MRI-Based Pressure Gradient Mapping in Patient-Specific Models of Coarctation of the Aorta
Nair, P.; Ferrari, L.; Loecher, M.; McGrath, C. M.; Castillo Passi, C. A.; Marsden, A. L.; Ennis, D. B.
Show abstract
Purpose: Accurate assessment of the pressure gradient ({Delta}P) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA. Methods: Patient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid-structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities. Results: Conventional 4D-Flow consistently underestimated {Delta}P (slope = 0.63, R2=0.75) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95, R2=0.75). FSI simulations showed the highest overall agreement with catheter-derived {Delta}P (slope = 1.14, R2=0.82). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes). Conclusion: 4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support further in vivo evaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Highly Accelerated Vessel-Selective Arterial Spin Labelling Angiography using Sparsity and Smoothness Constraints 95%
- Deep Learning Based Cardiac Cine Segmentation – Transfer Learning Application to 7T Ultrahigh-Field MRI 93%
- Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling 93%
Similar papers in this journal
- Validation of Left Ventricular High Frame Rate Echo-Particle Image Velocimetry against 4D Flow MRI in Patients 96%
- Strain patterns with ultrasound for improved assessment of abdominal aortic aneurysm vessel wall biomechanics 95%
- Indexing Cerebrovascular Health Using TranscranialDoppler Ultrasound 91%
Similar papers in this journal
- Reproducibility of 4D Flow MRI-based Personalized Cardiovascular Models; Inter-sequence, Intra-observer, and Inter-observer variability 96%
- AngioNet: A Convolutional Neural Network for Vessel Segmentation in X-ray Angiography 94%
- Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT 94%
Similar papers in this journal
- Impact of residual intimal flap displacement post-TEVAR on TBAD haemodynamics in compliant, patient-specific CFD simulations informed by MRI 95%
- Effect of Sinotubular Junction Size on TAVR Leaflet Thrombosis: A Fluid-structure Interaction Analysis 93%
- Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics 92%
Similar papers in this journal
"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.