Nonbinary quantification technique accounting for myocardial infarct heterogeneity: Feasibility of applying percent infarct mapping in patients. Issue 3 (15th February 2018)

• Record Type: Journal Article
• Authors: Mastrodicasa, Domenico; Elgavish, Gabriel A.; Schoepf, U. Joseph; Suranyi, Pal; van Assen, Marly; Albrecht, Moritz H.; De Cecco, Carlo N.; van der Geest, Rob J.; Hardy, Rayphael; Mantini, Cesare; Griffith, L. Parkwood; Ruzsics, Balazs; Varga‐Szemes, Akos
• Is Part Of: Journal of magnetic resonance imaging. Volume 48:Issue 3(2018)
• Page Start: 788
• Page End: 798
• Publication Date: 2018-02-15
• Subjects: myocardial infarct quantification -- myocardial infarct heterogeneity -- T1 mapping -- late gadolinium enhancement -- magnetic resonance imaging -- percent infarct mapping; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25973
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Binary threshold‐based quantification techniques ignore myocardial infarct (MI) heterogeneity, yielding substantial misquantification of MI. Purpose: To assess the technical feasibility of MI quantification using percent infarct mapping (PIM), a prototype nonbinary algorithm, in patients with suspected MI. Study Type: Prospective cohort Population: Patients ( n = 171) with suspected MI referred for cardiac MRI. Field Strength/Sequence: Inversion recovery balanced steady‐state free‐precession for late gadolinium enhancement (LGE) and modified Look–Locker inversion recovery (MOLLI) T1 ‐mapping on a 1.5T system. Assessment: Infarct volume (IV) and infarct fraction (IF) were quantified by two observers based on manual delineation, binary approaches (2–5 standard deviations [SD] and full‐width at half‐maximum [FWHM] thresholds) in LGE images, and by applying the PIM algorithm in T1 and LGE images (PIMT1 ; PIMLGE ). Statistical Test: IV and IF were analyzed using repeated measures analysis of variance (ANOVA). Agreement between the approaches was determined with Bland–Altman analysis. Interobserver agreement was assessed by intraclass correlation coefficient (ICC) analysis. Results: MI was observed in 89 (54.9%) patients, and 185 (38%) short‐axis slices. IF with 2, 3, 4, 5SDs and FWHM techniques were 15.7 ± 6.6, 13.4 ± 5.6, 11.6 ± 5.0, 10.8 ± 5.2, and 10.0 ± 5.2%, respectively. The 5SD and FWHM techniques had the best agreement with manual IF (9.9 ± 4.8%) determination (bias 1.0 and 0.2%; P = 0.1426 and P = 0.8094, respectively). The 2SD and 3SD algorithms significantly overestimated manual IF (9.9 ± 4.8%; both P < 0.0001). PIMLGE measured significantly lower IF (7.8 ± 3.7%) compared to manual values ( P < 0.0001). PIMLGE, however, showed the best agreement with the PIMT1 reference (7.6 ± 3.6%, P = 0.3156). Interobserver agreement was rated good to excellent for IV (ICCs between 0.727–0.820) and fair to good for IF (0.589–0.736). Data Conclusion: The application of the PIMLGE technique for MI quantification in patients is feasible. PIMLGE, with its ability to account for voxelwise MI content, provides significantly smaller IF than any thresholding technique and shows excellent agreement with the T1 ‐based reference. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:788–798.