500 cardiac MRI studies with cine function analysis and late gadolinium enhancement for cardiomyopathy classification

Overview

We are seeking a high-quality cardiac MRI (CMR) dataset for training and validating deep-learning models for automated biventricular segmentation, ejection fraction estimation, and myocardial fibrosis and scar burden quantification targeting clinical deployment in heart failure and cardiomyopathy care pathways. Required studies must originate from patients with confirmed or clinically suspected cardiomyopathy including dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), ischaemic cardiomyopathy (ICM), arrhythmogenic right ventricular cardiomyopathy (ARVC), or cardiac amyloidosis, as well as age-matched healthy volunteer controls, scanned on 1.5T or 3T scanners using ECG-triggered breath-hold or navigator-gated respiratory-compensated acquisition. Mandatory sequences per study: a short-axis cine balanced steady-state free precession (bSSFP) stack covering the full left and right ventricle from the atrioventricular plane to the apex in contiguous slices (slice thickness 8–10 mm, inter-slice gap 0–2 mm, temporal resolution ≤45 ms per cardiac phase with ≥25 cardiac phases reconstructed per slice, in-plane resolution ≤1.5 × 1.5 mm); long-axis cine views in two-chamber, three-chamber, and four-chamber orientations using identical bSSFP parameters; and phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) sequences acquired 10–15 minutes following intravenous gadolinium injection at 0.1–0.2 mmol/kg standard extracellular agent, with inversion time (TI) determined individually per patient using a TI scout sequence to null normal myocardium (typically 240–320 ms at 1.5T and 280–360 ms at 3T), co-localised to the short-axis cine stack with matching slice positions. T1 mapping using MOLLI (5(3)3 scheme) or ShMOLLI acquisition both natively pre-contrast and post-contrast at 15 minutes for extracellular volume (ECV) fraction computation is optional but highly desirable and compensated at a 15% per-study premium. T2 mapping sequences using T2-prepared bSSFP with at least three echo times are also optional and add myocardial oedema characterisation capability. Delivery must be in DICOM format with complete sequence metadata including inversion time, flip angle, TR, TE, and temporal resolution intact in the DICOM header; NIfTI-2 with JSON sidecars is accepted as an additional format and is required when the institution uses a BIDS-compatible CMR archive. Annotation requirements include endocardial and epicardial contours of the left ventricle (LV) and right ventricle (RV) at end-diastole and end-systole on all short-axis cine slices from base to apex, generated by a trained cardiac sonographer or CMR technologist and verified by a board-certified cardiac radiologist or cardiologist with ≥3 years of CMR reading experience, sufficient to compute LV ejection fraction (LVEF), LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV myocardial mass, RV end-diastolic volume (RVEDV), and RV ejection fraction (RVEF). Contours must be delivered as segmentation masks in NIfTI format or as polygon vertex coordinates in JSON. LGE burden must be annotated as a binary segmentation mask of LGE-positive scar versus healthy non-enhancing myocardium on the short-axis LGE stack, generated using a combination of semi-automated thresholding (6-SD above remote myocardium) and manual adjudication. LGE spatial pattern must be classified per study as ischaemic (subendocardial or transmural, coronary territory distribution) or non-ischaemic (midwall, epicardial, insertion point RV, or diffuse) to support cardiomyopathy aetiology classification downstream. Required clinical metadata: patient age, sex, body surface area, primary diagnosis code, NYHA functional class, LVEF from the clinical CMR report, NT-proBNP or BNP level if available, and presence of implantable cardiac device (pacemaker or ICD); cases with severe susceptibility artefact from device leads obscuring ≥20% of myocardial segments must be excluded from the training split. All imaging data must be fully de-identified per DICOM PS3.15 confidentiality profile with removal of patient name, birth date, accession number, and institution name; any incidentally acquired head MRI slices at the top of the short-axis stack that reveal facial structure must be defaced prior to delivery. This dataset will underpin a regulatory-grade CMR analysis platform targeting cardiologist adoption across European heart failure and cardiac imaging centres, with a planned CE marking submission under MDR 2017/745 as Class IIa medical device software and subsequent FDA 510(k) clearance for the North American market. Scanner vendor diversity across Siemens Magnetom, GE SIGNA, and Philips Ingenia platforms at both 1.5T and 3T is required with no single vendor exceeding 40% of the total study count.

Progress

Data Specifications

Use Cases

• Training and validating Medical imaging AI/ML models
• Benchmarking Medical imaging detection and segmentation algorithms
• Building de-identified Medical imaging research datasets for academic studies
• Augmenting existing Medical imaging datasets to reduce class imbalance