Automated quantification and evaluation of motion artifact on coronary CT angiography images

Abstract Purpose This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground‐truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground‐truth segmentations and the algorithm‐generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact‐length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground‐truth decisions of whether the datasets were of sufficient image quality. A five‐fold cross‐validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground‐truth segmentations. The MAS threshold and artifact‐length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion The Motion Artifact Quantification algorithm calculated accurate

Evaluation of Motion Artifact Metrics for Coronary CT Angiography

Purpose This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best‐phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground‐truth motion artifact scores from a series of pairwise comparisons. Method Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low‐Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine‐filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground‐truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground‐truth reader score. The Kendall\u27s Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed. Results On phantom images, the Kendall\u27s Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall\u27s Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall\u27s Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall\u27s Tau coefficient of 0.65. Conclusion The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images

Automatic segmentation of the left ventricle cavity and myocardium in MRI data

A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method

In vivo contrast free chronic myocardial infarction characterization using diffusion-weighted cardiovascular magnetic resonance.

BackgroundDespite the established role of late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) in characterizing chronic myocardial infarction (MI), a significant portion of chronic MI patients are contraindicative for the use of contrast agents. One promising alternative contrast free technique is diffusion weighted CMR (dwCMR), which has been shown ex vivo to be sensitive to myocardial fibrosis. We used a recently developed in vivo dwCMR in chronic MI pigs to compare apparent diffusion coefficient (ADC) maps with LGE imaging for infarct characterization.MethodsIn eleven mini pigs, chronic MI was induced by complete occlusion of the left anterior descending artery for 150 minutes. LGE, cine, and dwCMR imaging was performed 8 weeks post MI. ADC maps were derived from three orthogonal diffusion directions (b = 400 s/mm2) and one non-diffusion weighted image. Two semi-automatic infarct classification methods, threshold and full width half max (FWHM), were performed in both LGE and ADC maps. Regional wall motion (RWM) analysis was performed and compared to ADC maps to determine if any observed ADC change was significantly influenced by bulk motion.ResultsADC of chronic MI territories was significantly increased (threshold: 2.4 ± 0.3 μm2/ms, FWHM: 2.4 ± 0.2 μm2/ms) compared to remote myocardium (1.4 ± 0.3 μm2/ms). RWM was significantly reduced (threshold: 1.0 ± 0.4 mm, FWHM: 0.9 ± 0.4 mm) in infarcted regions delineated by ADC compared to remote myocardium (8.3 ± 0.1 mm). ADC-derived infarct volume and location had excellent agreement with LGE. Both LGE and ADC were in complete agreement when identifying transmural infarcts. Additionally, ADC was able to detect LGE-delineated infarcted segments with high sensitivity, specificity, PPV, and NPV. (threshold: 0.88, 0.93, 0.87, and 0.94, FWHM: 0.98, 0.97, 0.93, and 0.99, respectively).ConclusionsIn vivo diffusion weighted CMR has potential as a contrast free alternative for LGE in characterizing chronic MI

Quantitative imaging in cardiovascular CT angiography

In de afgelopen decennia is computertomografie (CT) een prominente niet-invasieve modaliteit om hart- en vaatziekten te evalueren geworden. Dit proefschrift heeft als doel de rol van CT in de therapeutische behandeling van coronaire hartziekte (CAD) en klepaandoeningen te onderzoeken.De relatie tussen kransslagadergeometrie (statisch en dynamisch) en aanwezigheid en omvang van CAD met CT werd onderzocht. De resultaten suggereren dat de statische geometrie van de kransslagader significant gerelateerd is aan de aanwezigheid van plaque en stenose. Er was echter geen verband tussen dynamische verandering van de coronaire arterie-geometrie en de ernst van CAD. Een algoritme om de invloed van intraluminair contrastmiddel op niet-verkalkte atherosclerotische plaque Hounsfield-Unit-waarden te corrigeren werd gepresenteerd en gevalideerd met behulp van fantomen.Diagnose en operatieplanning kunnen cruciale gevolgen hebben voor de klinische uitkomst van chirurgische ingrepen. In dit proefschrift wordt beschreven dat halfautomatische softwareprogramma’s het kwantificeren van het aortaklepgebied betere reproduceerbare resultaten toonden in vergelijking met handmatige metingen, en vergelijkbare resultaten met de huidige gouden standaard, de echocardiografie. Een systematische review over het dynamische gedrag van de aorta-annulus toont aan dat de vorm van de aorta-annulus tijdens de hartcyclus verandert, wat impliceert dat er bij het bepalen van een prothese rekening moet worden gehouden met meerdere fasen. Een andere review beschrijft het gebruik van 3D-printen in de chirurgische planning samen met andere toepassingen voor de behandeling van hartklepaandoeningen.CT is de belangrijkste beeldvormingsmodaliteit in deze onderzoeken, die gericht waren op de therapeutische behandeling van hart- en vaatziekten, van vroege risicobepaling tot diagnose en chirurgische planning.In the recent decades computed tomography (CT) has emerged as a dominant non-invasive modality to evaluate cardiovascular diseases. This thesis aimed to explore the role of CT in the therapeutic management of coronary artery disease (CAD) and valvular diseases.The relationship between both static and dynamic coronary artery geometry and presence and extent of CAD using CT was investigated. The results suggest that the static coronary artery geometry is significantly related to presence of plaque and significant stenosis. However, there were no such relationship between dynamic change of coronary artery geometry and severity of CAD. As part of this thesis an algorithm to correct the influence of lumen contrast enhancement on non-calcified atherosclerotic plaque Hounsfield-Unit values was presented. The algorithm was validated using phantoms. The diagnosis and surgical planning may have crucial impact on clinical outcome. Semi-automatic software for aortic valve area quantification presented in this thesis was proven to be more repeatable and similar to gold standard echocardiography in comparison to manual measurements. The systematic review regarding the dynamic behavior of aortic annulus revealed that aortic annulus geometry changes throughout the cardiac cycle which implies that multiple phases should be taken into account for prosthesis sizing. Another review in this thesis discusses the use of 3D printing in the surgical planning along with other applications for the treatment of valvular diseases.CT is the main imaging modality in these studies which were focused on the therapeutic management of cardiovascular diseases from early risk determination to diagnosis and surgical planning

Clinical evaluation of new automatic coronary specific best cardiac phase selection algortithm for single-beat coronary CT angiography

The aim of this study was to evaluate the workflow efficiency of a new automatic coronary-specific reconstruction technique (Smart Phase, GE Healthcare—SP) for selection of the best cardiac phase with least coronary motion when compared with expert manual selection (MS) of best phase in patients with high heart rate. A total of 46 patients with heart rates above 75 bpm who underwent single beat coronary computed tomography angiography (CCTA) were enrolled in this study. CCTA of all subjects were performed on a 256-detector row CT scanner (Revolution CT, GE Healthcare, Waukesha, Wisconsin, US). With the SP technique, the acquired phase range was automatically searched in 2% phase intervals during the reconstruction process to determine the optimal phase for coronary assessment, while for routine expert MS, reconstructions were performed at 5% intervals and a best phase was manually determined. The reconstruction and review times were recorded to measure the workflow efficiency for each method. Two reviewers subjectively assessed image quality for each coronary artery in the MS and SP reconstruction volumes using a 4-point grading scale. The average HR of the enrolled patients was 91.1±19.0bpm. A total of 204 vessels were assessed. The subjective image quality using SP was comparable to that of the MS, 1.45±0.85 vs 1.43±0.81 respectively (p = 0.88). The average time was 246 seconds for the manual best phase selection, and 98 seconds for the SP selection, resulting in average time saving of 148 seconds (60%) with use of the SP algorithm. The coronary specific automatic cardiac best phase selection technique (Smart Phase) improves clinical workflow in high heart rate patients and provides image quality comparable with manual cardiac best phase selection. Reconstruction of single-beat CCTA exams with SP can benefit the users with less experienced in CCTA image interpretation

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Methodology of a reevaluation of cardiovascular outcomes in the RECORD trial: study design and conduct

Background In 2010, after regulatory review of rosiglitazone licensing, the US Food and Drug Administration (FDA) requested a reevaluation of cardiovascular end points in the RECORD trial.<p></p> Methods Automated screening of the original clinical trial database and manual case report form review were performed to identify all potential cardiovascular and noncardiovascular deaths, and nonfatal myocardial infarction (MI) and stroke events. Search techniques were used to find participants lost to follow-up, and sites were queried for additional source documents. Suspected events underwent blinded adjudication using both original RECORD end point definitions and new FDA end point definitions, before analysis by the Duke Clinical Research Institute.<p></p> Results The reevaluation effort included an additional 328 person-years of follow-up. Automated screening identified 396 suspected deaths, 2,052 suspected MIs, and 468 suspected strokes. Manual review of documents by Duke Clinical Research Institute clinical events classification (CEC) coordinators identified an additional 31 suspected deaths, 49 suspected MIs, and 28 suspected strokes. There were 127 CEC queries issued requesting additional information on suspected deaths; 43 were closed with no site response, 61 were closed with a response that no additional data were available, and additional data were received for 23. Seventy CEC queries were issued requesting additional information for suspected MI and stroke events; 31 were closed with no site response, 20 were closed with a response that no additional data were available, and 19 resulted in additional data.<p></p> Conclusions Comprehensive procedures were used for rigorous event reascertainment and readjudication in a previously completed open-label, global clinical trial. These procedures used in this unique situation were consistent with other common approaches in the field, were enhanced to address the FDA concerns about the original RECORD trial results, and could be considered by clinical trialists designing event readjudication protocols for drug development programs that have been completed.<p></p&gt