Deep correction of breathing-related artifacts in real-time MR-thermometry. (January 2021)

• Record Type: Journal Article
• Authors: de Senneville, B. Denis; Coupé, P.; Ries, M.; Facq, L.; Moonen, C.T.W.
• Is Part Of: Computerized medical imaging and graphics. Volume 87(2021)
• Publication Date: 2021-01
• Subjects: Interventional procedures -- MR-thermometry -- Motion artifacts -- Deep neural network -- Real-time systems; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101834
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Deep learning is used for on-line correction of motion artifacts in MR-thermometry A convolutional neural network (CNN) learns the apparent temperature perturbation The proposed approach is evaluated on the liver of 12 healthy volunteers The proposed method is evaluated on a heating experiment performed on a porcine liver The proposed method is compared to two frequently employed multi-baseline strategies Abstract: Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance. In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR-thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map. The method's artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure.