Objective: Gadolinium-based contrast agents (GBCAs) are commonly employed with T1w MRI to enhance lesion visualization but are restricted in patients at risk of nephrogenic systemic fibrosis and variations in GBCA administration can introduce imaging inconsistencies. This study develops an efficient 3D deep-learning framework to generate T1-contrast enhanced images (T1C) from pre-contrast multiparametric MRI. Approach: We propose the 3D latent rectified flow (T1C-RFlow) model for generating high-quality T1C images. First, T1w and T2-FLAIR images are input into a pretrained autoencoder to acquire an efficient latent space representation. A rectified flow diffusion model is then trained in this latent space representation. The T1C-RFlow model was trained on a curated dataset comprised of the BraTS 2024 glioma (GLI; 1480 patients), meningioma (MEN; 1141 patients), and metastases (MET; 1475 patients) datasets. Selected patients were split into train (N=2860), validation (N=612), and test (N=614) sets. Results: Both qualitative and quantitative results demonstrate that the T1C-RFlow model outperforms benchmark 3D models (pix2pix, DDPM, Diffusion Transformers (DiT-3D)) trained in the same latent space. T1C-RFlow achieved the following metrics - GLI: NMSE 0.044 +/- 0.047, SSIM 0.935 +/- 0.025; MEN: NMSE 0.046 +/- 0.029, SSIM 0.937 +/- 0.021; MET: NMSE 0.098 +/- 0.088, SSIM 0.905 +/- 0.082. T1C-RFlow had the best tumor reconstruction performance and significantly faster denoising times (6.9 s/volume, 200 steps) than conventional DDPM models in both latent space (37.7s, 1000 steps) and patch-based in image space (4.3 hr/volume). Significance: Our proposed method generates synthetic T1C images that closely resemble ground truth T1C in much less time than previous diffusion models. Further development may permit a practical method for contrast-agent-free MRI for brain tumors.

In this work, we propose FFDP, a set of IO-aware non-GEMM fused kernels supplemented with a distributed framework for image registration at unprecedented scales. Image registration is an inverse problem fundamental to biomedical and life sciences, but algorithms have not scaled in tandem with image acquisition capabilities. Our framework complements existing model parallelism techniques proposed for large-scale transformer training by optimizing non-GEMM bottlenecks and enabling convolution-aware tensor sharding. We demonstrate unprecedented capabilities by performing multimodal registration of a 100 micron ex-vivo human brain MRI volume at native resolution - an inverse problem more than 570x larger than a standard clinical datum in about a minute using only 8 A6000 GPUs. FFDP accelerates existing state-of-the-art optimization and deep learning registration pipelines by upto 6 - 7x while reducing peak memory consumption by 20 - 59%. Comparative analysis on a 250 micron dataset shows that FFDP can fit upto 64x larger problems than existing SOTA on a single GPU, and highlights both the performance and efficiency gains of FFDP compared to SOTA image registration methods.

We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method enables causal interventions on target attributes, consistently propagating their effects to causal dependents without altering the core identity of the image. In contrast to prior approaches that rely on prompt engineering without explicit causal structure, Causal-Adapter leverages structural causal modeling augmented with two attribute regularization strategies: prompt-aligned injection, which aligns causal attributes with textual embeddings for precise semantic control, and a conditioned token contrastive loss to disentangle attribute factors and reduce spurious correlations. Causal-Adapter achieves state-of-the-art performance on both synthetic and real-world datasets, with up to 91\% MAE reduction on Pendulum for accurate attribute control and 87\% FID reduction on ADNI for high-fidelity MRI image generation. These results show that our approach enables robust, generalizable counterfactual editing with faithful attribute modification and strong identity preservation.

Traditional radiomics approaches focus on single-region feature extraction, limiting their ability to capture complex inter-regional relationships crucial for understanding pathophysiological mechanisms in complex diseases. This study introduces a novel cross-regional radiomics framework that systematically extracts relationship-based features between anatomically and functionally connected brain regions. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 140 early-stage Parkinson's disease patients (70 tremor-dominant, 70 postural instability gait difficulty) from the Parkinson's Progression Markers Initiative (PPMI) database across multiple imaging centers. Eight bilateral motor circuit regions (putamen, caudate nucleus, globus pallidus, substantia nigra) were segmented using standardized atlases. Two feature sets were developed: 48 traditional single-region of interest (ROI) features and 60 novel motor-circuit features capturing cross-regional ratios, asymmetry indices, volumetric relationships, and shape distributions. Six feature engineering scenarios were evaluated using center-based 5-fold cross-validation with six machine learning classifiers to ensure robust generalization across different imaging centers. Motor-circuit features demonstrated superior performance compared to single-ROI features across enhanced preprocessing scenarios. Peak performance was achieved with area under the curve (AUC) of 0.821 ± 0.117 versus 0.650 ± 0.220 for single-ROI features (p = 0.0012, Cohen's d = 0.665). Cross-regional ratios, particularly putamen-substantia nigra relationships, dominated the most discriminative features. Motor-circuit features showed superior generalization across multi-center data and better clinical utility through decision curve analysis and calibration curves. The proposed cross-regional radiomics framework significantly outperforms traditional single-region approaches for Parkinson's disease motor subtype classification. This methodology provides a foundation for advancing radiomics applications in complex diseases where inter-regional connectivity patterns are fundamental to pathophysiology.

Manual segmentation of sub-acute ischemic stroke lesions in fluid-attenuated inversion recovery magnetic resonance imaging (FLAIR MRI) is time-consuming and subject to inter-observer variability, limiting clinical workflow efficiency. To develop and validate an automated deep learning framework for accurate segmentation of sub-acute ischemic stroke lesions in FLAIR MRI using rigorous validation methodology. We propose a novel multi-path residual U-Net(U-shaped network) architecture with six parallel pathways per block (depths 0-5 convolutional layers) and 2.34 million trainable parameters. Hyperparameters were systematically optimized using 5-fold cross-validation across 60 configurations. We addressed intensity inhomogeneity using N4 bias field correction and employed strict patient-level data partitioning (18 training, 5 validation, 5 test patients) to prevent data leakage. Statistical analysis utilized bias-corrected bootstrap confidence intervals and Bonferroni correction for multiple comparisons. Our model achieved a validation dice similarity coefficient (DSC) of 0.85 ± 0.12 (95% CI: 0.79-0.91), a sensitivity of 0.82 ± 0.15, a specificity of 0.95 ± 0.04, and a Hausdorff distance of 14.1 ± 5.8 mm. Test set performance remained consistent (DSC: 0.89 ± 0.07), confirming generalizability. Computational efficiency was demonstrated with 45 ms inference time per slice. The architecture demonstrated statistically significant improvements over DRANet (p = 0.003), 2D CNN (p = 0.001), and Attention U-Net (p = 0.001), while achieving competitive performance comparable to CSNet (p = 0.68). The proposed framework demonstrates robust performance for automated stroke lesion segmentation with rigorous statistical validation. However, multi-site validation across diverse clinical environments remains essential before clinical implementation.

Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or counterfactual generation remains a challenge. In this work, we introduce a cycle training framework to fine-tune diffusion models for improved conditioning adherence and enhanced synthetic image realism. Our approach, Cycle Diffusion Model (CDM), enforces consistency between generated and original images by incorporating cycle constraints, enabling more reliable direct and counterfactual generation. Experiments on a combined 3D brain MRI dataset (from ABCD, HCP aging & young adults, ADNI, and PPMI) show that our method improves conditioning accuracy and enhances image quality as measured by FID and SSIM. The results suggest that the cycle strategy used in CDM can be an effective method for refining diffusion-based medical image generation, with applications in data augmentation, counterfactual, and disease progression modeling.

Precise confidence estimation in deep learning is vital for high-stakes fields like medical imaging, where overconfident misclassifications can have serious consequences. This work evaluates the effectiveness of Temperature Scaling (TS), a post-hoc calibration technique, in improving the reliability of convolutional neural networks (CNNs) for brain tumor classification. We develop a custom CNN and train it on a merged brain MRI dataset. To simulate real-world uncertainty, five types of image noise are introduced: Gaussian, Poisson, Salt & Pepper, Speckle, and Uniform. Model performance is evaluated using precision, recall, F1-score, accuracy, negative log-likelihood (NLL), and expected calibration error (ECE), both before and after calibration. Results demonstrate that TS significantly reduces ECE and NLL under all noise conditions without degrading classification accuracy. This underscores TS as an effective and computationally efficient approach to enhance decision confidence of medical AI systems, hence making model outputs more reliable in noisy or uncertain settings.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) pose significant challenges to public health and underscore the need for accurate and early diagnostic tools. Structural magnetic resonance imaging (sMRI) combined with advanced analytical techniques like convolutional neural networks (CNNs) seemed to offer a promising avenue for the diagnosis of these conditions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of CNN algorithms applied to sMRI data in differentiating between AD, MCI, and normal cognition (NC). Following the PRISMA-DTA guidelines, a comprehensive literature search was carried out in PubMed and Web of Science databases for studies published between 2018 and 2024. Studies were included if they employed CNNs for the diagnostic classification of sMRI data from participants with AD, MCI, or NC. The methodological quality of the included studies was assessed using the QUADAS-2 and METRICS tools. Data extraction and statistical analysis were performed to calculate pooled diagnostic accuracy metrics. A total of 21 studies were included in the study, comprising 16,139 participants in the analysis. The pooled sensitivity and specificity of CNN algorithms for differentiating AD from NC were 0.92 and 0.91, respectively. For distinguishing MCI from NC, the pooled sensitivity and specificity were 0.74 and 0.79, respectively. The algorithms also showed a moderate ability to differentiate AD from MCI, with a pooled sensitivity and specificity of 0.73 and 0.79, respectively. In the pMCI versus sMCI classification, a pooled sensitivity was 0.69 and a specificity was 0.81. Heterogeneity across studies was significant, as indicated by meta-regression results. CNN algorithms demonstrated promising diagnostic performance in differentiating AD, MCI, and NC using sMRI data. The highest accuracy was observed in distinguishing AD from NC and the lowest accuracy observed in distinguishing pMCI from sMCI. These findings suggest that CNN-based radiomics has the potential to serve as a valuable tool in the diagnostic armamentarium for neurodegenerative diseases. However, the heterogeneity among studies indicates a need for further methodological refinement and validation. This systematic review was registered in PROSPERO (Registration ID: CRD42022295408).

We aim to develop an interactive segmentation model that can offer accuracy and reliability for the segmentation of the irregularly shaped spinal cord and canal in degenerative cervical myelopathy (DCM) through manual click and model refinement. A dataset of 1444 frames from 294 magnetic resonance imaging records of DCM patients was used and we developed two different segmentation models for comparison : auto-segmentation and interactive segmentation. The former was based on U-Net and utilized a pretrained ConvNeXT-tiny as its encoder. For the latter, we employed an interactive segmentation model structured by SimpleClick, a large model that utilizes a vision transformer as its backbone, together with simple fine-tuning. The segmentation performance of the two models were compared in terms of their Dice scores, mean intersection over union (mIoU), Average Precision and Hausdorff distance. The efficiency of the interactive segmentation model was evaluated by the number of clicks required to achieve a target mIoU. Our model achieved better scores across all four-evaluation metrics for segmentation accuracy, showing improvements of +6.4%, +1.8%, +3.7%, and -53.0% for canal segmentation, and +11.7%, +6.0%, +18.2%, and -70.9% for cord segmentation with 15 clicks, respectively. The required clicks for the interactive segmentation model to achieve a 90% mIoU for spinal canal with cord cases and 80% mIoU for spinal cord cases were 11.71 and 11.99, respectively. We found that the interactive segmentation model significantly outperformed the auto-segmentation model. By incorporating simple manual inputs, the interactive model effectively identified regions of interest, particularly in the complex and irregular shapes of the spinal cord, demonstrating both enhanced accuracy and adaptability.

Contouring organs-at-risk (OARs) is a critical yet time-consuming step in head and neck (HaN) radiotherapy planning. Auto-segmentation methods have been widely studied, and commercial solutions are increasingly entering clinical use. However, their adoption warrants a comprehensive, multi-perspective evaluation. The purpose of this study is to compare three commercial artificial intelligence (AI) software solutions (Limbus, MIM and MVision) for HaN OAR auto-segmentation on a cohort of 10 computed tomography images with reference contours obtained from the public HaN-Seg dataset, from both observational (descriptive and empirical) and analytical (geometric, dosimetric and psychometric) perspectives. The observational evaluation included vendor questionnaires on technical specifications and radiographer feedback on usability. The analytical evaluation covered geometric (Dice similarity coefficient, DSC, and 95th percentile Hausdorff distance, HD95), dosimetric (dose constraint compliance, OAR priority-based analysis), and psychometric (5-point Likert scale) assessments. All software solutions covered a broad range of OARs. Overall geometric performance differences were relatively small (Limbus: 69.7% DSC, 5.0 mm HD95; MIM: 69.2% DSC, 5.6 mm HD95; MVision: 66.7% DSC, 5.3 mm HD95), however, statistically significant differences were observed for smaller structures such as the cochleae, optic chiasm, and pituitary and thyroid glands. Differences in dosimetric compliance were overall minor, with the lowest compliance observed for the oral cavity and submandibular glands. In terms of qualitative assessment, radiographers gave the highest average Likert rating to Limbus (3.9), followed by MVision (3.7) and MIM (3.5). With few exceptions, most software solutions produced good-quality AI-generated contours (Likert ratings ≥ 3), yet some editing should still be performed to reach clinical acceptability. Notable discrepancies were seen for the optic chiasm and in cases affected by mouth bites or dental artifacts. Importantly, no clear relationship emerged between geometric, dosimetric, and psychometric metrics, underscoring the need for a multi-perspective evaluation without shortcuts.