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's 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's 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's 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's 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's 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.

[1]  B. Desjardins,et al.  ECG-gated cardiac CT. , 2004, AJR. American journal of roentgenology.

[2]  Katsuyuki Taguchi,et al.  A fully four-dimensional, iterative motion estimation and compensation method for cardiac CT. , 2012, Medical physics.

[3]  Srini Tridandapani,et al.  Characterization of cardiac quiescence from retrospective cardiac computed tomography using a correlation-based phase-to-phase deviation measure. , 2015, Medical physics.

[4]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[5]  G. Hillis,et al.  64-Slice computed tomography angiography in the diagnosis and assessment of coronary artery disease: systematic review and meta-analysis , 2008, Heart.

[6]  H. Alkadhi,et al.  Coronary 64-slice CT angiography predicts outcome in patients with known or suspected coronary artery disease , 2008, European Radiology.

[7]  Jong Beom Ra,et al.  Cardiac motion correction based on partial angle reconstructed images in x-ray CT. , 2015, Medical physics.

[8]  S. Dutta,et al.  Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction. , 2015, Medical physics.

[9]  Jed D. Pack,et al.  Coronary artery motion estimation and compensation: A feasibility study , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[10]  W A Kalender,et al.  Simultaneous misalignment correction for approximate circular cone-beam computed tomography. , 2008, Physics in medicine and biology.

[11]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[12]  T Nielsen,et al.  Automatic phase determination for retrospectively gated cardiac CT. , 2004, Medical physics.

[13]  Christopher Rohkohl,et al.  Improving best-phase image quality in cardiac CT by motion correction with MAM optimization. , 2013, Medical physics.

[14]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[15]  Borut Marincek,et al.  Coronary artery motion and cardiac phases: dependency on heart rate -- implications for CT image reconstruction. , 2007, Radiology.

[16]  R Haberl,et al.  A dynamic approach to identifying desired physiological phases for cardiac imaging using multislice spiral CT. , 2003, Medical physics.

[17]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[18]  Elliot R. McVeigh,et al.  The impact of small motion on the visualization of coronary vessels and lesions in cardiac CT: A simulation study , 2017, Medical physics.

[19]  Werner Jaschke,et al.  The diagnostic and prognostic value of coronary CT angiography in asymptomatic high-risk patients: a cohort study , 2014, Open Heart.

[20]  Andrew S Phelps,et al.  Pairwise comparison versus Likert scale for biomedical image assessment. , 2015, AJR. American journal of roentgenology.

[21]  Ron Kikinis,et al.  Automatic Optimization of Segmentation Algorithms Through Simultaneous Truth and Performance Level Estimation (STAPLE) , 2004, MICCAI.

[22]  Konstantin Nikolaou,et al.  Dual-source CT cardiac imaging: initial experience , 2006, European Radiology.