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 stan-dard 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 detect -ability 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 repro-ducibility 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.

Palesi, F., Nigri, A., Gianeri, R., Aquino, D., Redolfi, A., Biagi, L., et al. (2022). MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms. PHYSICA MEDICA, 104, 93-100 [10.1016/j.ejmp.2022.10.008].

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms

Raffaele Agati;Raffaele Lodi;Claudia Testa;Caterina Tonon;
2022

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 stan-dard 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 detect -ability 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 repro-ducibility 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.
2022
Palesi, F., Nigri, A., Gianeri, R., Aquino, D., Redolfi, A., Biagi, L., et al. (2022). MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms. PHYSICA MEDICA, 104, 93-100 [10.1016/j.ejmp.2022.10.008].
Palesi, Fulvia; Nigri, Anna; Gianeri, Ruben; Aquino, Domenico; Redolfi, Alberto; Biagi, Laura; Carne, Irene; De Francesco, Silvia; Ferraro, Stefania; ...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/960478
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