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. 2025 Sep;62(3):827-839.
doi: 10.1002/jmri.29777. Epub 2025 Apr 9.

Improved and Automated Detection of Papillary Muscle Infarction Using Joint Bright- and Black-Blood Late Gadolinium Enhancement MRI

Théo Richard  1   2 , Victor de Villedon de Naide  1   2 , Victor Nogues  1 , Thaïs Génisson  1   2 , Kalvin Narceau  1   2 , Kun He  1 , Rabea Klaar  1   3   4 , Baptiste Durand  1   2 , Thibault Boullé  2 , Guillaume Poirot  2 , Soumaya Sridi  2 , Jean-David Maes  2 , Marion Constantin  1 , Kinan Kneizeh  1   5 , Konstantinos Vlachos  1   5 , Guido Caluori  1 , Pierre Jaïs  1   5 , Matthias Stuber  1   6   7 , Hubert Cochet  1   2 , Aurelien Bustin  1   2   6
Affiliations

Improved and Automated Detection of Papillary Muscle Infarction Using Joint Bright- and Black-Blood Late Gadolinium Enhancement MRI

Théo Richard et al. J Magn Reson Imaging. 2025 Sep.

Abstract

Background: Papillary muscle infarction (PMI) has been linked to significantly increased mortality and is associated with ventricular arrhythmias and mitral regurgitation. Reference bright-blood late gadolinium enhancement (LGE) imaging provides poor scar-to-blood contrast, making PMI visualization challenging. Black-blood LGE imaging overcomes this limitation by improving the blood-scar contrast.

Purpose: To evaluate a recent co-registered bright- (papillary muscle localization) and black-blood (PMI visualization) sequence (Scar-specific imaging with Preserved myOcardial visualizaTion: SPOT) to improve PMI visualization compared to a reference standard phase-sensitive inversion recovery (PSIR) sequence, and to enable automated PMI detection (auto-PMI).

Study type: Retrospective.

Population: 198 patients with ischemic heart disease were divided into an optimization dataset (N = 127) and a testing dataset (N = 71).

Field strength/sequence: 2D SPOT and PSIR balanced steady-state free precession sequences at 1.5 T.

Assessment: Auto-PMI included: image acquisition, slice selection, endocardial segmentation, blood pool preprocessing, and PMI detection. Three radiologists (8, 5 and 2 years of MRI experience) assessed PMI in SPOT and PSIR images independently. A consensus reading regarding all assessments of both sequences was established. The number of patients with PMI in SPOT and PSIR acquisitions was compared. The diagnostic performances of visual (SPOT and PSIR) and auto-PMI (SPOT) detection were evaluated. Inter- and intra-observer reproducibility of the visual PMI detection was assessed.

Statistical tests: McNemar test, p-value < 0.05 was considered statistically significant.

Results: In the testing dataset, significantly more patients with PMI were detected using SPOT compared to PSIR in each session (37 vs. 27, 36 vs. 29, 41 vs. 31, 42 vs. 25). Sensitivity ranges for visual PMI detection were significantly higher using SPOT (89%-100% vs. 61%-82%). SPOT vs. PSIR inter- and intra-observer reproducibility ranges were 77%-80% vs. 71%-77%, and 97% vs. 88%, respectively. Auto-PMI sensitivity was 87%.

Data conclusion: Co-registered bright- and black-blood SPOT imaging improved visual PMI detection and facilitated automated PMI assessment.

Evidence level: 3. Technical Efficacy: Stage 2.

Keywords: automated detection; late gadolinium enhancement; papillary muscle infarction; scar‐specific imaging with preserved myocardial visualization.

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Figures

FIGURE 1
FIGURE 1
Automated PMI detection algorithm framework. (A) SPOT acquires 2D co‐registered black‐ and bright‐blood late gadolinium enhancement images in a single acquisition. For each slice position, eight single‐shot images (four black‐blood and four bright‐blood) are acquired and averaged. (B) Both black‐ and bright‐blood slices close to the apex and to the base are excluded according to the total number of slices of the scan. (C) Endocardial contours are segmented on the selected bright‐blood slices and (D) propagated onto corresponding co‐registered black‐blood slices. LV blood pool is then extracted, and a morphological erosion is computed. (E) A pixel intensity‐based criterion, μ, is used to determine if enhancement corresponding to a potential PMI is present. LV = left ventricular; PM = papillary muscle; PI = pixel intensity; PMI = papillary muscle infarction.
FIGURE 2
FIGURE 2
Papillary muscle (PM) ablation experiment on a sheep model. (A) Short‐axis echocardiographic images confirming the ablation catheter (green arrow) in position against the posterior PM (left, white arrow), and the lateral PM (right, white arrow). (B) 3D electroanatomical mapping showing the anatomical shell (left) and voltage map (right) of the left ventricle pre‐ablation (mapping catheter shown). Red, blue and yellow arrows represent the anteroseptal, anterolateral and posterior ablation regions, respectively. (C) Post‐ablation short‐axis MRI images, including the reference PSIR and the proposed SPOT images (black‐blood and colored fusion), displayed at three slice positions. The SPOT technique clearly improves the visualization of PM scars. (D) Histological analysis using Masson's trichrome staining and TTC staining, emphasizing the ablation sites. TTC = triphenyl‐tetrazolium chloride.
FIGURE 3
FIGURE 3
Examples of colored SPOT images (bottom) obtained by merging bright‐blood (second row) and black‐blood (third row) images, compared to reference PSIR imaging (top), in four patients with myocardial infarction. Yellow arrows indicate uncertain papillary muscle enhancements on PSIR imaging, clearly highlighted on SPOT imaging. PSIR = phase‐sensitive inversion recovery; yo = years‐old.
FIGURE 4
FIGURE 4
Comprehensive ventricular comparison between the reference PSIR images (top) and the proposed colored SPOT images (bottom), obtained by combining bright‐blood (second row) and black‐blood (third row) images, in a 64‐year‐old woman with myocardial infarction. PSIR = phase‐sensitive inversion recovery; yo = years‐old.
FIGURE 5
FIGURE 5
Six receiver operating characteristic (ROC) curves obtained by varying the number of morphological erosions applied to the left ventricular blood pool contour in the automated papillary muscle infarction detection algorithm. The ROC with five erosions achieved the highest AUC, with the optimal threshold (detection criterion, μ) determined to be 1.8 at the highest Youden index. AUC = area under the ROC curve.
FIGURE 6
FIGURE 6
Examples of automated PMI detection in four patients with myocardial infarction. Reference standard PSIR images are shown (left). In the SPOT bright‐blood images (second column), the endocardial contour (red) is segmented using a deep learning model. The contour is propagated to the corresponding co‐registered black‐blood image (third column), where a morphological erosion is applied to prevent residual endocardial enhancement (fourth column). The ratio of maximum to mean pixel intensity in the blood pool is then computed (right) and compared to the optimal detection threshold (1.8). PSIR = phase‐sensitive inversion recovery; LV = left ventricular; PM = papillary muscle; PI = pixel intensity; PMI = papillary muscle infarction; yo = years‐old.
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