Chemical exchange saturation transfer (CEST) MRI is a powerful molecular imaging technique for detecting metabolites through proton exchange. While CEST MRI provides high sensitivity, its clinical application is hindered by prolonged scan time due to the need for imaging across numerous frequency offsets for parameter estimation. Since scan time is directly proportional to the number of frequency offsets, identifying and selecting the most informative frequency can significantly reduce acquisition time. We propose a novel deep learning-based framework that integrates frequency selection and parameter estimation to accelerate CEST MRI. Our method leverages channel pruning via batch normalization to identify the most informative frequency offsets while simultaneously training the network for accurate parametric map prediction. Using data from six healthy volunteers, channel pruning selects 13 informative frequency offsets out of 53 without compromising map quality. Images from selected frequency offsets were reconstructed using the MR Multitasking method, which employs a low-rank tensor model to enable under-sampling of k-space lines for each frequency offset, further reducing scan time. Predicted parametric maps of amide proton transfer (APT), nuclear overhauser effect (NOE), and magnetization transfer (MT) based on these selected frequencies were comparable in quality to maps generated using all frequency offsets, achieving superior performance compared to Fisher information-based selection methods from our previous work. This integrated approach has the potential to reduce the whole-brain CEST MRI scan time from the original 5:30 min to under 1:30 min without compromising map quality. By leveraging deep learning for frequency selection and parametric map prediction, the proposed framework demonstrates its potential for efficient and practical clinical implementation. Future studies will focus on extending this method to patient populations and addressing challenges such as B₀ inhomogeneity and abnormal signal variation in diseased tissues.

Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data and the lack of labeled data make it challenging to accurately assess the response of locally advanced rectal cancer (LARC) patients to neoadjuvant chemoradiotherapy (nCRT) using current imaging indicators. In this study, we propose a novel learning framework to automatically predict the response of LARC patients to nCRT. Specifically, we develop a deep learning network called the Expand Intensive Attention Network (EIA-Net), which enhances the network's feature extraction capability through cascaded 3D convolutions and coordinate attention. Instance-oriented collaborative self-supervised learning (IOC-SSL) is proposed to leverage unlabeled data for training, reducing the reliance on labeled data. In a dataset consisting of 1,575 volumes, the proposed method achieves an AUC score of 0.8562. The dataset includes two distinct parts: the self-supervised dataset containing 1,394 volumes and the supervised dataset comprising 195 volumes. Analysis of the lifetime predictions reveals that patients with pathological complete response (pCR) predicted by EIA-Net exhibit better overall survival (OS) compared to non-pCR patients with LARC. The retrospective study demonstrates that imaging-based pCR prediction for patients with low rectal cancer can assist clinicians in making informed decisions regarding the need for Miles operation, thereby improving the likelihood of anal preservation, with an AUC of 0.8222. These results underscore the potential of our method to enhance clinical decision-making, offering a promising tool for personalized treatment and improved patient outcomes in LARC management.

Translating quantitative skeletal muscle MRI biomarkers into clinics requires efficient automatic segmentation methods. The purpose of this work is to investigate a simple yet effective iterative methodology for building a high-quality automatic segmentation model while minimizing the manual annotation effort. We used a retrospective database of quantitative MRI examinations (n = 70) of healthy and pathological thighs for training a nnU-Net segmentation model. Healthy volunteers and patients with various neuromuscular diseases, broadly categorized as dystrophic, inflammatory, neurogenic, and unlabeled NMDs. We designed an iterative procedure, progressively adding cases to the training set and using a simple visual five-level rating scale to judge the validity of generated segmentations for clinical use. On an independent test set (n = 20), we assessed the quality of the segmentation in 13 individual thigh muscles using standard segmentation metrics-dice coefficient (DICE) and 95% Hausdorff distance (HD95)-and quantitative biomarkers-cross-sectional area (CSA), fat fraction (FF), and water-T1/T2. We obtained high-quality segmentations (DICE = 0.88 ± 0.15/0.86 ± 0.14, HD95 = 6.35 ± 12.33/6.74 ± 11.57 mm), comparable to recent works, although with a smaller training set (n = 30). Inter-rater agreement on the five-level scale was fair to moderate but showed progressive improvement of the segmentation model along with the iterations. We observed limited differences from manually delineated segmentations on the quantitative outcomes (MAD: CSA = 65.2 mm², FF = 1%, water-T1 = 8.4 ms, water-T2 = 0.35 ms), with variability comparable to manual delineations.

Breast cancer poses a major health concern for women globally. Effective segmentation of breast tumors for ultrasound images is crucial for early diagnosis and treatment. Conventional convolutional neural networks have shown promising results in this domain but face challenges due to image complexities and are computationally expensive, limiting their practical application in real-time clinical settings. We propose Eff-AResUNet-GL, a segmentation model using EfficienetNet-B3 as the encoder. This design integrates attention gates in skip connections to focus on significant features and residual blocks in the decoder to retain details and reduce gradient loss. Additionally, guided loss functions are applied at each decoder layer to generate better features, subsequently improving segmentation accuracy. Experimental results on BUSIS and Dataset B demonstrate that Eff-AResUNet-GL achieves superior performance and computational efficiency compared to state-of-the-art models, showing robustness in handling complex breast ultrasound images. Eff-AResUNet-GL offers a practical, high-performing solution for breast tumor segmentation, demonstrating potential clinical through improved segmentation accuracy and efficiency.

Idiopathic Pulmonary Fibrosis (IPF) is a progressive lung disease that continuously scars and thickens lung tissue, leading to respiratory difficulties. Timely assessment of IPF progression is essential for developing treatment plans and improving patient survival rates. However, current clinical standards require multiple (usually two) CT scans at certain intervals to assess disease progression. This presents a dilemma: the disease progression is identified only after the disease has already progressed. To address this issue, a feasible solution is to generate the follow-up CT image from the patient's initial CT image to achieve early prediction of IPF. To this end, we propose a lung structure and function information-guided residual diffusion model. The key components of our model include (1) using a 2.5D generation strategy to reduce computational cost of generating 3D images with the diffusion model; (2) designing structural attention to mitigate negative impact of spatial misalignment between the two CT images on generation performance; (3) employing residual diffusion to accelerate model training and inference while focusing more on differences between the two CT images (i.e., the lesion areas); and (4) developing a CLIP-based text extraction module to extract lung function test information and further using such extracted information to guide the generation. Extensive experiments demonstrate that our method can effectively predict IPF progression and achieve superior generation performance compared to state-of-the-art methods.

Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Accurate monitoring of VS volume before and after GKS is essential for assessing treatment efficacy. To evaluate the accuracy of an artificial intelligence (AI) algorithm, originally developed to identify NF2-SWN-related VS, in segmenting non-NF2-SWN-related VS and detecting volume changes pre- and post-GKS. We hypothesize this AI algorithm, trained on NF2-SWN-related VS data, will accurately apply to non-NF2-SWN VS and VS treated with GKS. In this retrospective cohort study, we reviewed data from an established Gamma Knife database, identifying 16 patients who underwent GKS for VS and had pre- and post-GKS scans. Contrast-enhanced T1-weighted MRI scans were analyzed with both manual segmentation and the AI algorithm. DICE similarity coefficients were computed to compare AI and manual segmentations, and a paired t-test was used to assess statistical significance. Volume changes for pre- and post-GKS scans were calculated for both segmentation methods. The mean DICE score between AI and manual segmentations was 0.91 (range 0.79-0.97). Pre- and post-GKS DICE scores were 0.91 (range 0.79-0.97) and 0.92 (range 0.81-0.97), indicating high spatial overlap. AI-segmented VS volumes pre- and post-GKS were consistent with manual measurements, with high DICE scores indicating strong spatial overlap. The AI algorithm processed scans within 5 min, suggesting it offers a reliable, efficient alternative for clinical monitoring. DICE scores showed high similarity between manual and AI segmentations. The pre- and post-GKS VS volume percentage changes were also similar between manual and AI-segmented VS volumes, indicating that our AI algorithm can accurately detect changes in tumor growth.

Deformable registration is essential in medical image analysis, especially for handling various multi- and mono-modal registration tasks in neuroimaging. Existing studies lack exploration of brain MR-CT registration, and face challenges in both accuracy and efficiency improvements of learning-based methods. To enlarge the practice of multi-modal registration in brain, we present SR-Reg, a new benchmark dataset comprising 180 volumetric paired MR-CT images and annotated anatomical regions. Building on this foundation, we introduce MambaMorph, a novel deformable registration network based on an efficient state space model Mamba for global feature learning, with a fine-grained feature extractor for low-level embedding. Experimental results demonstrate that MambaMorph surpasses advanced ConvNet-based and Transformer-based networks across several multi- and mono-modal tasks, showcasing impressive enhancements of efficacy and efficiency. Code and dataset are available at https://github.com/mileswyn/MambaMorph.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be highly sensitive in diagnosing breast tumours, due to the kinetic and volumetric features inherent in it. To utilise the kinetics-related and volume-related information, this paper aims to develop and validate a classification for differentiating benign and malignant breast tumours based on DCE-MRI, though fusing deep features and cross-attention-encoded radiomics features using discriminant correlation analysis (DCA). Classification experiments were conducted on a dataset comprising 261 individuals who underwent DCE-MRI including those with multiple tumours, resulting in 137 benign and 163 malignant tumours. To improve the strength of correlation between features and reduce features' redundancy, a novel fusion method that fuses deep features and encoded radiomics features based on DCA (eFF-DCA) is proposed. The eFF-DCA includes three components: (1) a feature extraction module to capture kinetic information across phases, (2) a radiomics feature encoding module employing a cross-attention mechanism to enhance inter-phase feature correlation, and (3) a DCA-based fusion module that transforms features to maximise intra-class correlation while minimising inter-class redundancy, facilitating effective classification. The proposed eFF-DCA method achieved an accuracy of 90.9% and an area under the receiver operating characteristic curve of 0.942, outperforming methods using single-modal features. The proposed eFF-DCA utilises DCE-MRI kinetic-related and volume-related features to improve breast tumour diagnosis accuracy, but non-end-to-end design limits multimodal fusion. Future research should explore unified end-to-end deep learning architectures that enable seamless multimodal feature fusion and joint optimisation of feature extraction and classification.

This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE). In this retrospective study, data were collected from female patients who underwent US examinations and had DE. The US image records were divided into two parts. First, during the model development phase, an AI-DE model was trained employing YOLOv8 to detect pelvic DE nodules. Subsequently, its clinical applicability was evaluated by comparing the diagnostic performance of junior sonologists with and without AI-model assistance. The AI-DE model was trained using 248 images, which demonstrated high performance, with a mAP50 (mean Average Precision at IoU threshold 0.5) of 0.9779 on the test set. Total 147 images were used for evaluate the diagnostic performance. The diagnostic performance of junior sonologists improved with the assistance of the AI-DE model. The area under the receiver operating characteristic (AUROC) curve improved from 0.748 (95% CI, 0.624-0.867) to 0.878 (95% CI, 0.792-0.964; p < 0.0001) for junior sonologist A, and from 0.713 (95% CI, 0.592-0.835) to 0.798 (95% CI, 0.677-0.919; p < 0.0001) for junior sonologist B. Notably, the sensitivity of both sonologists increased significantly, with the largest increase from 77.42% to 94.35%. The AI-DE model based on US images showed good performance in DE detection and significantly improved the diagnostic performance of junior sonologists.

Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implications. Digital subtraction angiography (DSA) remains the gold standard for diagnosing vascular disease, but its invasiveness and procedural demands underscore the need for alternative diagnostic approaches. Coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive method for accurately classifying and localizing coronary artery stenosis. However, the complexity of CCTA images and their dependence on manual interpretation highlight the essential role of artificial intelligence in supporting clinicians in stenosis detection. This paper introduces a novel framework, Coronaryproposal-based Graph Convolutional Networks (Coronary p-Graph), designed for the automated detection of coronary stenosis from CCTA scans. The framework transforms CCTA data into curved multi-planar reformation (CMPR) images that delineate the coronary artery centerline. After aligning the CMPR volume along this centerline, the entire vasculature is analyzed using a convolutional neural network (CNN) for initial feature extraction. Based on predefined criteria informed by prior knowledge, the model generates candidate stenotic segments, termed "proposals," which serve as graph nodes. The spatial relationships between nodes are then modeled as edges, constructing a graph representation that is processed using a graph convolutional network (GCN) for precise classification and localization of stenotic segments. All CCTA images were rigorously annotated by three expert radiologists, using DSA reports as the reference standard. This novel methodology offers diagnostic performance equivalent to invasive DSA based solely on non-invasive CCTA, potentially reducing the need for invasive procedures. The proposed method was evaluated on a retrospective dataset comprising 259 cases, each with paired CCTA and corresponding DSA reports. Quantitative analyses demonstrated the superior performance of our approach compared to existing methods, with the following metrics: accuracy of 0.844, specificity of 0.910, area under the receiver operating characteristic curve (AUC) of 0.74, and mean absolute error (MAE) of 0.157.