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

Moving Metal Artifact Reduction and Intrinsic Gating for Cone-Beam CT Scans of the Thorax Region

In this work, novel algorithms in the field of retrospective intrinsic respiratory and cardiac gating and moving metal artifact reduction (MMAR) for cone-beam CT (CBCT) scans that are used in image-guided radiation therapy (IGRT) were developed. The added difficulty for CBCT scans is the relatively long acquisition time of up to 60 s compared to 0.25 s for clinical CT scans. The occuring respiratory and cardiac motion in combination with metal inserts cannot be handled with classical metal artifact reduction (MAR) methods. The proposed MMAR algorithms utilize an approach that combines the fields of motion compensation (MoCo) with classical MAR methods. In order for the MMAR methods to work, the motion state has to be known for every projection angle. A novel intrinsic gating approach, that automatically generates a list of potential motion surrogate candidates and identifies the best, was developed and used as a basis for the MMAR algorithms, so they can be applied for patients where no externally recorded motion signal is available. Whereas classical approaches for intrinsic gating are not designed for a laterally shifted detector, that is commonly used in IGRT with a CBCT, the novel algorithm works on scans with or without a shifted detector. It can also be used as a basis for 4D (3D + respiratory) or 5D (3D + respiratory + cardiac) MoCo algorithms

High frame-rate cardiac ultrasound imaging with deep learning

Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.Comment: To appear in the Proceedings of MICCAI, 201

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under review for MICCAI 201

Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

A deep learning framework for quality assessment and restoration in video endoscopy

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. We propose a fully automatic framework that can: 1) detect and classify six different primary artifacts, 2) provide a quality score for each frame and 3) restore mildly corrupted frames. To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. We introduce a quality metric to assess frame quality and predict image restoration success. Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. Our detector yields the highest mean average precision (mAP at 5% threshold) of 49.0 and the lowest computational time of 88 ms allowing for accurate real-time processing. Our restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos we show that our approach preserves an average of 68.7% which is 25% more frames than that retained from the raw videos.Comment: 14 page