Automated low‐contrast pattern recognition algorithm for magnetic resonance image quality assessment. Issue 8 (30th June 2017)

• Record Type: Journal Article
• Authors: Ehman, Morgan O.; Bao, Zhonghao; Stiving, Scott O.; Kasam, Mallik; Lanners, Dianna; Peterson, Teresa; Jonsgaard, Renee; Carter, Rickey; McGee, Kiaran P.
• Is Part Of: Medical physics. Volume 44:Issue 8(2017)
• Page Start: 4009
• Page End: 4024
• Publication Date: 2017-06-30
• Subjects: American College of Radiology -- automated detection -- fuzzy logic -- low contrast resolution -- magnetic resonance imaging -- quality control; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12370
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Low contrast (LC) detectability is a common test criterion for diagnostic radiologic quality control (QC) programs. Automation of this test is desirable in order to reduce human variability and to speed up analysis. However, automation is challenging due to the complexity of the human visual perception system and the ability to create algorithms that mimic this response. This paper describes the development and testing of an automated LC detection algorithm for use in the analysis of magnetic resonance (MR) images of the American College of Radiology (ACR) QC phantom. Methods: The detection algorithm includes fuzzy logic decision processes and various edge detection methods to quantify LC detectability. Algorithm performance was first evaluated using a single LC phantom MR image with the addition of incremental zero mean Gaussian noise resulting in a total of 200 images. A c‐statistic was calculated to determine the role of CNR to indicate when the algorithm would detect ten spokes. To evaluate inter‐rater agreement between experienced observers and the algorithm, a blinded observer study was performed on 196 LC phantom images acquired from nine clinical MR scanners. The nine scanners included two MR manufacturers and two field strengths (1.5 T, 3.0 T). Inter‐rater and algorithm‐rater agreement was quantified using Krippendorff's alpha. Results: For the Gaussian noise added data, CNR ranged from 0.519 to 11.7 with CNR being considered an excellent discriminator of algorithm performance (c‐statistic = 0.9777). Reviewer scoring of the clinical phantom data resulted in an inter‐rater agreement of 0.673 with the agreement between observers and algorithm equal to 0.652, both of which indicate significant agreement. Conclusions: This study demonstrates that the detection of LC test patterns for MR imaging QC programs can be successfully developed and that their response can model the human visual detection system of expert MR QC readers.