MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms. (December 2022)

• Record Type: Journal Article
• Authors: Palesi, Fulvia; Nigri, Anna; Gianeri, Ruben; Aquino, Domenico; Redolfi, Alberto; Biagi, Laura; Carne, Irene; De Francesco, Silvia; Ferraro, Stefania; Martucci, Paola; Paul Medina, Jean; Napolitano, Antonio; Pirastru, Alice; Baglio, Francesca; Tagliavini, Fabrizio; Grazia Bruzzone, Maria; Tosetti, Michela; Gandini Wheeler-Kingshott, Claudia A.M.
• Is Part Of: Physica medica. Volume 104(2023)
• Page Start: 93
• Page End: 100
• Publication Date: 2022-12
• Subjects: ACR -- Quality control -- Multisite; qMRI quantitative magnetic resonance imaging -- ACR American College of Radiology; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2022.10.008
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Optimization of a quality control routine across sites in a 3T multicentric study. More than 80% of ACR quality control measures were within the acceptance ranges. All measures demonstrated good reproducibility over time. Inter-vendor differences were observed in the uniformity and geometric metrics. The automated analysis allows each site to independently monitor scanner performance. Abstract: Purpose: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. Methods: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. Results: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). Conclusions: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.