ABSTRACT
BACKGROUND: Active inflammatory bowel disease (A-IBD) but not remission (R-IBD) has been associated with an increased risk of cardiovascular death and hospitalization for heart failure. OBJECTIVES: Using cardiovascular magnetic resonance (CMR), this study aims to assess adverse myocardial remodeling in patients with IBD in correlation with disease activity. METHODS: Forty-four IBD patients without cardiovascular disease (24 female, median-age: 39.5 years, 26 A-IBD, 18 R-IBD) and 44 matched healthy volunteers (HV) were prospectively enrolled. The disease stage was determined by endoscopic and patient-reported criteria. Participants underwent CMR for cardiac phenotyping: cine imaging and strain analysis were performed to assess ventricular function. T1 mapping, extracellular volume and late-gadolinium enhanced images were obtained to assess focal and diffuse myocardial fibrosis. Simultaneous T1 and T2 elevation (T1 > 1049.3 ms, T2 > 54 ms) was considered to indicate a myocardial segment was inflamed. RESULTS: 16/44 (16.4%) IBD patients described dyspnea on exertion and 10/44 (22.7%) reported chest pain. A-IBD patients showed impaired ventricular function, indicated by reduced global circumferential and radial strain despite preserved left-ventricular ejection fraction. 16% of all IBD patients had focal fibrosis in a non-ischemic pattern. A-IDB patients had increased markers of diffuse left ventricular fibrosis (T1-values: A-IBD: 1022.0 ± 34.83 ms, R-IBD: 1010.10 ± 32.88 ms, HV: 990.61 ± 29.35 ms, p < .01). Significantly more participants with A-IDB (8/26, 30.8%) had at least one inflamed myocardial segment than patients in remission (0/18) and HV (1/44, 2.3%, p < .01). Markers of diffuse fibrosis correlated with disease activity. CONCLUSION: This study, using CMR, provides evidence of myocardial involvement and patterns of adverse left ventricular remodeling in patients with IBD. CLINICAL TRIAL REGISTRATION: ISRCTN30941346.
ABSTRACT
BACKGROUND AND OBJECTIVES: Cardiovascular Magnetic Resonance (CMR) imaging is a growing field with increasing diagnostic utility in clinical routine. Quantitative diagnostic parameters are typically calculated based on contours or points provided by readers, e.g. natural intelligences (NI) such as clinicians or researchers, and artificial intelligences (AI). As clinical applications multiply, evaluating the precision and reproducibility of quantitative parameters becomes increasingly important. Although segmentation challenges for AIs and guidelines for clinicians provide quality assessments and regulation, the methods ought to be combined and streamlined for clinical applications. The goal of the developed software, Lazy Luna (LL), is to offer a flexible evaluation tool that is readily extendible to new sequences and scientific endeavours. METHODS: An interface was designed for LL, which allows for comparing annotated CMR images. Geometric objects ensure precise calculations of metric values and clinical results regardless of whether annotations originate from AIs or NIs. A graphical user interface (GUI) is provided to make the software available to non-programmers. The GUI allows for an interactive inspection of image datasets as well as implementing tracing procedures, which follow statistical reader differences in clinical results to their origins in individual image contours. The backend software builds on a set of meta-classes, which can be extended to new imaging sequences and clinical parameters. Following an agile development procedure with clinical feedback allows for a quick implementation of new classes, figures and tables for evaluation. RESULTS: Two application cases present LL's extendibility to clinical evaluation and AI development contexts. The first concerns T1 parametric mapping images segmented by two expert readers. Quantitative result differences are traced to reveal typical segmentation dissimilarities from which these differences originate. The meta-classes are extended to this new application scenario. The second applies to the open source Late Gadolinium Enhancement (LGE) quantification challenge for AI developers "Emidec", which illustrates LL's usability as open source software. CONCLUSION: The presented software Lazy Luna allows for an automated multilevel comparison of readers as well as identifying qualitative reasons for statistical reader differences. The open source software LL can be extended to new application cases in the future.
Subject(s)
Contrast Media , Gadolinium , Reproducibility of Results , Magnetic Resonance Imaging/methods , SoftwareABSTRACT
Cardiovascular magnetic resonance imaging is the gold standard for cardiac function assessment. Quantification of clinical results (CR) requires precise segmentation. Clinicians statistically compare CRs to ensure reproducibility. Convolutional Neural Network developers compare their results via metrics. Aim: Introducing software capable of automatic multilevel comparison. A multilevel analysis covering segmentations and CRs builds on a generic software backend. Metrics and CRs are calculated with geometric accuracy. Segmentations and CRs are connected to track errors and their effects. An interactive GUI makes the software accessible to different users. The software's multilevel comparison was tested on a use case based on cardiac function assessment. The software shows good reader agreement in CRs and segmentation metrics (Dice > 90%). Decomposing differences by cardiac position revealed excellent agreement in midventricular slices: > 90% but poorer segmentations in apical (> 71%) and basal slices (> 74%). Further decomposition by contour type locates the largest millilitre differences in the basal right cavity (> 3 ml). Visual inspection shows these differences being caused by different basal slice choices. The software illuminated reader differences on several levels. Producing spreadsheets and figures concerning metric values and CR differences was automated. A multilevel reader comparison is feasible and extendable to other cardiac structures in the future.
