AIMC Topic: Artifacts

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Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Artificial intelligence in medicine
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detec...

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

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: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method...

Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

NeuroImage
Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications...

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance.

International journal of computer assisted radiology and surgery
PURPOSE: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides ...

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

Physics in medicine and biology
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, m...

Motion correction of respiratory-gated PET images using deep learning based image registration framework.

Physics in medicine and biology
Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory...

Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact-free and denoised images.

Magnetic resonance in medicine
PURPOSE: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected maps from multi-gradient recalled echo (mGRE) MRI data.

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

International journal of computer assisted radiology and surgery
PURPOSE: This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoper...