AIMC Topic: Artifacts

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MC-RED: A deep learning network for motion correction in 3D CEST imaging.

Magnetic resonance in medicine
PURPOSE: Chemical exchange saturation transfer (CEST) imaging is highly sensitive to patient motion, which can compromise the reliability of quantitative molecular analysis. This study aims to develop and validate a deep learning-based motion correct...

AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.

European radiology
OBJECTIVES: Artifacts in clinical MRI can compromise the performance of AI models. This study evaluates how different data augmentation strategies affect an AI model's segmentation performance under variable artifact severity.

CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...

From Faster Frames to Flawless Focus: Deep Learning HASTE in Postoperative Single Sequence MRI.

Academic radiology
BACKGROUND: This study evaluates the feasibility of a novel deep learning-accelerated half-fourier single-shot turbo spin-echo sequence (HASTE-DL) compared to the conventional HASTE sequence (HASTE) in postoperative single-sequence MRI for the detect...

Development of an anomaly detection system for Gibbs artifact identification in amyloid PET imaging.

Radiological physics and technology
The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom. This visual evaluation requires observation of the entire image of the phantom and confirmation of ...

Deep Learning CAIPIRINHA-VIBE Improves and Accelerates Head and Neck MRI.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate image quality for contrast-enhanced (CE) neck MRI with a deep learning-reconstructed VIBE sequence with acceleration factors (AF) 4 (DL4-VIBE) and 6 (DL6-VIBE).

Denoising of high-resolution 3D UTE-MR angiogram data using lightweight and efficient convolutional neural networks.

Magnetic resonance imaging
High-resolution magnetic resonance angiography (∼ 50 μm MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its...

Advancement of an automatic segmentation pipeline for metallic artifact removal in post-surgical ACL MRI.

Magnetic resonance imaging
Magnetic resonance imaging (MRI) has the potential to identify post-operative risk factors for re-tearing an anterior cruciate ligament (ACL) using a combination of imaging signal intensity (SI) and cross-sectional area measurements of the healing AC...

Deep Learning-Based Saturation Compensation for High Dynamic Range Multispectral Fluorescence Lifetime Imaging.

IEEE transactions on bio-medical engineering
In multispectral fluorescence lifetime imaging (FLIm), achieving consistent imaging quality across all spectral channels is crucial for accurately identifying a wide range of fluorophores. However, these essential measurements are frequently compromi...