AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 5871 to 5880 of 6780 articles

Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging.

Computer methods and programs in biomedicine
OBJECTIVES: Achieving efficient analysis of multiple pathological indicators has great significance for breast cancer prognosis and therapeutic decision-making. In this study, we aim to explore a deep multi-task learning (MTL) framework for collabora...

Deep implicit optimization enables robust learnable features for deformable image registration.

Medical image analysis
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, existing DLIR methods forego many of the benefi...

Optimizing clinical risk stratification of localized prostate cancer.

Current opinion in urology
PURPOSE OF REVIEW: To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value.

Magnetic resonance image generation using enhanced TransUNet in temporomandibular disorder patients.

Dento maxillo facial radiology
OBJECTIVES: Temporomandibular disorder (TMD) patients experience a variety of clinical symptoms, and MRI is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep l...

SHFormer: Dynamic spectral filtering convolutional neural network and high-pass kernel generation transformer for adaptive MRI reconstruction.

Neural networks : the official journal of the International Neural Network Society
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between regions from distant pixel neighborhoods to compute feature representations. Accelerated magnetic resonance image (MRI) reconst...

Machine learning-based multi-pool Voigt fitting of CEST, rNOE, and MTC in Z-spectra.

Magnetic resonance in medicine
PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-po...

Hybrid strategy of coronary atherosclerosis characterization with T1-weighted MRI and CT angiography to non-invasively predict periprocedural myocardial injury.

European heart journal. Cardiovascular Imaging
AIMS: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after percutaneous coronary intervention (PCI). We aimed to investigate whether integrating MRI with CCTA, u...

[Cross modal translation of magnetic resonance imaging and computed tomography images based on diffusion generative adversarial networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translati...