Reduction of motion artifacts in cardiac CT based on partial angle reconstructions from short scan data

We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.

Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

Purpose: Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including phantom, cadaver, and animal studies as precursors to clinical studies. Methods: The method corrects deformable motion in CBCT scan data by solving for a motion vector field (MVF) that optimizes a sharpness criterion in the 3D image (viz., gradient entropy). MVFs are estimated by interpolating M locally rigid motion trajectories across N temporal nodes and are incorporated in a modified 3D filtered backprojection approach. The method was evaluated in a cervical spine phantom under flexion, and a cadaver undergoing variable magnitude of complex motion while imaged on a mobile C-arm (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Further assessment was performed on a preclinical animal study using a clinical fixed-room C-arm (Artis Zee, Siemens Healthineers, Forchheim, Germany). Results: In phantom studies, the algorithm resolved visibility of cervical vertebrae under situations of strong flexion, reducing the root-mean-square error by 60% when compared to a motion-free reference. Reduced motion artifacts (blurring, streaks, and loss of soft-tissue edges) were evident in abdominal CBCT of a cadaver imaged during small, medium, and large motion-induced deformation. The animal study demonstrated reduction of streaks from complex motion of bowel gas during the scan. Conclusion: Overall, the studies demonstrate the robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., mobile or fixed-room C-arms) and other confounding factors in real (not simulated) experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional body radiology.

A motion compensation method that is aimed at correcting motion artifacts of cardiac valves is proposed. The primary focus is the aorticvalve. 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. 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 and , respectively. In the case of artifact-free images, a chance to introduce blurring was observed and their average score was raised by 0.42 ± 0.03. Motion artifacts were consistently removed and image qualityimproved.

This paper presents a three-dimensional method to reconstruct moving objects from cone-beam X-ray projections using an iterative reconstruction algorithm and a given motion vector field. For the image representation, adapted blobs are used, which can be implemented efficiently as basis functions. Iterative reconstruction requires the calculation of line integrals (forward projections) through the image volume, which are compared with the actual measurements to update the image volume. In the existence of a divergent motion vector field, a change in the volumes of the blobs has to be taken into account in the forward and backprojections. An efficient method to calculate the line integral through the adapted blobs is proposed. It solves the problem, how to compensate for the divergence in the motion vector field on a grid of basis functions. The method is evaluated on two phantoms, which are subject to three different known motions. Moreover, a motion-compensated filtered back-projection reconstruction method is used, and the reconstructed images are compared. Using the correct motion vector field with the iterative motion-compensated reconstruction, sharp images are obtained, with a quality that is significantly better than gated reconstructions.

While head motion is considered a significant problem in magnetic resonance imaging (MRI), there is no data to quantify its extent, severity, or effect on image quality. PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI offers a novel means of quantifying and compensating for head motion. We performed axial T2-weighted PROPELLER (motion corrected: P-CR; uncorrected: P-UNCR) and conventional MRI (CONV), with equal scan times, in five normal volunteers and 35 clinical subjects. Volunteers were examined lying still and performing two separate head movements (shake "no" and nod "yes") to assess detection and compensation of in-plane motion by PROPELLER MRI. Images were examined by three radiologists for motion artifact and for overall image quality. Head rotation and translation was detected in all subjects during each slice acquisition, with expected changes occurring with volunteer head motion. Motion artifact was less commonly seen on PROPELLER than CONV MR (chi(2) test P < 0.001). PROPELLER was preferred over CONV in all subjects (P < 0.05) and P-CR was judged superior to P-UNCR (P = 0.02). Intracranial pathology was equally or better demonstrated with PROPELLER. PROPELLER MRI offers a means of quantifying head motion, reducing motion artifact, and improving image quality.

AimTo subjectively and objectively evaluate the feasibility and diagnostic reliability of a low-dose, long-pitch dual-source chest CT protocol on third-generation dual-source CT (DSCT) with spectral shaping at 100Sn kVp for COVID-19 patients.Materials and methodsPatients with COVID-19 and positive swab-test undergoing to a chest CT on third-generation DSCT were included. The imaging protocol included a dual-energy acquisition (HD-DECT, 90/150Sn kVp) and fast, low-dose, long-pitch CT, dual-source scan at 100Sn kVp (LDCT). Subjective (Likert Scales) and objective (signal-to-noise and contrast-to-noise ratios, SNR and CNR) analyses were performed; radiation dose and acquisition times were recorded. Nonparametric tests were used.ResultsThe median radiation dose was lower for LDCT than HD-DECT (Effective dose, ED: 0.28 mSv vs. 3.28 mSv, p = 0.016). LDCT had median acquisition time of 0.62 s (vs 2.02 s, p = 0.016). SNR and CNR were significantly different in several thoracic structures between HD-DECT and LDCT, with exception of lung parenchyma. Qualitative analysis demonstrated significant reduction in motion artifacts (p = 0.031) with comparable diagnostic reliability between HD-DECT and LDCT.ConclusionsUltra-low-dose, dual-source, fast CT protocol provides highly diagnostic images for COVID-19 with potential for reduction in dose and motion artifacts.

Motivation: The need for a non-ionizing, high-resolution 3D lung imaging method with minimized motion artifacts to evaluate pulmonary disease. Goal(s): To develop a free-breathing, high-resolution volumetric lung MRI technique that reduces motion blurring and provides functional ventilation maps. Approach: Free-breathing pulmonary images were acquired using a 3D SPGR UTE sequence with a 3D conical trajectory. Respiratory motion was extracted from central k-space, enabling phase-resolved reconstruction, ventilation mapping, and motion-compensated reconstruction. Results: The integration of 3D conical readouts with motion-compensated reconstruction yielded high-resolution images devoid of respiratory motion artifacts. Regional ventilation mapping derived from the reconstruction pipeline was validated in healthy subjects. Impact: Free-breathing 3D conical acquisition with motion compensation enables isotropic, high-resolution volumetric lung imaging with reduced motion blurring. This technique is crucial for diagnosing and monitoring lung diseases like COPD and pulmonary fibrosis, significantly enhancing noninvasive diagnostic capabilities and treatment planning.

Rotational coronary angiography provides a multitude of x-ray projections of the contrast agent enhanced coronary arteries along a given trajectory with parallel ECG recording. These data can be used to derive motion information of the coronary arteries including vessel displacement and pulsation. In this paper, a fully automated algorithm to generate 4D motion vector fields for coronary arteries from multi-phase 3D centerline data is presented. The algorithm computes similarity measures of centerline segments at different cardiac phases and defines corresponding centerline segments as those with highest similarity. In order to achieve an excellent matching accuracy, an increasing number of bifurcations is included as reference points in an iterative manner. Based on the motion data, time-dependent vessel surface extraction is performed on the projections without the need of prior reconstruction. The algorithm accuracy is evaluated quantitatively on phantom data. The magnitude of longitudinal errors (parallel to the centerline) reaches approx. 0.50 mm and is thus more than twice as large as the transversal 3D extraction errors of the underlying multi-phase 3D centerline data. It is shown that the algorithm can extract asymmetric stenoses accurately. The feasibility on clinical data is demonstrated on five different cases. The ability of the algorithm to extract time-dependent surface data, e.g. for quantification of pulsating stenosis is demonstrated.

CT images acquired during deep-inspiration breath-holding (DIBH) have higher image quality than free-breathing (FB) CT images as they are free from respiratory motion artifacts. However, there exists significant mismatch between DIBH CT images and FB PET images since the displacement of organs in the thorax and abdomen at DIBH is significantly larger than that during regular FB. Our goal is to correct for the large mismatch between FB PET and DIBH CT in PET/CT imaging. The list-mode PET data were divided into four equal-count frames using amplitude based data-driven respiratory gating technique. Frame 1 and Frame 4 corresponded to end-expiration and end-inspiration respectively, and they were reconstructed using OSEM image reconstruction algorithm. To avoid attenuation-activity mismatch artifacts, the attenuation map used in attenuation correction was modified by filling the lung region with attenuation coefficient of soft tissue. 3D motion vector field from Frame 4 to Frame 1 was calculated from the reconstructed PET images using B-spline image registration algorithm. Assuming the respiratory motion during FB is consistent during FB and DIBH, the motion vector field from DIBH to end-inspiration phase is modeled by scaling the calculated respiratory motion from Frame 4 to Frame 1. The scalar was determined by maximizing the mutual information between transformed DIBH attenuation map and PET image at Frame 4. DIBH attenuation map was transformed using the scaled motion vector field from Frame 4 to Frame 1 to obtain attenuation map at Frame 4. Attenuation maps for other frames were obtained by transforming attenuation map at Frame 4 using corresponding motion vector fields. The method was applied to clinical patient data acquired using DIBH CT and FB PET protocol. While DIBH attenuation map creates significant mismatch artifacts in PET images, the proposed method was able to generate attenuation maps matching FB PET frames and reduce mismatch artifacts.

For spotlight synthetic aperture radar (SAR) on circular trajectories, the phase error caused by motion error is 2-D space-variant. For the conventional motion compensation (MOCO) algorithm, the imaging scene is generally segmented into many sub-regions to compensate for the space-variant phase error. However, the complex phase error results in that fact that it is difficult to obtain the quantitative criterion of scene segmentation. To address this issue, we propose a novel MOCO algorithm. This method contains three steps; the first step is non-space-variant phase error compensation; the second step is to remove the height term; the third step is space-variant phase error compensation. Among these steps, the most critical operation is removing the height term. In the distance equation, the height term is independent of the ground target coordinate. Hence, it can be removed by a serial of operations containing resampling and phase compensation. Through removing height term, it doesn't only compensate the height error but also makes the residual phase error into 1-D space-variant. Then, the phase error is exclusively related to the radius error and the target abscissa. Hence, after compensating for the space-variant error, it only needs to segment scene along the abscissa direction. Meanwhile, it can gain the quantitative criterion of scene segmentation to guarantee the phase error variation range is less than π/4 in each sub-scene. Eventually, some simulations are conducted to illustrate that the proposed method is valid.

.Significance: Confocal laser scanning enables optical sectioning in clinical fiber bundle endomicroscopes, but lower-cost, simplified endomicroscopes use widefield incoherent illumination instead. Optical sectioning can be introduced in these simple systems using structured illumination microscopy (SIM), a multiframe digital subtraction process. However, SIM results in artifacts when the probe is in motion, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesize a larger effective field of view (FOV).Aim: We report and validate an automatic motion compensation technique to overcome motion artifacts and allow generation of mosaics in SIM endomicroscopy.Approach: Motion compensation is achieved using image registration and real-time pattern orientation correction via a digital micromirror device. We quantify the similarity of moving probe reconstructions to those acquired with a stationary probe using the relative mean of the absolute differences (MAD). We further demonstrate mosaicking with a moving probe in mechanical and freehand operation.Results: Reconstructed SIM images show an improvement in the MAD from 0.85 to 0.13 for lens paper and from 0.27 to 0.12 for bovine tissue. Mosaics also show vastly reduced artifacts.Conclusion: The reduction in motion artifacts in individual SIM reconstructions leads to mosaics that more faithfully represent the morphology of tissue, giving clinicians a larger effective FOV than the probe itself can provide.

In this paper, to detect the moving objects between frames in compressed video and to obtain the best compression video and the noiseless video. We describe a video in which frames by classifying macroblocks (MB), and describe motion estimation (ME), motion vector field (MV) and motion compensation (MC). we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content based on the motion vector. MB class information video applications such as shot change detection, motion discontinuity detection, Outlier rejection for global motion estimation. To reduce the noise and to improve the clarity of the compressed video by using contrast limited adaptive histogram equalization (CLAHE) Algorithm.

Motion-compensated frame rate up-conversion (MC-FRUC) increases the frame rate of a video by interpolating a new frame from temporally consecutive frames. MC-FRUC consists of two key operations: motion estimation and motion compensated frame interpolation. Motion compensation along unidirectional motion trajectories directly causes overlaps and holes issues. To solve these issues, this paper presents a new algorithm for bidirectional motion compensated frame interpolation. Firstly, the proposed method generates a bidirectional motion vector field from two unidirectional motion vector fields (forward and backward) obtained from the motion estimation. It is done by projecting the forward and backward motion vectors into the interpolated frame. A comprehensive metric as an extension of the distance between a projected block and an interpolated block is proposed to compute weighted coefficients in case the interpolated block has multiple projected ones. Holes are filled based on vector median filter of non-hole available neighbor blocks. The proposed method outperforms existing MC-FRUC methods and removes block artifacts significantly.

Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed. Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region. Then the motion region was extracted to carry out motion estimation and motion compensation on it. The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation (MRME) is improved.