Medical image analysis最新文献

{"title":"Measuring and predicting where and when pathologists focus their visual attention while grading whole slide images of cancer","authors":"Souradeep Chakraborty ,&nbsp;Ruoyu Xue ,&nbsp;Rajarsi Gupta ,&nbsp;Oksana Yaskiv ,&nbsp;Constantin Friedman ,&nbsp;Natallia Sheuka ,&nbsp;Dana Perez ,&nbsp;Paul Friedman ,&nbsp;Won-Tak Choi ,&nbsp;Waqas Mahmud ,&nbsp;Beatrice Knudsen ,&nbsp;Gregory Zelinsky ,&nbsp;Joel Saltz ,&nbsp;Dimitris Samaras","doi":"10.1016/j.media.2025.103752","DOIUrl":"10.1016/j.media.2025.103752","url":null,"abstract":"<div><div>The ability to predict the attention of expert pathologists could lead to decision support systems for better pathology training. We developed methods to predict the spatio-temporal (“where” and “when”) movements of pathologists’ attention as they grade whole slide images (WSIs) of prostate cancer. We characterize a pathologist’s attention trajectory by their x, y, and m (magnification) movements of a viewport as they navigate WSIs using a digital microscope. This information was obtained from 43 pathologists across 123 WSIs, and we consider the task of predicting the pathologist attention scanpaths constructed from the viewport centers. We introduce a fixation extraction algorithm that simplifies an attention trajectory by extracting “fixations” in the pathologist’s viewing while preserving semantic information, and we use these pre-processed data to train and test a two-stage model to predict the dynamic (scanpath) allocation of attention during WSI reading via intermediate attention heatmap prediction. In the first stage, a transformer-based sub-network predicts the attention heatmaps (static attention) across different magnifications. In the second stage, we predict the attention scanpath by sequentially modeling the next fixation points in an autoregressive manner using a transformer-based approach, starting at the WSI center and leveraging multi-magnification feature representations from the first stage. Experimental results show that our scanpath prediction model outperforms chance and baseline models. Tools developed from this model could assist pathology trainees in learning to allocate their attention during WSI reading like an expert.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103752"},"PeriodicalIF":11.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI","authors":"Ziyun Liang ,&nbsp;Xiaoqing Guo ,&nbsp;Wentian Xu ,&nbsp;Yasin Ibrahim ,&nbsp;Natalie Voets ,&nbsp;Pieter M. Pretorius ,&nbsp;Alzheimer’s Disease Neuroimaging Initiative,&nbsp;J. Alison Noble ,&nbsp;Konstantinos Kamnitsas","doi":"10.1016/j.media.2025.103763","DOIUrl":"10.1016/j.media.2025.103763","url":null,"abstract":"<div><div>Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as ‘normal’. In the testing phase, they identify patterns that deviate from this normal distribution as ‘anomalies’. To learn the ‘normal’ distribution, prevailing methods corrupt the images and train a model to reconstruct them. During testing, the model attempts to reconstruct corrupted inputs based on the learned ‘normal’ distribution. Deviations from this distribution lead to high reconstruction errors, which indicate potential anomalies. However, corrupting an input image inevitably causes information loss even in normal regions, leading to suboptimal reconstruction and an increased risk of false positives. To alleviate this, we propose <span><math><mi>IterMask3D</mi></math></span>, an iterative spatial mask-refining strategy designed for 3D brain MRI. We iteratively spatially mask areas of the image as corruption and reconstruct them, then shrink the mask based on reconstruction error. This process iteratively unmasks ‘normal’ areas to the model, whose information further guides reconstruction of ‘normal’ patterns under the mask to be reconstructed accurately, reducing false positives. In addition, to achieve better reconstruction performance, we also propose using high-frequency image content as additional structural information to guide the reconstruction of the masked area. Extensive experiments on the detection of both synthetic and real-world imaging artifacts, as well as segmentation of various pathological lesions across multiple MRI sequences, consistently demonstrate the effectiveness of our proposed method. Code is available at <span><span>https://github.com/ZiyunLiang/IterMask3D</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103763"},"PeriodicalIF":11.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StaDis: Stability distance to detecting out-of-distribution data in computational pathology","authors":"Di Zhang ,&nbsp;Jiusong Ge ,&nbsp;Jiashuai Liu ,&nbsp;Chunbao Wang ,&nbsp;Tieliang Gong ,&nbsp;Zeyu Gao ,&nbsp;Chen Li","doi":"10.1016/j.media.2025.103774","DOIUrl":"10.1016/j.media.2025.103774","url":null,"abstract":"<div><div>Modern Computational pathology (CPath) models aim to alleviate the burden on pathologists. However, once deployed, these models may generate unreliable predictions when encountering data types not seen during training, potentially causing a trust crisis within the computational pathology community. Out-of-distribution (OOD) detection, acting as a safety measure before model deployment, demonstrates significant promise in ensuring the reliable use of models in real clinical application. However, most existing computational pathology models lack OOD detection mechanisms, and no OOD detection method is specifically designed for this field. In this paper, we propose a novel OOD detection approach called Stability Distance (StaDis), uniquely developed for CPath. StaDis measures the feature gap between an image and its perturbed counterpart. As a plug-and-play module, it requires no retraining and integrates seamlessly with any model. Additionally, for the first time, we explore OOD detection at the whole-slide image (WSI) level within the multiple instance learning (MIL) framework. Then, we design different pathological OOD detection benchmarks covering three real clinical scenarios: patch- and slide-level anomaly tissue detection, rare case mining, and frozen section (FS) detection. Finally, extensive comparative experiments are conducted on these pathological OOD benchmarks. In 38 experiments, our approach achieves SOTA performance in 23 cases and ranks second in 10 experiments. Especially, the AUROC results of StaDis with “Conch” as the backbone improve by 7.91% for patch-based anomaly tissue detection. Our code is available at <span><span>https://github.com/zdipath/StaDis</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103774"},"PeriodicalIF":11.8,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QCResUNet: Joint subject-level and voxel-level segmentation quality prediction","authors":"Peijie Qiu ,&nbsp;Satrajit Chakrabarty ,&nbsp;Phuc Nguyen ,&nbsp;Soumyendu Sekhar Ghosh ,&nbsp;Aristeidis Sotiras","doi":"10.1016/j.media.2025.103718","DOIUrl":"10.1016/j.media.2025.103718","url":null,"abstract":"<div><div>Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementation in clinical practice difficult. Therefore, there is a need for quality control (QC) to screen the quality of the segmentation results. Although numerous automatic QC methods have been developed for segmentation quality screening, most were designed for cardiac MRI segmentation, which involves a single modality and a single tissue type. Furthermore, most prior works only provided subject-level predictions of segmentation quality and did not identify erroneous parts segmentation that may require refinement. To address these limitations, we proposed a novel multi-task deep learning architecture, termed QCResUNet, which produces subject-level segmentation-quality measures as well as voxel-level segmentation error maps for each available tissue class. To validate the effectiveness of the proposed method, we conducted experiments on assessing its performance on evaluating the quality of two distinct segmentation tasks. First, we aimed to assess the quality of brain tumor segmentation results. For this task, we performed experiments on one internal (Brain Tumor Segmentation (BraTS) Challenge 2021, <span><math><mrow><mi>n</mi><mo>=</mo><mn>1</mn><mo>,</mo><mn>251</mn></mrow></math></span>) and two external datasets (BraTS Challenge 2023 in Sub-Saharan Africa Patient Population (BraTS-SSA), <span><math><mrow><mi>n</mi><mo>=</mo><mn>40</mn></mrow></math></span>; Washington University School of Medicine (WUSM), <span><math><mrow><mi>n</mi><mo>=</mo><mn>175</mn></mrow></math></span>). Specifically, we first performed a three-fold cross-validation on the internal dataset using segmentations generated by different methods at various quality levels, followed by an evaluation on the external datasets. Second, we aimed to evaluate the segmentation quality of cardiac Magnetic Resonance Imaging (MRI) data from the Automated Cardiac Diagnosis Challenge (ACDC, <span><math><mrow><mi>n</mi><mo>=</mo><mn>100</mn></mrow></math></span>). The proposed method achieved high performance in predicting subject-level segmentation-quality metrics and accurately identifying segmentation errors on a voxel basis. This has the potential to be used to guide human-in-the-loop feedback to improve segmentations in clinical settings.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103718"},"PeriodicalIF":11.8,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos","authors":"Md Mostafa Kamal Sarker ,&nbsp;Divyanshu Mishra ,&nbsp;Mohammad Alsharid ,&nbsp;Netzahualcoyotl Hernandez-Cruz ,&nbsp;Rahul Ahuja ,&nbsp;Olga Patey ,&nbsp;Aris T. Papageorghiou ,&nbsp;J. Alison Noble","doi":"10.1016/j.media.2025.103758","DOIUrl":"10.1016/j.media.2025.103758","url":null,"abstract":"<div><div>Fetal echocardiography offers non-invasive and real-time imaging acquisition of fetal heart images to identify congenital heart conditions. Manual acquisition of standard heart views is time-consuming, whereas automated detection remains challenging due to high spatial similarity across anatomical views with subtle local image appearance variations. To address these challenges, we introduce a very lightweight frequency-guided deep learning-based model named HarmonicEchoNet that can automatically detect heart standard views in a transverse sweep or freehand ultrasound scan of the fetal heart.</div><div>HarmonicEchoNet uses harmonic convolution blocks (HCBs) and a harmonic spatial and channel squeeze-and-excitation (hscSE) module. The HCBs apply a Discrete Cosine Transform (DCT)-based harmonic decomposition to input features, which are then combined using learned weights. The hscSE module identifies significant regions in the spatial domain to improve feature extraction of the fetal heart anatomical structures, capturing both spatial and channel-wise dependencies in an ultrasound image. The combination of these modules improves model performance relative to recent CNN-based, transformer-based, and CNN+transformer-based image classification models.</div><div>We use four datasets from two private studies, PULSE (Perception Ultrasound by Learning Sonographic Experience) and CAIFE (Clinical Artificial Intelligence in Fetal Echocardiography), to develop and evaluate HarmonicEchoNet models. Experimental results show that HarmonicEchoNet is 10–15 times faster than ConvNeXt, DeiT, and VOLO, with an inference time of just 3.9 ms. It also achieves 2%–7% accuracy improvement in classifying fetal heart standard planes compared to these baselines. Furthermore, with just 19.9 million parameters compared to ConvNeXt’s 196.24 million, HarmonicEchoNet is nearly ten times more parameter-efficient.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103758"},"PeriodicalIF":11.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Category-specific unlabeled data risk minimization for ultrasound semi-supervised segmentation","authors":"Lu Xu, Mingyuan Liu, Boxuan Wei, Yihua He, Zhifan Gao, Hongbin Han, Jicong Zhang","doi":"10.1016/j.media.2025.103773","DOIUrl":"https://doi.org/10.1016/j.media.2025.103773","url":null,"abstract":"Achieving accurate computer-aided analysis of ultrasound images is challenging, since not only its image artifacts but also the difficulties in collecting large-scale pixel-wise annotations from experts for training. Semi-supervised segmentation is a solution for learning from labeled and unlabeled data, which mainly focuses on generating pseudo annotations for unlabeled data or learning consistent features in enhanced views of images to enhance model generalization. However, anatomically, diverse learning difficulties across tissues are overlooked, and, technically, the estimation and minimization of empirical risk for unlabeled training data are largely ignored. Motivated by them, this work proposes a semi-supervised segmentation model, named CSUDRM, with two modules. The former is called category-specific distribution alignment (CSDA), which learns more consistent feature representations of the same class across labeled and unlabeled data. Moreover, it enhances feature space intra-class compactness and inter-class discrepancy and provides category-specific penalties for more robust learning. The latter one is Unlabeled Data Risk Minimization (UDRM). It minimizes the risk on the entire training data, which distinguishes it from most existing works that merely optimize labels. The risk of unlabeled data is estimated by a novel learnable class prior estimator, with the help of distributional hints from CSDA. This design could reinforce the robustness of the model and achieve stable segmentation. CSUDRM achieves state-of-the-art performances on four ultrasound datasets. Extensive ablation studies, including quantitative comparisons, feature space visualization, and robustness analysis, demonstrate the superiority of our designs.","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"13 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supervised white matter bundle segmentation in glioma patients with transfer learning","authors":"Chiara Riccardi ,&nbsp;Ludovico Coletta ,&nbsp;Sofia Ghezzi ,&nbsp;Gabriele Amorosino ,&nbsp;Luca Zigiotto ,&nbsp;Jorge Jovicich ,&nbsp;Silvio Sarubbo ,&nbsp;Paolo Avesani","doi":"10.1016/j.media.2025.103766","DOIUrl":"10.1016/j.media.2025.103766","url":null,"abstract":"<div><div>In clinical settings, the virtual dissection of white matter tracts represents an informative source of information for monitoring neurological conditions or to support the planning of a treatment. The implementation of this task through data-driven methodologies and, in particular, deep learning models demonstrates promising evidence of achieving high accuracy when applied to healthy individuals. However, the lack of large clinical datasets and the profound differences between healthy and clinical populations hinder the translation of these results to patients. Here, we investigated for the first time the effectiveness of transfer learning in adapting a deep learning architecture trained on a healthy population to glioma patients. Importantly, we provided the first thorough characterization of domain shift and its complexity, distinguishing systematic (i.e. measurement and pre-processing related) from tumor-specific components. Our results suggest that (i) models trained on a large normative healthy population have a significant performance drop when the inference is carried out on patients; (ii) transfer learning can be an effective strategy to overcome the shortage of clinical data and to manage the systematic shift; (iii) fine-tuning of the learning model cannot accommodate large white matter deformations induced by the tumor. The results were coherent across the five white matter bundles and the three input modalities tested, highlighting their robustness and generalizability. Our work provides valuable insights for advancing automated white matter segmentation in clinical populations and enhancing clinical transfer learning applications.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103766"},"PeriodicalIF":11.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised 4D-flow MRI reconstruction based on partially-independent generative modeling and complex-difference sparsity constraint","authors":"Zhongsen Li ,&nbsp;Aiqi Sun ,&nbsp;Haining Wei ,&nbsp;Wenxuan Chen ,&nbsp;Chuyu Liu ,&nbsp;Haozhong Sun ,&nbsp;Chenlin Du ,&nbsp;Rui Li","doi":"10.1016/j.media.2025.103769","DOIUrl":"10.1016/j.media.2025.103769","url":null,"abstract":"<div><div>4D-flow MRI can provide spatiotemporal quantification of in-vivo blood flow velocity, which holds significant diagnostic value for various vascular diseases. Due to the large data size, 4D-flow MRI typically requires undersampling to shorten the scan time and employs reconstruction algorithms to recover images. Recently, deep learning methods have emerged for 4D-flow MRI reconstruction, but most of them are supervised algorithms, which have two major problems. First, supervised methods require high-quality fully sampled data for network training, which is usually very limited for 4D-flow MRI. Second, concerns are raised about the algorithm’s generalization ability since the morphology and velocity distribution vary in different vascular beds. In this work, we propose an unsupervised method for 4D-flow MRI reconstruction based on the deep image prior framework, which exploits the structural prior of convolutional neural networks for generative image recovery. Our method has three central components. First, we design a partially-independent network to improve the parameter efficiency and reduce the model size for 4D-flow MRI generation. Second, we incorporate the complex difference sparsity constraint to improve the accuracy of image phase recovery. Third, we introduce a joint generative and sparse optimization goal, and propose a “pretraining + ADMM finetuning” optimization algorithm for solution. Comprehensive experiments were conducted on two in-house acquired 4D-flow MRI datasets: an aorta dataset and a brain vessel dataset, compared with compressed-sensing algorithms and supervised deep-learning methods. The results demonstrate the superior reconstruction performance and generalization capability of the proposed method.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103769"},"PeriodicalIF":11.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A doppler-exclusive computational diagnostic framework to enhance conventional 2-D clinical ultrasound with 3-D mitral valve dynamics and cardiac hemodynamics","authors":"Nikrouz Bahadormanesh ,&nbsp;Mohamed Abdelkhalek ,&nbsp;Zahra Keshavarz-Motamed","doi":"10.1016/j.media.2025.103772","DOIUrl":"10.1016/j.media.2025.103772","url":null,"abstract":"<div><div>Mitral valve diseases are prevalent cardiac conditions especially by aging. With their high prevalence, the accessibility, accuracy, and reliability of the diagnostic methods are crucial. Mitral valve dynamics assessment could offer crucial insights into the progression of cardiac deterioration and recovery, significantly influencing patient care, intervention planning, and critical clinical decision-making in scenarios with potentially life-threatening risks. In this study, we developed a Doppler-exclusive computational diagnostic framework to assess mitral valve motion and dynamics as well as cardiac hemodynamics in patients non-invasively and at no risk to the patients. The framework was developed based on transthoracic echocardiogram (TTE) data (N=20), validated against transesophageal echocardiography (TEE) data (N=12) as well as CT data (N=4). In addition, we demonstrated the framework’s diagnostic abilities by providing novel and clinically-relevant analyses and interpretations of clinical data. Based on our findings, patient-specific left ventricular pressure was a strong predictor of stress levels in our cohort of 20 patients, despite being neglected by previous studies. There was a very strong negative correlation between the 3-D finite element-based coaptation area and vena Contracta width (R = -0.8; <em>p</em> &lt; 0.001). Furthermore, the LV conicity index, as the geometrical parameter showing left ventricle dilatation, had a strong positive correlation with end diastolic von Mises stress, used for quantification of leaflet tethering (R = 0.78; <em>p</em> &lt; 0.001). Finally, the patient-specific left ventricular pressure, and the rest length of the chords played a primary role in the biomechanical behavior of the mitral leaflets. The developed framework, while aligned with the current clinical metrics, could provide a strong add-on to the established clinical practice for the diagnosis of mitral valve diseases. Notably, this framework is novel in that it relies solely on standard Doppler ultrasound inputs, requiring no additional imaging or invasive measurements to achieve 3-D assessment. Clinically, the DE-MV-Dyn can be seamlessly applied in routine echocardiography exams to provide clinicians with new patient-specific metrics (e.g., leaflet stress, strain, and dynamic coaptation measures) for improved diagnosis and personalized mitral valve therapy planning.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103772"},"PeriodicalIF":11.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paired phase and magnitude reconstruction neural network for multi-shot diffusion magnetic resonance imaging","authors":"Qiaoling Lin ,&nbsp;Xuanchu Chen ,&nbsp;Boxuan Shi ,&nbsp;Chen Qian ,&nbsp;Mingyang Han ,&nbsp;Liuhong Zhu ,&nbsp;Dafa Shi ,&nbsp;Xiaoyong Shen ,&nbsp;Wanjun Hu ,&nbsp;Dan Ruan ,&nbsp;Yi Guo ,&nbsp;Jianjun Zhou ,&nbsp;Xiaobo Qu","doi":"10.1016/j.media.2025.103771","DOIUrl":"10.1016/j.media.2025.103771","url":null,"abstract":"<div><div>Diffusion weighted imaging (DWI) is an important magnetic resonance imaging modality that reflects the diffusion of water molecules and has been widely used in tumor diagnosis. Higher image resolution is possible through multi-shot sampling but raises the challenge of suppressing image artifacts and noise when combining multi-shot data. Conventional methods introduce the magnitude and/or phase priors and regularize the reconstructed image in an iterative computing process, which suffers from slow computational speed. Deep learning offers a valuable solution to this challenge. In this work, traditional methods are adopted to generate the training labels offline. Then, a neural network is designed for paired phase and magnitude reconstruction. Last, the network is further improved by incorporating a high signal-to-noise ratio (SNR) b0 image with small geometric distortions. Compared with the state-of-the-art deep learning approach, results on simulated and in vivo data demonstrate that the proposed method enables sub-second fast reconstruction and achieves better objective evaluation criteria. Besides, a study by six radiologists on image quality confirms that the proposed method is within the excellent range and provides higher scores of image artifact suppression and more stable overall quality as well as SNR. This work provides a solution for fast and promising image reconstruction for multi-shot DWI.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103771"},"PeriodicalIF":11.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}