AIMC Topic: Magnetic Resonance Imaging

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Emergence and organization of adult brain function throughout child development.

NeuroImage
Adult cognitive neuroscience has guided the study of human brain development by identifying regions associated with cognitive functions at maturity. The activity, connectivity, and structure of a region can be compared across ages to characterize the...

Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders.

IEEE transactions on medical imaging
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convoluti...

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging of...

Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Comprehensive dosimetric analysis is required prior to the clinical implementation of pelvic MR-only sites, other than prostate, due to the limited number of site specific synthetic-CT (sCT) dosimetric assessments in the liter...

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic F-FDG PET Brain Studies.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic F-FDG brain studies. Ten he...

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

Medical physics
BACKGROUND AND PURPOSE: Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric mag...

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network.

Physics in medicine and biology
Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual di...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Physics in medicine and biology
Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatm...