Magnetic Resonance Imaging (MRI) is a leading diagnostic modality for a wide range of exams, where multiple contrast images are often acquired for characterizing different tissues. However, acquiring high-resolution MRI typically extends scan time,...

Intraoperative radiographs are used in image-guided spine surgery (IGSS) for vertebrae planning and other processes like 2D/3D registration, single image tomography, and 3D reconstruction. Since preoperative CT is also a prerequisite for the ...

Transformer-based networks applied to image patches have achieved cutting-edge performance in many vision tasks. However, lacking the built-in bias of convolutional neural networks (CNN) for local image statistics, they require large datasets and ...

Multi-view breast ultrasound imaging has been routinely performed in clinical settings to ensure comprehensive disease evaluation. Recently, artificial intelligence (AI) has been developed to interpret medical images; however, most of the current ...

Iterative refinement based image super-resolution with conditional denoising diffusion probabilistic models (DDPM), such as SR3 [21], has shown promise in the super-resolution of magnetic resonance images (MRIs). However, these methods are ...

Positron Emission Tomography (PET) is an established functional imaging modality integral to clinical practices. Despite its widespread utility, the attendant radiation exposure from PET scans has raised substantial health concerns. To address ...

MR data are acquired in the frequency domain, known as k-space. Acquiring high-quality and high-resolution MR images can be time-consuming, posing a significant challenge when multiple sequences providing complementary contrast information are ...$\mathrm{}$

Multi-modality imaging plays a crucial role in clinical diagnosis. Reconstructing missing modality images, such as CT-to-MR, is quite important when only one modality is available. Previous works either fall short in preserving the anatomical ...

Ultra-high-field (7T) MR imaging offers superior resolution and exceptional anatomical details when compared to conventional 3T MRI. However, the current scarcity and higher cost of 7T MRI scanners limit their accessibility in both clinical and ...

Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR ...

Magnetic Resonance Imaging (MRI) is a crucial non-invasive diagnostic tool. The image quality, however, is often limited by k-space under-sampling and noise, which is exacerbated for low-field systems. K-space learning has the potential to support ...

Magnetic resonance imaging (MRI) plays a vital role in brain imaging, offering exceptional soft tissue contrast without the use of ionizing radiation, ensuring safe and effective medical diagnosis. In clinic settings, 2D acquisitions are preferred ...

This paper introduces a Virtual Immunohistochemistry Multiplex staining (VIMs) model designed to generate multiple immunohistochemistry (IHC) stains from a single hematoxylin and eosin (H&E) stained tissue section. IHC stains are crucial in ...

Cortical surface reconstruction utilizing Neural Ordinary Differential Equation (NODE) stands as a prominent method, renowned for generating surfaces of accuracy and robustness. However, these methodologies, tailored predominantly for the adult ...

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have demonstrated strong capabilities in region-of-interest (ROI) detection and disease severity scoring, aiming to reduce the manual workload for ...