Cardiac Motion Correction for Helical CT Scan With an Ordinary Pitch

Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm

Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman's Rho. Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 ± 1.65 to 2.26 ± 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction. The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS.

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric

PURPOSE

Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image.

METHODS

The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was evaluated via metrics of correlation with ground truth VIF ${\bm{VIF}}$ and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120 kV) and dose (1.19-39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity.

RESULTS

The magnitude and spatial map ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the localVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation usingVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness.

CONCLUSION

The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

Milestones in CT: Past, Present, and Future

In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.

Intraindividual evaluation of effects of image filter function on image quality in coronary computed tomography angiography

Objectives

To evaluate whether applying image filters (smooth 3D+ and edge-2) improves image quality in coronary CT angiography (CCTA).

Methods

Ninety patients (routine group) with suspected coronary artery diseases based on 16-cm wide coverage detector CT findings were retrospectively enrolled at a chest pain center from December 2019 to September 2021. Two image filters, smooth 3D+ and edge-2 available on the Advantage Workstation (AW) were subsequently applied to the images to generate the research group (SE group). Quantitative parameters, including CT value, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), image sharpness and image quality score, and diagnostic accuracy were compared between the two groups.

Results

A total of 900 segments from 270 coronary arteries in 90 patients were analyzed. SNR, CNR, and image sharpness for vessels and image quality scores in the SE group were significantly better than those in the routine group (all p < 0.001). The SE group showed a slightly higher negative predictive value (NPV) on the left anterior descending artery and right coronary artery (RCA) stenosis evaluations, as well as total NPV. The SE group also showed slightly higher sensitivity and accuracy than the routine group on RCA stenosis evaluation.

Conclusion

The use of an image filter combining smooth 3D+ and edge-2 on an AW could improve the image quality of CCTA and increase radiologists' diagnostic confidence.

Reference-free learning-based similarity metric for motion compensation in cone-beam CT

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT.Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric.Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σDL-VIF = 0.008 versusσgradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images.Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Motion compensation for aortic valves using partial angle CT reconstructions motion compensation of cardiac valve CT

PURPOSE

A motion compensation method that is aimed at correcting motion artifacts of cardiac valves is proposed. The primary focus is the aortic valve.

METHODS

The method is based around partial angle reconstructions and a cost function including the image entropy. A motion model is applied to approximate the cardiac motion in the temporal and spatial domain. Based on characteristic values for velocities and strain during cardiac motion, penalties for the velocity and spatial derivatives are introduced to maintain anatomically realistic motion vector fields and avoid distortions. The model addresses global elastic deformation, but not the finer and more complicated motion of the valve leaflets.

RESULTS

The method is verified based on clinical data. Image quality was improved for most artifact impaired reconstructions. An image quality study with Likert scoring of the motion artifact severity on a scale from 1 (highest image quality) to 5 (lowest image quality/extreme artifact presence) was performed. The biggest improvements after applying motion compensation were achieved for strongly artifact impaired initial images scoring 4 and 5, resulting in an average change of the scores by -0.59 ± 0.06 and -1.33 ± 0.03, respectively. In case of artifact free images, a chance to introduce blurring was observed and their average score was raised by 0.42 ± 0.03.

CONCLUSION

Motion artifacts were consistently removed and image quality improved.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%-92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5-5 mm^-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Motion correction for routine X-ray lung CT imaging

A novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

Validation of a novel cardiac motion correction algorithm for x-ray computed tomography: From phantom experiments to initial clinical experience

A novel cardiac motion correction algorithm has been introduced recently. Unlike other segmentation-based approaches it is fully automatic and capable of correcting motion artifacts of myocardial wall and other moving structures as well as coronary arteries of the heart. In addition, it requires raw data of only less than a single rotation for motion estimation and correction, which is a significant advantage from the perspective of x-ray exposure and workflow. The aim of this study is to explore the capability of the proposed method through phantoms and in-vivo experiments. Motion correction of coronary arteries and other heart structures including myocardial wall is the main focus of the evaluation. First, we provide a brief introduction to the concept of the motion correction algorithm. Next we address the procedure of our studies using an XCAT phantom and commercially available physical phantoms. Results of XCAT phantom demonstrate that our solution significantly improves the structural similarity of coronary arteries compared to FBP (proposed: 0.94, FBP: 0.77, p<0.001). Besides, it provides significantly lower root mean square error (proposed: 20.27, FBP: 25.33, p = 0.01) of the whole heart image. Mocomo phantom study shows that the proposed method improves the visualization of coronary arteries estimated based on motion score (1: worst, 5: best) from two experienced radiologists (proposed: 3.5, FBP: 2.1, p<0.001). The results of these phantom studies reveal that the proposed has a great potential in handling motion artifacts of other heart structures as well as coronary arteries. Finally, we provide the results of in-vivo animal and human studies. The 3D and 4D heart images show a consistently superior performance in the visualization of coronary arteries along with myocardial wall and other cardiothoracic structures. Based on these findings of our studies, we are of the opinion that our solution has a considerable potential to improve temporal resolution of cardiac CT imaging. This would open the door to innovations in structural or functional diagnosis of the heart.

Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning

Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.