Dataset Information

Effective Study: Development and Application of a Question-Driven, Time-Effective Cardiac Magnetic Resonance Scanning Protocol.

ABSTRACT: Background Long scanning times impede cardiac magnetic resonance (CMR) clinical uptake. A "one-size-fits-all" shortened, focused protocol (eg, only function and late-gadolinium enhancement) reduces scanning time and costs, but provides less information. We developed 2 question-driven CMR and stress-CMR protocols, including tailored advanced tissue characterization, and tested their effectiveness in reducing scanning time while retaining the diagnostic performances of standard protocols. Methods and Results Eighty three consecutive patients with cardiomyopathy or ischemic heart disease underwent the tailored CMR. Each scan consisted of standard cines, late-gadolinium enhancement imaging, native T1-mapping, and extracellular volume. Fat/edema modules, right ventricle cine, and in-line quantitative perfusion mapping were performed as clinically required. Workflow was optimized to avoid gaps. Time target was <30 minutes for a CMR and <35 minutes for a stress-CMR. CMR was considered impactful when its results drove changes in diagnosis or management. Advanced tissue characterization was considered impactful when it changed the confidence level in the diagnosis. The quality of the images was assessed. A control group of 137 patients was identified among scans performed before February 2020. Compared with standard protocols, the average scan duration dropped by >30% (CMR: from 42±8 to 28±6 minutes; stress-CMR: from 50±10 to 34±6 minutes, both P<0.0001). Independent on the protocol, CMR was impactful in ≈60% cases, and advanced tissue characterization was impactful in >45% of cases. Quality grading was similar between the 2 protocols. Tailored protocols did not require additional staff. Conclusions Tailored CMR and stress-CMR protocols including advanced tissue characterization are accurate and time-effective for cardiomyopathies and ischemic heart disease.

SUBMITTER: Torlasco C

PROVIDER: S-EPMC9075206 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effective Study: Development and Application of a Question-Driven, Time-Effective Cardiac Magnetic Resonance Scanning Protocol.

Torlasco Camilla C Castelletti Silvia S Soranna Davide D Volpato Valentina V Figliozzi Stefano S Menacho Katia K Cernigliaro Franco F Zambon Antonella A Kellman Peter P Moon James C JC Badano Luigi P LP Parati Gianfranco G

Journal of the American Heart Association 20211231 1

Background Long scanning times impede cardiac magnetic resonance (CMR) clinical uptake. A "one-size-fits-all" shortened, focused protocol (eg, only function and late-gadolinium enhancement) reduces scanning time and costs, but provides less information. We developed 2 question-driven CMR and stress-CMR protocols, including tailored advanced tissue characterization, and tested their effectiveness in reducing scanning time while retaining the diagnostic performances of standard protocols. Methods ...[more]

PMID: 34970923

Similar Datasets

Project description:ObjectivesTo develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI.Materials and methodsRetrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4.ResultsThe best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data.ConclusionA deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development.Clinical relevance statementA robust automated inversion time selection tool for late gadolinium-enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection.Key points• A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.

Project description:ObjectivesQuantitative evaluations of function, volume and mass are fundamental in the diagnostic workup of different cardiovascular diseases and can be exactly determined by CMRI in sinus rhythm. This does not hold true in arrhythmia as CMR is hampered by reconstruction artifacts caused by inconsistent data from multiple heartbeats. Real-time (RT) MRI at high temporal resolution might reduce these problems.MethodsConsecutive patients with atrial fibrillation were prospectively included and underwent RT and conventional CINE CMR in randomized order. 29 patients were studied at 1.5 T and 30 patients at 3 T. At 3 T a group of 20 subjects in sinus rhythm served as controls. RT and CINE image quality was evaluated in different planes and for different wall sections using a Likert scale (from zero to four). Volumetric analysis was performed using two types of software and differences between RT and CINE CMR were evaluated.ResultsIn patients with atrial fibrillation RT CMR short axis (SA) resulted in a significantly higher image quality compared to CINE imaging both at 1.5 T and 3 T (1.5 T: mid SA: 3.55 ± 0.5 RT vs 2.6 ± 0.9 CINE, p = 0.0001; 3 T: mid SA: 3.15 ± 0.9 RT vs 2.6 ±1.0 CINE, p = 0.03); This qualitative difference was more marked and significant for the long axis views (2CV and 4CV) at 1.5 T (1.5 T: 2CV: 3.2 ± 0.6 RT vs 2.65 ± 1.1 CINE; p = 0.011; 4CV: 2.9 ± 0.69 RT vs 2.4 ± 0.9 CINE; p = 0.0044). During sinus rhythm CINE images were superior concerning diagnostic quality (3 T mid SA: 3.35 ± 0.45 RT vs 3.8 ± 0.5 CINE, p = 0.008). Quantitative analysis was successful with both software packages and the results showed a good correlation (Pearson correlation between 0.679 and 0.921 for patients). RT CMR resulted in slightly lower functional volumes than CINE CMR (3 T: patients: EDVI 86 ± 29 ml/m2 RT vs 93 29 ml/m2± 29 CINE, Pearson r = 0.902) but similar ejection fractions (3 T: patients: EF 47 ± 16% RT vs 45 ± 13% CINE, Pearson r = 0679; controls: EF 63 ± 6 RT vs 63 ± 3 CINE, Pearson r = 0.695).ConclusionRT CMR improves image quality in arrhythmic patients and renders studies more comfortable. Volumetric analysis is feasible with slightly lower values relative to CINE CMR, while ejection fractions are comparable.

Project description:A recent large-scale clinical trial found that an initial invasive strategy does not improve cardiac outcomes beyond optimized medical therapy in patients with stable coronary artery disease. Novel methods to stratify at-risk patients may refine therapeutic decisions to improve outcomes.In a cohort of 815 consecutive patients referred for evaluation of myocardial ischemia, we determined the net reclassification improvement of the risk of cardiac death or nonfatal myocardial infarction (major adverse cardiac events) incremental to clinical risk models, using guideline-based low (<1%), moderate (1% to 3%), and high (>3%) annual risk categories. In the whole cohort, inducible ischemia demonstrated a strong association with major adverse cardiac events (hazard ratio=14.66; P<0.0001) with low negative event rates of major adverse cardiac events and cardiac death (0.6% and 0.4%, respectively). This prognostic robustness was maintained in patients with previous coronary artery disease (hazard ratio=8.17; P<0.0001; 1.3% and 0.6%, respectively). Adding inducible ischemia to the multivariable clinical risk model (adjusted for age and previous coronary artery disease) improved discrimination of major adverse cardiac events (C statistic, 0.81-0.86; P=0.04; adjusted hazard ratio=7.37; P<0.0001) and reclassified 91.5% of patients at moderate pretest risk (65.7% to low risk; 25.8% to high risk) with corresponding changes in the observed event rates (0.3%/y and 4.9%/y for low and high risk posttest, respectively). Categorical net reclassification index was 0.229 (95% confidence interval, 0.063-0.391). Continuous net reclassification improvement was 1.11 (95% confidence interval, 0.81-1.39).Stress cardiac magnetic resonance imaging effectively reclassifies patient risk beyond standard clinical variables, specifically in patients at moderate to high pretest clinical risk and in patients with previous coronary artery disease.http://www.clinicaltrials.gov. Unique identifier: NCT01821924.

Dataset Information

Effective Study: Development and Application of a Question-Driven, Time-Effective Cardiac Magnetic Resonance Scanning Protocol.

Publications

Effective Study: Development and Application of a Question-Driven, Time-Effective Cardiac Magnetic Resonance Scanning Protocol.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets