What is the clinical evidence from this study?

Study design: Observational. Population: Ischemic heart disease (n=450). Intervention: SPOT imaging with AI-powered automated analysis vs. Conventional bright-blood PSIR LGE imaging. Primary outcome: Detection of scarred myocardial segments (out of 800 total segments) (p=0.018).

December 17, 2025Open Access

AI-powered SPOT imaging for enhanced myocardial scar detection and quantification

Key Result

AI-powered SPOT imaging increased the detection of scarred myocardial segments by 11.2% compared to conventional PSIR imaging and enabled rapid, fully automated quantification of myocardial injury.

Study Design

Type

Observational (n=450)

Multicenter

Structured PICO

Does AI-powered SPOT imaging improve the detection and quantification of myocardial scar in animal models and patients with heart disease?

Population

Simulations, animal infarct models, and patients with heart disease

Intervention

SPOT (multi-spectral bright- and black-blood imaging sequence) integrated with an artificial intelligence (AI) framework

Comparator

Bright-blood late gadolinium enhanced MRI (current gold standard)

Outcome

Detection and quantification of myocardial injury/scarsurrogate

AI-powered SPOT imaging provides a novel, automated approach for accurate and operator-independent quantification of myocardial scar, potentially improving the assessment of ischemic heart disease.

Main Result

Absolute Event Rate: 466% vs 419%

p-value: p=0.018

Limitations

Reliance on breath-holding introduces susceptibility to motion artifacts
Limitation of 2D acquisitions may lead to potential lesion underestimation
Single-center, single-vendor, and single magnetic field strength (1.5-T) design
Exclusive focus on ischemic patients limits applicability to nonischemic cardiomyopathies
Automated segmentation algorithms trained on 1.5-T datasets may require retraining for 3.0-T systems

Abstract

Cardiovascular disease is the leading global cause of death, underscoring the need for accurate assessment of myocardial injury. The current gold standard, bright-blood late gadolinium enhanced MRI, suffers from poor contrast at the blood-scar interface, reducing sensitivity for subendocardial scar detection and limiting reproducibility. Moreover, reliance on expert manual analysis makes interpretation labor-intensive and variable. Here, we present SPOT, a multi-spectral bright- and black-blood imaging sequence that provides unprecedented scar-to-blood contrast and clear anatomical detail. Integrated with an artificial intelligence (AI) framework for automated image analysis, SPOT enables rapid, fully automated, and operator-independent quantification of myocardial injury. Validated in simulations, animal infarct models, and patients with heart disease, this combined imaging-AI platform delivers accurate detection and quantification in a single acquisition. This innovation presents significant opportunities for earlier diagnosis and enhanced therapeutic management of ischemic heart disease, with potential applications in a wide spectrum of other clinical settings.

Bookmark

View Full Paper