Dataset Information

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

ABSTRACT:

Background

Magnetic resonance cholangiopancreatography (MRCP) is an important tool for noninvasive imaging of biliary disease, however, its assessment is currently subjective, resulting in the need for objective biomarkers.

Purpose

To investigate the accuracy, scan/rescan repeatability, and cross-scanner reproducibility of a novel quantitative MRCP tool on phantoms and in vivo. Additionally, to report normative ranges derived from the healthy cohort for duct measurements and tree-level summary metrics.

Study type

Prospective.

Phantoms/subjects

Phantoms: two bespoke designs, one with varying tube-width, curvature, and orientation, and one exhibiting a complex structure based on a real biliary tree. Subjects Twenty healthy volunteers, 10 patients with biliary disease, and 10 with nonbiliary liver disease.

Sequence/field strength

MRCP data were acquired using heavily T₂ -weighted 3D multishot fast/turbo spin echo acquisitions at 1.5T and 3T.

Assessment

Digital instances of the phantoms were synthesized with varying resolution and signal-to-noise ratio. Physical 3D-printed phantoms were scanned across six scanners (two field strengths for each of three manufacturers). Human subjects were imaged on four scanners (two fieldstrengths for each of two manufacturers).

Statistical tests

Bland-Altman analysis and repeatability coefficient (RC).

Results

Accuracy of the diameter measurement approximated the scanning resolution, with 95% limits of agreement (LoA) from -1.1 to 1.0 mm. Excellent phantom repeatability was observed, with LoA from -0.4 to 0.4 mm. Good reproducibility was observed across the six scanners for both phantoms, with a range of LoA from -1.1 to 0.5 mm. Inter- and intraobserver agreement was high. Quantitative MRCP detected strictures and dilatations in the phantom with 76.6% and 85.9% sensitivity and 100% specificity in both. Patients and healthy volunteers exhibited significant differences in metrics including common bile duct (CBD) maximum diameter (7.6 mm vs. 5.2 mm P = 0.002), and overall biliary tree volume 12.36 mL vs. 4.61 mL, P = 0.0026).

Data conclusion

The results indicate that quantitative MRCP provides accurate, repeatable, and reproducible measurements capable of objectively assessing cholangiopathic change. Evidence Level: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:807-820.

SUBMITTER: Goldfinger MH

PROVIDER: S-EPMC7496952 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

Goldfinger Marc H MH Ridgway Gerard R GR Ferreira Carlos C Langford Caitlin R CR Cheng Lin L Kazimianec Arina A Borghetto Andrea A Wright Thomas G TG Woodward Gary G Hassanali Neelam N Nicholls Rowan C RC Simpson Hayley H Waddell Tom T Vikal Siddarth S Mavar Marija M Rymell Soubera S Wigley Ioan I Jacobs Jaco J Kelly Matt M Banerjee Rajarshi R Brady J Michael JM

Journal of magnetic resonance imaging : JMRI 20200308 3

<h4>Background</h4>Magnetic resonance cholangiopancreatography (MRCP) is an important tool for noninvasive imaging of biliary disease, however, its assessment is currently subjective, resulting in the need for objective biomarkers.<h4>Purpose</h4>To investigate the accuracy, scan/rescan repeatability, and cross-scanner reproducibility of a novel quantitative MRCP tool on phantoms and in vivo. Additionally, to report normative ranges derived from the healthy cohort for duct measurements and tree- ...[more]

PMID: 32147892

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Dataset Information

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

Background

Purpose

Study type

Phantoms/subjects

Sequence/field strength

Assessment

Statistical tests

Results

Data conclusion

Publications

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

Similar Datasets

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