Due to the inherent limitation of MRI in directly capturing early metabolic abnormalities associated with neurological disorders, and considering the high cost and radiation risks associated with PET scans, cross-modal MRI-to-PET image synthesis has emerged as a critical pathway for early and precise diagnosis. However, current methods generally suffer from structural distortion, blurred details, and computational inefficiencies, significantly restricting their clinical applicability. To address these limitations, this paper proposes an innovative multi-scale autoregressive-driven framework for MRI-to-PET cross-modal image generation. By explicitly modeling scalewise transformations between MRI and PET via a multi-scale autoregressive mechanism, and incorporating wavelet transform with a linear multi-step connection strategy, our framework effectively enhances structural accuracy and texture detail expression, especially in lesion regions. Experimental results on the ADNI Alzheimer's Disease dataset and a private epilepsy dataset demonstrate that the proposed method consistently outperforms state-of-the-art approaches, generating high-quality PET images efficiently and robustly. Furthermore, it substantially reduces diagnostic costs and radiation exposure, showcasing promising prospects for clinical adoption. Our source code is available at https://github.com/Guanyu-Zhou/WARPNet.

ABSTRACT Multiple sclerosis (MS) lesion segmentation from MR imaging is a prerequisite step in clinical diagnosis and treatment of brain diseases. However, automated segmentation of MS lesions remains a challenging task, owing to the variant morphology and uncertain distribution of lesions across subjects. Despite the achieved success by existing methods, two problems still persist in automated segmentation of MS lesions, namely the lack of an effective feature enhancement approach for capturing locality context and the lack of global coherence in prediction for pixels. Hence, we propose a correlation learning network for both local and global context in this work. Specifically, we propose a sparse spatial correlation module to learn the spatial correlations within neighbours for local context. Besides, we propose a global coherence module to encode long‐range dependencies for global context. The proposed method is evaluated on a public ISBI2015 datatset and a private in‐house dataset collected from hospital. Experimental results show the competitive performance of our method against state‐of‐the‐art methods.

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into "realistic" healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92.50 $\pm$ 2.70%.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of ischemic tissue are subtle and easily confounded by normal brain tissue, it remains a very challenging task. However, existing methods often ignore or confuse the contribution of different types of anatomical asymmetry caused by intrinsic and pathological changes to segmentation. Further, inefficient domain knowledge utilization leads to mis-segmentation for AIS infarcts. Inspired by this idea, we propose a pathological asymmetry-guided progressive learning (PAPL) method for AIS infarct segmentation. PAPL mimics the step-by-step learning patterns observed in humans, including three progressive stages: knowledge preparation stage, formal learning stage, and examination improvement stage. First, knowledge preparation stage accumulates the preparatory domain knowledge of the infarct segmentation task, helping to learn domain-specific knowledge representations to enhance the discriminative ability for pathological asymmetries by constructed contrastive learning task. Then, formal learning stage efficiently performs end-to-end training guided by learned knowledge representations, in which the designed feature compensation module (FCM) can leverage the anatomy similarity between adjacent slices from the volumetric medical image to help aggregate rich anatomical context information. Finally, examination improvement stage encourages improving the infarct prediction from the previous stage, where the proposed perception refinement strategy (RPRS) further exploits the bilateral difference comparison to correct the mis-segmentation infarct regions by adaptively regional shrink and expansion. Extensive experiments on public and in-house NCCT datasets demonstrated the superiority of the proposed PAPL, which is promising to help better stroke evaluation and treatment.

The segmentation of multiple sclerosis (MS) lesions from MR imaging sequences remains a challenging task, due to the characteristics of variant shapes, scattered distributions and unknown numbers of lesions. However, the current automated MS segmentation methods with deep learning models face the challenges of (1) capturing the scattered lesions in multiple regions and (2) delineating the global contour of variant lesions. To address these challenges, in this paper, we propose a novel attention and graph-driven network (DAG-Net), which incorporates (1) the spatial correlations for embracing the lesions in distant regions and (2) the global context for better representing lesions of variant features in a unified architecture. Firstly, the novel local attention coherence mechanism is designed to construct dynamic and expansible graphs for the spatial correlations between pixels and their proximities. Secondly, the proposed spatial-channel attention module enhances features to optimize the global contour delineation, by aggregating relevant features. Moreover, with the dynamic graphs, the learning process of the DAG-Net is interpretable, which in turns support the reliability of segmentation results. Extensive experiments were conducted on a public ISBI2015 dataset and an in-house dataset in comparison to state-of-the-art methods, based on geometrical and clinical metrics. The experimental results validate the effectiveness of proposed DAG-Net on segmenting variant and scatted lesions in multiple regions.

The non-invasive quantification of the cerebral metabolic rate for glucose (CMRGlc) and the characterization of cerebral metabolism in the cerebrovascular territories are helpful in understanding ischemic cerebrovascular disease (ICVD). Firstly, we investigated a non-invasive quantification approach based on an image-derived input function (IDIF) in ICVD. Second, we studied the metabolic changes in CMRGlc after surgical intervention. We evaluated the hypothesis that the IDIF method based on the unilateral internal carotid artery could address challenges in ICVD quantification. The CMRGlc and standardized uptake value ratio (SUVR) were used to measure glucose metabolism activity. Healthy controls showed no significant differences in CMRGlc values between bilateral and unilateral IDIF measurements (intraclass correlation coefficient [ICC]: 0.91-0.98). Patients with ICVD showed significantly increased CMRGlc values after surgical intervention for all territories (percentage changes: 7.4%-22.5%). In contrast, SUVR showed minor differences between postoperative and preoperative patients, indicating that it was a poor biomarker for the diagnosis of ICVD. A significant association between CMRGlc and the National Institutes of Health Stroke Scale (NIHSS) scores was observed (r=-0.54). Our findings suggested that IDIF could be a valuable tool for CMRGlc quantification in patients with ICVD and may advance personalized precision interventions.

Fusion of multimodal imaging data with nonimaging data is critically important for a more complete understanding of the disease characteristics and therefore essential to accurate computer-aided diagnosis. However, there are two major challenges. 1) Effective discovery of the discriminative multimodal information during the fusion process is hindered by the large dimension gap between raw medical images and clinical factors. 2) Interpreting the complex nonlinear cross-modal association, especially in deep-network-based fusion models, remains an unsolved challenge, which is essential for uncovering the disease mechanism. To address the two challenges, we propose an Interpretable Deep Multimodal Fusion (DMFusion) Framework based on Deep Canonical Correlation Analysis (CCA). Specifically, a novel DMFusion loss is proposed to optimize the discovery of discriminative multimodal representations in low-dimensional latent fusion space. It is achieved by jointly exploiting intermodal correlational association via CCA loss and intra-modal structural and discriminative information via reconstruction loss and cross-entropy loss. For interpreting the nonlinear cross-modal association in DMFusion network, we propose a cross-modal association (CA) score to quantify the importance of input features towards the correlated association, by harnessing integrated gradients in deep networks and canonical loading in CCA projection. The proposed fusion framework was validated on the differential diagnosis of demyelinating diseases in Central Nervous System (CNS) and outperformed six state-of-the-art methods on three fusion tasks.

Feature engineering and deep learning have been widely used to characterize imaging features in medical applications. However, the importance of geometric structure and spatial relationship of multiple lesions for multifocal diseases are often neglected by these methods. In this paper, we propose a Multi-level Topological Analysis (MTA) framework based on persistent homology, by capturing global-level topological invariants underlying geometric structure and local-level spatial adjacency relationship among lesions and local structure. In particular, a novel Filtration-based Community Discovery algorithm is designed to efficiently partition lesions to local clusters. Experiments demonstrate that our MTA framework outperforms five state-of-the-art persistent homology methods and achieved AUC 824±0.132 on a task of differentiating two multifocal diseases, Multiple Sclerosis and Neuromyelitis Optica.