Browsing by Subject "Quantitative MRI"

Now showing 1 - 3 of 3
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    A composite score using quantitative magnetic resonance cholangiopancreatography predicts clinical outcomes in primary sclerosing cholangitis
    (Elsevier, 2023-06-29) Vuppalanchi, Raj; Are, Vijay; Telford, Alison; Young, Liam; Mouchti, Sofia; Ferreira, Carlos; Kettler, Carla; Gromski, Mark; Akisik, Fatih; Chalasani, Naga; Radiology and Imaging Sciences, School of Medicine
    Background & aims: Magnetic resonance cholangiopancreatography (MRCP) for evaluation of biliary disease currently relies on subjective assessment with limited prognostic value because of the lack of quantitative metrics. Artificial intelligence-enabled quantitative MRCP (MRCP+) is a novel technique that segments biliary anatomy and provides quantitative biliary tree metrics. This study investigated the utility of MRCP+ as a prognostic tool for the prediction of clinical outcomes in primary sclerosing cholangitis (PSC). Methods: MRCP images of patients with PSC were post-processed using MRCP+ software. The duration between the MRCP and clinical event (liver transplantation or death) was calculated. Survival analysis and stepwise Cox regression were performed to investigate the optimal combination of MRCP+ metrics for the prediction of clinical outcomes. The resulting risk score was validated in a separate validation cohort and compared with an existing prognostic score (Mayo risk score). Results: In this retrospective study, 102 patients were included in a training cohort and a separate 50 patients formed a validation cohort. Between the two cohorts, 34 patients developed clinical outcomes over a median duration of 3 years (23 liver transplantations and 11 deaths). The proportion of bile ducts with diameter 3-5 mm, total bilirubin, and aspartate aminotransferase were independently associated with transplant-free survival. Combined as a risk score, the overall discriminative performance of the MRCP+ risk score (M+BA) was excellent; area under the receiver operator curve 0.86 (95% CI: 0.77, 0.95) at predicting clinical outcomes in the validation cohort with a hazard ratio 5.8 (95% CI: 1.5, 22.1). This was superior to the Mayo risk score. Conclusions: A composite score combining MRCP+ with total bilirubin and aspartate aminotransferase (M+BA) identified PSC patients at high risk of liver transplantation or death. Prospective studies are warranted to evaluate the clinical utility of this novel prognostic tool. Impact and implications: Primary sclerosis cholangitis (PSC) is a disease of the biliary tree where inflammation and fibrosis cause areas of narrowing (strictures) and expansion (dilatations) within the biliary ducts leading to liver failure and/or cancer (cholangiocarcinoma). In this study, we demonstrate that quantitative assessment of the biliary tree can better identify patients with PSC who are at high risk of either death or liver transplantation than a current blood-based risk score (Mayo risk score).
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    Accelerated model‐based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation
    (Wiley, 2025) Huang, Shuai; Lah, James J.; Allen, Jason W.; Qiu, Deqiang; Radiology and Imaging Sciences, School of Medicine
    Purpose: To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. Theory: We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. Methods: We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the l1 norm minimization, PICS and other model-based approaches such as GraSP, MOBA. Results: Compared to conventional compressed sensing approaches such as the l1 -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T*2 mapping and comparable performance in T1 and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. Conclusion: AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.
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    Noninvasive Assessment of Osteoarthritis Severity in Human Explants by Multicontrast MRI
    (Wiley, 2014) Griebel, Adam J.; Trippel, Stephen B.; Emery, Nancy C.; Neu, Corey P.; Orthopaedic Surgery, School of Medicine
    Purpose: Medical imaging has the potential to noninvasively diagnose early disease onset and monitor the success of repair therapies. Unfortunately, few reliable imaging biomarkers exist to detect cartilage diseases before advanced degeneration in the tissue. Method: In this study, we quantified the ability to detect osteoarthritis (OA) severity in human cartilage explants using a multicontrast magnetic resonance imaging (MRI) approach, inclusive of novel displacements under applied loading by MRI, relaxivity measures, and standard MRI. Results: Displacements under applied loading by MRI measures, which characterized the spatial micromechanical environment by 2D finite and Von Mises strains, were strong predictors of histologically assessed OA severity, both before and after controlling for factors, e.g., patient, joint region, and morphology. Relaxivity measures, sensitive to local macromolecular weight and composition, including T1ρ, but not T1 or T2, were predictors of OA severity. A combined multicontrast approach that exploited spatial variations in tissue biomechanics and extracellular matrix structure yielded the strongest relationships to OA severity. Conclusion: Our results indicate that combining multiple MRI-based biomarkers has high potential for the noninvasive measurement of OA severity and the evaluation of potential therapeutic agents used in the treatment of early OA in animal and human trials.