HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimation

Accurate liver segmentation in multi-phase MRI is crucial for assessing liver fibrosis, but labeled data is often limited. A new label-efficient segmentation approach has been proposed, which enhances cross-modality generalization under real-world conditions. This method utilizes a foundation-scale 3D segmentation backbone, co-training with cross pseudo supervision, and a standardized preprocessing pipeline, achieving robust segmentation performance without spatial registration.

The study presents a novel approach to understanding the complex geometry of the human cortex, specifically focusing on sulcal depth. It highlights the influence of global brain size on sulcal depth measurements, introduces a scale-invariant method for estimating sulcal depth, and validates this method using a large sample of 1,987 subjects. The research aims to enhance both basic and clinical applications of brain imaging.

Bladder cancer, with a recurrence rate of up to 78%, poses significant challenges for post-operative monitoring. Traditional multi-sequence contrast-enhanced MRI scans are often difficult to interpret due to changes from surgery. This study introduces H-CNN-ViT, a new AI model designed to enhance bladder cancer recurrence prediction by utilizing a curated multi-sequence MRI dataset, which aims to improve diagnostic accuracy and patient management.

The study presents a novel approach to multi-contrast magnetic resonance imaging (MRI) super-resolution, aiming to reconstruct high-resolution images from low-resolution scans. By utilizing structural information from high-resolution reference images with varying contrasts, the proposed dual-prompt expert network, CD-DPE, addresses challenges in feature integration caused by contrast disparities. This method enhances anatomical detail and soft tissue differentiation, which are crucial for early diagnosis and clinical decision-making.

The paper introduces Knowledge-Informed Dynamic Optimal Transport (KIDOT), a framework aimed at improving medical image reconstruction from measurement data. Traditional deep learning methods often rely on paired measurements and high-quality images, leading to performance issues when applied to real prospective data due to the retrospective-to-prospective gap. KIDOT addresses this by conceptualizing reconstruction as a dynamic transport path, learning from unpaired data and ensuring consistency with imaging physics.

The Cross-Modal Compositional Self-Distillation (CCSD) framework has been proposed to enhance brain tumor segmentation from multi-modal MRI scans. This method addresses the challenge of missing modalities in clinical settings, which can hinder the performance of deep learning models. By utilizing a shared-specific encoder-decoder architecture and two self-distillation strategies, CCSD aims to improve the robustness and accuracy of segmentation, ultimately aiding in clinical diagnosis and treatment planning.

A recent study presents a deformation-aware graph neural network model designed to predict breast cancer sites in real time during biopsy procedures. This innovation addresses the limitations of traditional MRI-guided biopsies, which are often time-consuming and costly. By utilizing individual-specific finite element models derived from MRI images, the new approach aims to enhance the accuracy of cancer detection, ultimately improving patient outcomes through timely diagnosis and treatment planning.