AIMC Topic: Image Processing, Computer-Assisted

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Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.

European journal of nuclear medicine and molecular imaging
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...

Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.

Nuclear medicine communications
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...

Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Magma (New York, N.Y.)
OBJECTIVE: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

Journal of X-ray science and technology
OBJECTIVE: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses...

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Scientific reports
Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show tha...

HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification.

Scientific reports
Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a nov...

Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.

Scientific reports
Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonosc...

Accelerating veterinary low field MRI acquisitions using the deep learning based denoising solution HawkAI.

Scientific reports
Magnetic resonance imaging (MRI) has changed veterinary diagnosis but its long-sequence time can be problematic, especially because animals need to be sedated during the exam. Unfortunately, shorter scan times implies a fall in overall image quality ...

Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers.

European radiology experimental
BACKGROUND: We evaluated the acceleration of a three-dimensional isotropic flow-independent magnetic resonance angiography (MRA) (relaxation-enhanced angiography without contrast and triggering, REACT) of neck arteries using compressed SENSE (CS) com...

Multi-task learning for automated contouring and dose prediction in radiotherapy.

Physics in medicine and biology
. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment pl...