Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing:Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment

Automating cardiac function assessment on cardiac magnetic resonance short-axis cines is faster and more reproducible than manual contour-tracing; however, accurately tracing basal contours remains challenging. Three automated post-processing software packages (Level 1) were compared to manual assessment. Subsequently, automated basal tracings were manually adjusted using a standardized protocol combined with software package-specific relative-to-manual standard error correction (Level 2). All post-processing was performed in 65 healthy subjects. Manual contour-tracing was performed separately from Level 1 and 2 automated analysis. Automated measurements were considered accurate when the difference was equal or less than the maximum manual inter-observer disagreement percentage. Level 1 (2.1 ± 1.0 min) and Level 2 automated (5.2 ± 1.3 min) were faster and more reproducible than manual (21.1 ± 2.9 min) post-processing, the maximum inter-observer disagreement was 6%. Compared to manual, Level 1 automation had wide limits of agreement. The most reliable software package obtained more accurate measurements in Level 2 compared to Level 1 automation: left ventricular end-diastolic volume, 98% and 53%; ejection fraction, 98% and 60%; mass, 70% and 3%; right ventricular end-diastolic volume, 98% and 28%; ejection fraction, 80% and 40%, respectively. Level 1 automated cardiac function post-processing is fast and highly reproducible with varying accuracy. Level 2 automation balances speed and accuracy

Assessment of myocardium at risk with contrast enhanced steady-state free precession cine cardiovascular magnetic resonance compared to single-photon emission computed tomography

<p>Abstract</p> <p>Background</p> <p>Final infarct size following coronary occlusion is determined by the duration of ischemia, the size of myocardium at risk (MaR) and reperfusion injury. The reference method for determining MaR, single-photon emission computed tomography (SPECT) before reperfusion, is impractical in an acute setting. The aim of the present study was to evaluate whether MaR can be determined from the contrast enhanced myocardium using steady-state free precession (SSFP) cine cardiovascular magnetic resonance (CMR) performed one week after the acute event in ST-elevation myocardial infarction (STEMI) patients with total coronary occlusion.</p> <p>Results</p> <p>Sixteen patients with STEMI (age 64 ± 8 years) received intravenous 99 m-Tc immediately before primary percutaneous coronary intervention. SPECT was performed within four hours. MaR was defined as the non-perfused myocardial volume derived with SPECT. CMR was performed 7.8 ± 1.2 days after the myocardial infarction using a protocol in which the contrast agent was administered before acquisition of short-axis SSFP cines. MaR was evaluated as the contrast enhanced myocardial volume in the cines by two blinded observers. MaR determined from the enhanced region on cine CMR correlated significantly with that derived with SPECT (r²= 0.78, p < 0.001). The difference in MaR determined by CMR and SPECT was 0.5 ± 5.1% (mean ± SD). The interobserver variability of contrast enhanced cine SSFP measurements was 1.6 ± 3.7% (mean ± SD) of the left ventricle wall volume.</p> <p>Conclusions</p> <p>Contrast enhanced SSFP cine CMR performed one week after acute infarction accurately depicts MaR prior to reperfusion in STEMI patients with total occlusion undergoing primary PCI. This suggests that a single CMR examination might be performed for determination of MaR and infarct size.</p

Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images

International audienceStatistical shape models (SSMs) represent a powerful tool used in patient-specific modeling to segment medical images because they incorporate a-priori knowledge that guide the model during deformation. Our aim was to evaluate segmentation accuracy in terms of left ventricular (LV) volumes obtained using four different SSMs versus manual gold standard tracing on cardiac magnetic resonance (CMR) images. A database of 3D echocardiographic (3DE) LV surfaces obtained in 435 patients was used to generate four different SSMs, based on cardiac phase selection. Each model was scaled and deformed to detect LV endocardial contours in the enddiastolic (ED) and end-systolic (ES) frames of a CMR short-axis (SAX) stack for 15 patients with normal LV function. Linear correlation and Bland–Altman analyses versus gold-standard showed in all cases high correlation (r²>0.95), non-significant biases and narrow limits of agreement

The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

The evaluation of left ventricular wall motion in Magnetic Resonance Imaging (MRI) clinical practice is based on a visual assessment of cine-MRI sequences. In fact, clinical interpreters (radiologists) proceed with a global visual evaluation of multiple cine-MRI sequences acquired in the three standard views. In addition, some functional parameters are quantified following a manual or a semi-automatic contouring of the myocardial borders. Although these parameters give information about the functional state of the left ventricle, they are not able to provide the location and the extent of wall motion abnormalities, which are associated with many cardiovascular diseases. In the past years, several approaches were developed to overcome the limitations of the classical evaluation techniques of left ventricular function. The aim of this article is to present an overview of the different methods and to summarize the relevant techniques based on myocardial contour detection and optical flow for regional assessment of left ventricular abnormalities

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac Magnetic Resonance Image Registry of Single Ventricle Patients

Purpose: To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (FORCE). / Materials and Methods: This retrospective study used 250 cardiac MRI examinations (November 2007–December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two UNet 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. / Results: There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (Bias: -0.6 mL/m2, LOA: -20.6–19.5 mL/m2), and end-systolic volume (ESV) (Bias: - 1.1 mL/m2, LOA: -18.1–15.9 mL/m2), with high intraclass correlation coefficients (ICC > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (Bias: -1.9 g/m2, LOA: -17.3–13.5 g/m2) and a ICC (0.94). There was also acceptable agreement for stroke volume (Bias:0.6 mL/m2, LOA: -17.2–18.3 mL/m2) and ejection fraction (Bias:0.6%, LOA: -12.2%–13.4%), with high ICCs (> 0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. / Conclusion: The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry

Standardized image interpretation and post-processing in cardiovascular magnetic resonance - 2020 update : Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post-Processing of the Society for Cardiovascular Magnetic Resonance (SCMR). The aim of the Task Force is to recommend requirements and standards for image interpretation and post-processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate. It is an update of the original recommendations published 2013

Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges

National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Center at BartsSmartHeart EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1)London Medical Imaging and AI Center for Value-Based HealthcareCAP-AI programmeEuropean Union's Horizon 2020 research and innovation programme under grant agreement No 825903