Six-Minute Knee MRI: A Comparison of Novel Approaches for Accelerated Imaging
Goyal, A.; MacKay, J.; Petterson, M.; van der Heijden, R.; Stevens, K.; Yoon, M.; Liang, T.; Chaudhari, A.; Kogan, F.
Show abstract
BackgroundAccelerated knee MRI protocols using deep learning (DL)-based reconstruction, 3D acquisitions, and parallel imaging can significantly reduce scan times. These methods enhance patient throughput and comfort while maintaining diagnostic quality. However, their clinical efficacy and diagnostic performance across various knee pathologies requires systematic evaluation. PurposeTo evaluate the diagnostic performance and image quality of four accelerated knee MRI protocols ([~]6 minutes) compared with the standard conventional 2D FSE protocol ([~]15-30 minutes). Materials and MethodsThis prospective study enrolled adults with symptomatic knee pain between May 2021 and March 2022. All participants underwent five knee MRI protocols: a conventional 2D fast spin echo (FSE) protocol, a 2D FSE protocol with DL reconstruction, a 3D Cube protocol, a 3D quantitative double-echo steady-state (qDESS) protocol, and a thin-slice 2D FSE protocol. Five radiologists evaluated pathologies in joint tissues. Inter-reader and inter-method agreements were assessed using Gwets AC, with sensitivity and specificity calculated. Image diagnostic quality was evaluated using a Likert scale, and the Friedman was test used (p<0.05 indicative of statistically significant difference). ResultsA total of 32 participants were evaluated (14 men, aged 47{+/-}16 years). Of 160 total scans, 12 were excluded due to motion artifacts. Across pathologies, the 3D qDESS protocol had the highest inter-reader agreement for menisci (0.86) and cartilage (0.48), with sensitivities of 95% and 83%, respectively. The 2D DL protocol showed strong performance for bone marrow (sensitivity: 78%). Lastly, for effusion, both the 2D DL and 3D Cube protocols exhibited high inter-reader agreement (0.89 and 0.87, respectively). Diagnostic image quality scores exceeded were diagnostically acceptable in 95% of evaluations for menisci, bone marrow, and ligaments. ConclusionThe 2D DL, 3D Cube, and 3D qDESS protocols matched the diagnostic performance of the conventional protocol while reducing scan times to six minutes, improving workflow and patient experience.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MRI-derived Articular Cartilage Strains Predict Patient-Reported Outcomes Six Months Post Anterior Cruciate Ligament Reconstruction 96%
- MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography 94%
- Multiscale Correlations between Joint and Tissue-Specific Biomechanics and Anatomy in Postmortem Ovine Stifles 93%
Similar papers in this journal
- Real-time three-dimensional MRI for the assessment of dynamic carpal instability 96%
- pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage 93%
- Investigating the relationship between internal spinal alignment and back shape in patients with scoliosis using PCdare: a comparative, reliability and validation study 93%
Similar papers in this journal
- AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI): a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan 91%
- Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI using Reduced Field-of-View Echo-Planar Imaging and Enhanced Reverse Polarity Gradient Method 91%
- Pancreas MRI segmentation into head, body, and tail enables regional quantitative analysis of heterogeneous disease 90%
Similar papers in this journal
- Simulated Diagnostic Performance of Ultra-Low-Field MRI: Harnessing Open-Access Datasets to Evaluate Novel Devices 91%
- Increased Brain Volumetric Measurement Precision from Multi-Site 3D T1-weighted 3T Magnetic Resonance Imaging by Correcting Geometric Distortions 91%
- Liver Volumetry from Magnetic Resonance Images with Convolutional Neural Networks 91%
Similar papers in this journal
- Pairwise learning of MRI scans using a convolutional Siamese network for prediction of knee pain 96%
- Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks 92%
- Assessing GPT-4 Multimodal Performance in Radiological Image Analysis 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.