AIMC Topic: Magnetic Resonance Imaging

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Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.

Computer methods and programs in biomedicine
BACKGROUND: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based com...

Residual dense network for medical magnetic resonance images super-resolution.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: High-resolution magnetic resonance images (MRI) help experts to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI. Furthermore, image super-resolution technology based on deep learning...

A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation.

IEEE transactions on cybernetics
The fuzzy C -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) durin...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

Magma (New York, N.Y.)
BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of ob...

Scale- and Slice-aware Net (S aNet) for 3D segmentation of organs and musculoskeletal structures in pelvic MRI.

Magnetic resonance in medicine
PURPOSE: MRI of organs and musculoskeletal structures in the female pelvis presents a unique display of pelvic anatomy. Automated segmentation of pelvic structures plays an important role in personalized diagnosis and treatment on pelvic structures d...

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

Radiological physics and technology
In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation...

[Importance of conventional pancreas imaging: ultrasound, CT, and MRI. Facts and possibilities].

Magyar onkologia
Modern imaging procedures, including CT and MR diagnostics, play a significant role in recognizing, characterizing, determining the extent of pancreatic tumor lesions, assessing their operability, and evaluating response to treatment and patient foll...

Domain knowledge augmentation of parallel MR image reconstruction using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is presented that incorporates domain knowledge of parallel MR imaging to augment the DL networks for accurate and stable image reconstruction. The proposed DL method employs...

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimen...

A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper present...