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Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous,…
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or…
Infrared and visible image fusion aims at generating a fused image containing the intensity and detail information of source images, and the key issue is effectively measuring and integrating the complementary information of multi-modality…
Multimodal medical image fusion integrates complementary information from different imaging modalities to enhance diagnostic accuracy and treatment planning. While deep learning methods have advanced performance, existing approaches face…
Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread…
Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However,…
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimization-based models suffer several limitations: strict…
Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases. For example, in clinical cervical cancer radiotherapy, after acquiring subabdominal MRI images,…
Accurate brain tumor segmentation using multiparametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquisition of all MRI sequences is not always possible. The absence of certain MRI modalities…
Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and…
Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor…
Magnetic Resonance Imaging (MRI) field-strength enhancement holds immense value for both clinical diagnostics and advanced research. However, existing methods typically focus on isolated enhancement tasks, such as specific 64mT-to-3T or…
This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness. Aiming at the complex anatomical structure of spinal images, this paper introduces a…
Remote sensing image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral data and a low-resolution image rich in spectral information. Current deep learning (DL)…
Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a diffcult task due to different types of brain abnormalities. Using information and features from multimodal MRI including T1, T1-weighted inversion recovery…
Multi-focus image fusion is a challenging field of study that aims to provide a completely focused image by integrating focused and un-focused pixels. Most existing methods suffer from shift variance, misregistered images, and…
Accurate brain tumor segmentation is crucial for neuro-oncology diagnosis and treatment planning. Deep learning methods have made significant progress, but automatic segmentation still faces challenges, including tumor morphological…
The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information from multiple sources and adapt to the heterogeneous nature…
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. The combination of a specified set of modalities, which is selected depending on…
We propose a novel method for adjusting luminance for multi-exposure image fusion. For the adjustment, two novel scene segmentation approaches based on luminance distribution are also proposed. Multi-exposure image fusion is a method for…