippi","doi":"10.1088/1361-6560/ae02db","DOIUrl":"10.1088/1361-6560/ae02db","url":null,"abstract":"","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insertion of hepatic lesions into clinical photon-counting-detector CT projection data.","authors":"Hao Gong, Shravani Kharat, Jarod Wellinghoff, Ahmed Omar El Sadaney O Rabie, Joel G Fletcher, Shaojie Chang, Lifeng Yu, Shuai Leng, Cynthia H McCollough","doi":"10.1088/1361-6560/ae0975","DOIUrl":"10.1088/1361-6560/ae0975","url":null,"abstract":"<p><p><i>Objective.</i>To facilitate task-driven image quality assessment of lesion detectability in clinical photon-counting-detector CT (PCD-CT), it is desired to have patient image data with known pathology and precise annotation. Standard patient case collection and reference standard establishment are time- and resource-intensive. To mitigate this challenge, we aimed to develop a projection-domain lesion insertion framework that efficiently creates realistic patient cases by digitally inserting real radiopathologic features into patient PCD-CT images.<i>Approach.</i>This framework used an artificial-intelligence-assisted semi-automatic annotation to generate digital lesion models from real lesion images. The x-ray energy for commercial beam-hardening correction in PCD-CT system was estimated and used for calculating multi-energy forward projections of these lesion models at different energy thresholds. Lesion projections were subsequently added to patient projections from PCD-CT exams. The modified projections were reconstructed to form realistic lesion-present patient images, using the CT manufacturer's offline reconstruction software. Image quality was qualitatively and quantitatively validated in phantom scans and patient cases with liver lesions, using visual inspection, CT number accuracy, structural similarity index (SSIM), and radiomic feature analysis. Statistical tests were performed using Wilcoxon signed rank test.<i>Main results.</i>No statistically significant discrepancy (<i>p</i>> 0.05) of CT numbers was observed between original and re-inserted tissue- and contrast-media-mimicking rods and hepatic lesions (mean ± standard deviation): rods 0.4 ± 2.3 HU, lesions -1.8 ± 6.4 HU. The original and inserted lesions showed similar morphological features at original and re-inserted locations: mean ± standard deviation of SSIM 0.95 ± 0.02. Additionally, the corresponding radiomic features presented highly similar feature clusters with no statistically significant differences (<i>p</i>> 0.05).<i>Significance.</i>The proposed framework can generate patient PCD-CT exams with realistic liver lesions using archived patient data and lesion images. It will facilitate systematic evaluation of PCD-CT systems and advanced reconstruction and post-processing algorithms with target pathological features.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated computation of detectability index and generation of contrast-detail curves for CT protocol optimization.","authors":"Choirul Anam, Ariij Naufal, Heri Sutanto, Kusworo Adi, Chai Hong Yeong, Geoff Dougherty","doi":"10.1088/1361-6560/ae0ab0","DOIUrl":"10.1088/1361-6560/ae0ab0","url":null,"abstract":"<p><p><i>Objective.</i>The aim of this study was to develop an automatic method for generating a detectability index (<i>d'</i>)-based contrast-detail (<i>C</i>-<i>D</i>) curve across multiple object sizes and contrasts, and to evaluate its performance under varying tube current settings and reconstruction filter types.<i>Approach.</i>To compute<i>d'</i>for a given object size and contrast, the task-transfer function and noise power spectrum were obtained from ACR 464 computed tomography (CT) phantom images acquired at tube currents of 80, 120, 160 and 200 mA, using Edge, Lung, and Soft filter types. The task objects were varied in size (1-15 mm) and contrast levels (1-15 HU) with both flat and Gaussian signal types. For each defined task object,<i>d'</i>was calculated using a non-prewhitening model observer. This process was iterated for every predefined task function across multiple object sizes and contrasts, resulting in a<i>d'</i>map corresponding to the synthetic low-contrast images. A<i>C</i>-<i>D</i>curve was then generated using a<i>d'</i>cut-off value defined by the user. For comparison, a separate<i>C</i>-<i>D</i>curve was generated based on visual assessment by five human observers (HOs).<i>Main results.</i>The automated method successfully computed<i>d'</i>values and arranged synthetic low-contrast images into a grid according to object size and contrast.<i>C</i>-<i>D</i>curves using<i>d'</i>cut-off values of 3 or 4 most closely reflected HOs performance. For tube current variations, increasing the current led to higher detectability. For filter type variations, the Lung filter resulted in relatively lower detectability compared to the Edge and Soft filters.<i>Significance</i>. An automated method to calculate<i>d'</i>across a wide range of object sizes and contrasts, and to generate a<i>d'</i>-based<i>C</i>-<i>D</i>curve for CT protocol optimization was developed. The results were consistent with HO trends and effectively captured detectability changes across different imaging parameters.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-enhanced 3D real-time photoacoustic imaging using experimental ground truths obtained from fluctuation imaging.","authors":"Ivana Falco, Godefroy Guillaume, Maxime Henry, Véronique Josserand, Emmanuel Bossy, Bastien Arnal","doi":"10.1088/1361-6560/ae0f70","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0f70","url":null,"abstract":"<p><p>3D conventional photoacoustic (PA) imaging often suffers from visibility artifacts caused by the limited bandwidth and constrained viewing angles of ultrasound transducers, as well as the use of sparse arrays. PA fluctuation imaging (PAFI), which leverages signal variations due to blood flow, compensates for these visibility artifacts at the cost of temporal resolution. Deep learning (DL)--based photoacoustic image enhancement has previously demonstrated strong potential for improved reconstruction at a high temporal resolution. However, generating an experimental training dataset remains problematic. &#xD;Herein, we propose creating an experimental training dataset based on single-shot 3D PA images (input) and corresponding PAFI images (ground truth) of chicken embryo vasculature, which is used to train a 3D ResU-Net neural network.&#xD;The trained DL-PAFI network predictions on new experimental test images reveal effective improvement in visibility and contrast. We observe, however, that the output image resolution is lower than that of PAFI. Importantly, incorporating only experimental data into training already yields a good performance, while pre-training with simulated examples improves the overall accuracy. Additionally, we demonstrate the feasibility of real-time rendering and present preliminary in vivo predictions in mice, generated by the network trained exclusively on chicken embryo vasculature. These findings suggest the potential for achieving real-time, artifact-free 3D PA imaging with sparse arrays, adaptable to various in vivo applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards distinguishing intra-canal and paraspinal cavitation activity during focused ultrasound exposures in the spine.","authors":"Andrew P Frizado, Meaghan Anne O'Reilly","doi":"10.1088/1361-6560/ae0f36","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0f36","url":null,"abstract":"<p><strong>Objective: </strong>Although less established than transcranial focused ultrasound (FUS), transvertebral FUS is being developed to treat spinal cord pathologies. Transvertebral sonication of the spinal cord for microbubble-mediated drug delivery generates cavitation at the target in the spinal canal, and outside the spinal canal due to reflection off the posterior surface of the spinal column. In these two regions, circulating microbubbles are excited by local foci to generate acoustic emissions that are used to monitor FUS treatments. When trying to localize acoustic emissions generated from cavitation in the spinal cord, prefocal cavitation emissions emanating from paraspinal regions can dwarf signals originating in the canal and compromising monitoring capabilities. This paper evaluates alternative reconstruction algorithms to delay-sum-and-integrate (DAS) in-silico and ex-vivo to more reliably map intra-spinal canal sources in the face of interference. &#xD;Approach: A proof-of-concept 400/800 kHz (transmit/receive) spine-specific array prototype was used to generate intracanal cavitation through intact human vertebrae and passively monitor the corresponding acoustic emissions. Delay-multiply-sum-and-integrate (DMAS) beamforming was compared to DAS in two different implementations, full array (DMAS) and half-array multiplicative compounding (DMASMu), in the modelled cavitation scenarios where paraspinal cavitation is present. &#xD;Main Results: Both DMAS and DMASMu improved image quality by reducing peak sidelobes and increasing image signal-to-noise ratio. Aberration corrections further improved image quality metrics and, when applied selectively to voxels co-registered to the canal, assisted localization when prefocal sources were present in silico. When localizing canal sources in the presence of paraspinal cavitation, a switch to DMAS/DMASMu offered a more consistent localization rate in silico and ex vivo, though ex vivo phase and amplitude corrections failed to replicate in silico findings.&#xD;Significance: DMAS or DMASMu reconstruction with multiple dynamic ranges and sub-image integration timings can provide more reliable mapping of cavitation in the canal in the presence of interference from paraspinal cavitation. &#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tensor-Based SPFD Method for Accurate Low-Frequency Magnetic Field Dosimetry in Anatomical Models.","authors":"Eikei Yamada, Yinliang Diao, Ilkka Laakso, Akimasa Hirata","doi":"10.1088/1361-6560/ae0ef9","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0ef9","url":null,"abstract":"<p><strong>Objective: </strong>Concerns regarding the potential adverse health effects of electromagnetic field (EMF) exposure are increasing. At low frequencies, international guidelines have adopted the induced electric field within the human body as an index for safety assessment. However, because direct, non-invasive measurement of the induced field is not feasible, computational analysis using anatomically realistic human models is commonly employed. However, these models rely on tissue segmentation and are prone to numerical artifacts, particularly staircasing errors, at tissue interfaces with sharp conductivity contrast. To address this issue, we propose a novel three-dimensional scalar-potential finite-difference (SPFD) method that incorporates a tensor-conductance model and applies it to realistic human head models for the first time.&#xD;Approach: We propose a three-dimensional SPFD method incorporating a tensor-conductance model, applied here for the first time to anatomically realistic human head models. The method was validated with multilayer spherical models and evaluated under uniform magnetic field exposure and transcranial magnetic stimulation (TMS).&#xD;Main Results: In spherical models, the proposed method reduced RMSE by up to 65% and improved agreement with theoretical values compared to the conventional method. In head models, it consistently suppressed numerical artifacts and reduced maximum electric field values by up to 22% under uniform exposure and by 5-8% under TMS. Computational efficiency was improved using a multigrid method, achieving a 25-fold speedup without compromising accuracy.&#xD;Significance: The tensor-based 3D SPFD method significantly improves field estimation accuracy while reducing computational artifacts in complex anatomical models. This approach may contribute to refining exposure limits and enhancing simulation fidelity for medical applications.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing Breathing Variability Using Surface Tracking-assisted Time-resolved Multi-cycle 4D Lung MRI.","authors":"Xiao Liang, Li Pan, Erez Nevo, Mumtaz Hussain Soomro, Steve Roys, Rao Gullapalli, Amit Sawant, Thomas Ernst, Jiachen Zhuo","doi":"10.1088/1361-6560/ae0ef8","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0ef8","url":null,"abstract":"<p><strong>Objective: </strong>To develop time-resolved, multi-cycle, MRI (TRMC-MRI) for 4D lung imaging that can capture respiration-induced cycle-to-cycle variations in the internal anatomy. &#xD;Approach: Golden-angle 3D stack-of-stars gradient echo data were continuously acquired during free breathing for 2 minutes, or 4000 radial views. Thoracoabdominal surface motion was concurrently tracked by four MR-compatible electromagnetic motion tracking sensors at a frequency of one tracking event for every two radial views. A radial view was a stack of k-space radial spokes acquired with the same radial angle for all the partition phase encoding steps. A continuous breathing state was defined for each tracking event, and the two radial views associated with each tracking event, by the principal component scores based on the sensor positions. To reconstruct a dynamic volume for a tracking event, radial views with similar breathing states to the tracking event in question were collected from the entire acquisition to fill the k-space. Sensitivity maps were estimated from all the acquired radial views. Reconstruction of dynamic volumes was performed with parallel imaging with total variation regularization. The proposed method was performed on four healthy volunteers (Male/Female: 3/1, Age: 30±2.3 years) in the right lung.&#xD;Main results: Thoracoabdominal surface tracking showed cycle-to-cycle breathing variability (coefficients of variation for period: 8-23%, for amplitude: 7-36%) despite instruction of breathing regularly. Dynamic lung volumes covering 320x320x24mm3 were generated at every 60.6ms for the entire 2-minute acquisition consisting of on average 23.2 (range: 18-34) breathing cycles. Considerable breathing variations were captured in time-resolved multi-cycle breathing motion. &#xD;Significance: The surface tracking-assisted TRMC-MRI framework can provide critical breathing variations information for MR-guided lung radiotherapy, including treatment planning, motion modeling and prediction, and training for real-time MR in the treatment room.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of the Medscint HYPERSCINT scintillation dosimetry research platform with orthovoltage photon beams and a novel quasi monoenergetic beam.","authors":"Leonard Che Fru, Benjamin Abraham Insley, Dongyoen Lee, Dirk Alan Bartkoski, Reza Reiazi, Aviad Schori, Ramesh Tailor, Mohammad R Salehpour","doi":"10.1088/1361-6560/ae0efb","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0efb","url":null,"abstract":"<p><strong>Objective: </strong>This work aimed to evaluate the dosimetric characteristics of the new HyperScintTM Research Platform 200 system (HS-RP200), with orthovoltage beams and a novel kilovoltage converging beam, Converging Radiotherapy and Radiosurgery (CRnR) beam.</p><p><strong>Approach: </strong>Several dosimetric properties of the HS-RP200 detector with several orthovoltage beams were studied in air. Relative dosimetry of the HS-RP200 detector was also studied for use on the converging x-ray beam. Energy correction factors were compared between measurement, Monte Carlo, and analytical methods.</p><p><strong>Main results: </strong>The signal-to-noise ratio (SNR) for the detector increased as the frame exposure time increased, and repeated measurements had less uncertainty when measurements were performed without stem-effect removal (WOSER). The short-term stability study of the detector's signal showed an acceptable variance of its readings to within 1% during one-hour measurements. The detector exhibited angular independence within the measurement uncertainty of 1%. The detector exhibited excellent dose linearity response with the orthovoltage beams. During an 80-day period, repeated measurements determined that the detector possesses long-term stability within a root mean square (rms) of 2.3% and 0.6% when measurements were performed with stem effect removal (WSER) and WOSER, respectively. Calculated energy correction factors implied significant fluctuations of the HS-RP200 detector with the beam quality of orthovoltage beams, ranging from 3% to 66%, depending on the divergence of the effective energy of the beam from reference quality. Monte Carlo-simulated energy correction factors agreed with measurements with a maximum difference of 5.4% when measurements were performed WOSER. Lastly, the HS-RP200 detector system exhibited good relative dosimetric characteristics with the CRnR beam. A maximum rms value of 0.019 was calculated for the normalized profiles when measurements from the HS-RP200 detector were compared to measurements from film.</p><p><strong>Significance: </strong>The HS-RP200 detector could be used as an alternative to other detectors with orthovoltage beams, including the CRnR beam.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TDMAR-Net: A Frequency-Aware Tri-Domain Diffusion Network for CT Metal Artifact Reduction.","authors":"Wenzhuo Chen, Bowen Ning, Zekun Zhou, Liu Shi, Qiegen Liu","doi":"10.1088/1361-6560/ae0efc","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0efc","url":null,"abstract":"<p><p>Metal implants and other high-density objects cause significant artifacts in computed tomography (CT) images, hindering clinical diagnosis. Traditional metal artifact reduction methods often leave residual artifacts due to sinogram edges discontinuities. Supervised deep learning approaches struggle due to reliance on paired data, while unsupervised methods often lack multi-domain information. In this paper, we propose TDMAR-Net, a diffusion model-based three-domain neural network that leverages priors from projection, image, and Fourier domains for removing metal artifact and enhancing CT image quality. To enhance the model's learning capability and gradient optimization while preventing reliance on a single data structure, we employ a two-stage training strategy that combines large-scale pretraining with masked data fine-tuning, improving both accuracy and adaptability in metal artifact removal. The specific process is to adjust the weight of the high frequency and low frequency components of the input image through the high-pass filter module in the Fourier domain, and process the image into blocks to extract the diffusion prior information. The prior information is then introduced iteratively into the sinogram and image domains to fill in the metal-induced artifacts. Our method overcomes the challenges of information sharing and complementarity across different domains, ensuring that each domain contributes effectively, thereby enhancing the precision and robustness of metal artifact elimination. Experiments show that our approach superior to existing unsupervised methods, which we have validated on both synthetic and clinical datasets.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast water/fat T2 and PDFF mapping via multiple overlapping‑echo detachment acquisition and deep learning reconstruction.","authors":"Qing Lin, Weikun Chen, Taishan Kang, 健 吴, Xinran Chen, Xiaobo Qu, Liangjie Lin, Jiazheng Wang, Jianzhong Lin, Zhong Chen, Shuhui Cai, Congbo Cai","doi":"10.1088/1361-6560/ae0efa","DOIUrl":"https://doi.org/10.1088/1361-6560/ae0efa","url":null,"abstract":"<p><strong>Objective: </strong>Rapid and accurate quantitative assessment of muscle tissue characteristics is critical for the diagnosis and monitoring of neuromuscular diseases (NMDs). Quantitative magnetic resonance imaging enables non-invasive assessment of muscle pathology by using water T2 values to detect muscle damage and proton density fat fraction (PDFF) to quantify fat infiltration. However, conventional methods for simultaneous water-fat separation and T2 quantification often require long acquisition times. This study aims to develop an ultrafast method for simultaneous water-fat separation and T2 quantification.&#xD;Approach: A novel water-fat separation framework that combines chemical shift encoding with the multiple overlapping-echo detachment sequence (CSE-MOLED) was proposed. Synthetic training data and deep learning-based reconstruction were employed to address challenges in water-fat separation, including the complex multi-peak spectral characteristic of fat and the non-idealities in MRI acquisition. The proposed method was validated through numerical simulations, phantom studies, and in vivo experiments involving five healthy volunteers, one subject with muscle atrophy, and one with muscle damage.&#xD;Main results: In numerical experiments, the R2 values were all 0.999 for water T2, fat T2, and PDFF. In phantom experiments, the R2 values were 0.995, 0.733, and 0.996 for water T2, fat T2, and PDFF, respectively. High repeatability (coefficient of variation < 2.0%) was achieved in both phantom and in vivo experiments. In patient scans, CSE-MOLED successfully distinguished between fat infiltration and muscle damage.&#xD;Significance: CSE-MOLED simultaneously obtains T2 and proton density maps for both water and fat, along with T2-corrected PDFF map, in 162 ms per slice, offering the potential to enhance the diagnostic accuracy of NMDs without increasing the clinical scanning burden.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}