800 knee MRI studies with radiologist annotations for ACL, meniscus, and cartilage pathology

Overview

We are compiling a musculoskeletal MRI dataset focused on the knee joint to train and validate computer-aided detection models for common sports-medicine and orthopaedic pathologies. Required cases must include patients aged 16–70 presenting with acute or chronic knee symptoms such as instability, locking, or persistent pain, scanned on 1.5T or 3T systems using a dedicated transmit-receive or receive-only knee coil with ≥8 elements. Mandatory sequences per study: sagittal proton density fat-suppressed (PDFS) with slice thickness ≤3 mm, in-plane resolution ≤0.4 × 0.4 mm, TR 2500–4000 ms, and TE 30–40 ms; coronal PDFS with matching in-plane resolution; and axial PDFS or T2 fat-saturated (fat-sat) with slice thickness ≤3 mm. Preferred field strength is 3T, which provides superior cartilage signal-to-noise ratio and spatial resolution compared with 1.5T systems. Optional but highly valued sequences include 3D DESS (dual echo steady state) or 3D MEDIC for quantitative cartilage morphometry and T2 relaxation mapping, and sagittal T2-weighted without fat saturation for bone marrow oedema assessment and subchondral bone characterisation. T2 mapping with multi-echo spin echo acquisition (echo times 10, 20, 30, 40, 50, 60 ms) is desirable for cartilage matrix assessment and will be purchased at a 15% premium per study. Preferred delivery format is DICOM series with original pixel data and unmodified DICOM headers; PAR/REC format from Philips scanners with corresponding XML headers is also accepted. JSON sidecars with sequence parameters including TR, TE, flip angle, bandwidth, and reconstruction matrix are requested for all cases to enable automated sequence classification. Annotation requirements: each case must carry a structured radiology report or structured JSON label file documenting the status of the following anatomical structures — anterior cruciate ligament (ACL: intact, partial tear, or complete tear with retraction measurement in mm), posterior cruciate ligament (PCL: intact or torn), medial meniscus body, anterior horn, and posterior horn (each graded 0–III by signal intensity and tear morphology), lateral meniscus with equivalent grading, medial and lateral compartment articular cartilage (MOAKS score optional but encouraged), and presence of joint effusion with volume estimate where available. Bounding boxes around the primary lesion site on the most diagnostically informative slice are required for all ACL tear-positive and meniscal tear-positive cases. Segmentation masks of the ACL, medial meniscus, and lateral meniscus are optional but will be compensated at a 25% premium over the base case rate. All data must be de-identified following HIPAA Safe Harbor standard with removal of patient name, date of birth, accession number, device serial number, and all other of the 18 specified identifiers; scan acquisition date must be shifted by a consistent per-patient random offset to prevent re-identification via date triangulation. The resulting dataset will be used to develop and independently validate a deep-learning second-reader tool for knee MRI interpretation intended for deployment in community and teleradiology practices where subspecialty musculoskeletal radiologist access is limited. Cases with post-operative hardware artefacts from prior ACL reconstruction or partial meniscectomy must be excluded unless explicitly flagged in metadata. Mixed pathology cases combining ACL tear with concurrent meniscal tear or chondral defect are particularly desirable as training examples for multi-label pathology detection models.

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