MO‐D‐330A‐02: Motion Artifact Correction Using a Novel Data Consistency Condition

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.

Rotational coronary angiography enables 3D reconstruction but suffers from intra-scan cardiac and respiratory motion. While gating handles cardiac motion, respiratory motion requires compensation. State-of-the-art algorithms rely on 3D-2D registration that depends on initial reconstructions of sufficient quality. We propose a compensation method that is applied directly in projection domain. It overcomes the need for reconstruction and thus complements the state-of-the-art. Virtual single-frame background subtraction based on vessel segmentation and spectral deconvolution yields non-truncated images of the contrasted lumen. This allows motion compensation based on data consistency conditions. We compensate craniocaudal shifts by optimizing epipolar consistency to (a) devise an image-based surrogate for cardiac motion and (b) compensate for respiratory motion. We validate our approach in two numerical phantom studies and three clinical cases. Correlation of the image-based surrogate for cardiac motion with the ECG-based ground truth was excellent yielding a Pearson correlation of 0.93±0.04. Considering motion compensation, the target error measure decreased by 98% and 69%, respectively, for the phantom experiments while for the clinical cases the same figure of merit improved by 46±21%. The proposed method is entirely image-based and accurately estimates craniocaudal shifts due to respiration and cardiac contraction. Future work will investigate experimental trajectories and possibilities for simplification of the single-frame subtraction pipeline.

In volumetric CT imaging of small animals, the breathing motion of the lungs during the image acquisition process results in inconsistent projection data being acquired. When reconstructed, these inconsistent data may produce images with reduced spatial resolution and image contrast. In order to minimize these effects, various approaches have been utilized to capture the respiratory signal of the animals under study and to only obtain CT data at specific moments in the respiratory cycle. These approaches typically utilize hardware-based physiological monitoring equipment in order to record the respiratory signal and either prospectively or retrospectively correlate this signal with acquired CT projection data. In this work, a new method of CT respiratory gating is described that does not rely on external physiological monitoring. Rather, determination of the respiratory phase of the animal is performed by postprocessing the acquired projection data. With this approach, any CT projection data can be respiratory gated with minimal effort. Validation of the method has been performed using a dynamic phantom and accuracy in tidal volumes determined to be within 16%. Rats and mice have been scanned and processed using the proposed method and compared to physiological-based measurement. With the proposed method, image quality is significantly improved in addition to providing quantitative information regarding tidal lung volumes.

To obtain multiphase cardiac cine images with high resolution, a novel self-gating method for both cardiac and respiratory motions is proposed. The proposed method uses the phase of projection data obtained from a separate axial slice to measure cardiac and respiratory motion, after the acquisition of every k-space line in the image plane. Cardiac motion is estimated from the phase of the projection data passing through the aorta, which is amplified by superior-inferior directional bipolar gradients, whereas respiratory motion is estimated from the phase of the left-right directional projection data of the abdomen. To verify the proposed self-gating method, a simulation and in vivo steady state free precession cardiac imaging were performed. The proposed method provides high resolution multiphase cardiac cine images. Using the proposed self-gating method, the phase variation of the projection data offers information about cardiac and respiratory motions that is equivalent to external gating devices. The proposed method can capture time-resolved cardiac and respiratory motion from the phase information of the projection data. Because the projection data is obtained from a separate gating slice, the self-gating signals are not affected by imaging planes. Magn Reson Med 77:1216-1222, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Data consistency conditions (DCC) are mathematical equations characterizing the redundancy in X-ray projections. They have been used to correct inconsistent projections before computed tomography (CT) reconstruction. This article investigates DCC for a helical acquisition with a cylindrical detector, the geometry of most diagnostic CT scanners. The acquired projections are analyzed pair-by-pair. The intersection of each plane containing the two source positions with the corresponding cone-beams defines two fan-beams for which a DCC can be computed. Instead of rebinning the two fan-beam projections to a conventional detector, we directly derive the DCC in detector coordinates. If the line defined by two source positions intersects the field-of-view (FOV), the DCC presents a singularity which is accounted for in our numerical implementation to increase the number of DCC compared to previous approaches which excluded these pairs of source positions. Axial truncation of the projections is addressed by identifying for which set of planes containing the two source positions the fan-beams are not truncated. The ability of these DCC to detect breathing motion has been evaluated on simulated and real projections. Our results indicate that the DCC can detect motion if the baseline and the FOV do not intersect. If they do, the inconsistency due to motion is dominated by discretization errors and noise. We therefore propose to normalize the inconsistency by the noise to obtain a noise-aware metric which is mostly sensitive to inconsistencies due to motion. Combined with a moving average to reduce noise, the derived DCC can detect breathing motion.

Accurate fusion of late gadolinium enhancement magnetic resonance imaging (MRI) and electro-anatomical voltage mapping (EAM) is required to evaluate the potential of MRI to identify the substrate of ventricular tachycardia. However, both datasets are not acquired at the same cardiac phase and EAM data is corrupted with respiratory motion limiting the accuracy of current rigid fusion techniques. Knowledge of cardiac and respiratory motion during EAM is thus required to enhance the fusion process. In this study, we propose a novel approach to characterize both cardiac and respiratory motion from EAM data using the temporal evolution of the 3D catheter location recorded from clinical EAM systems. Cardiac and respiratory motion components are extracted from the recorded catheter location using multi-band filters. Filters are calibrated for each EAM point using estimates of heart rate and respiratory rate. The method was first evaluated in numerical simulations using 3D models of cardiac and respiratory motions of the heart generated from real time MRI data acquired in 5 healthy subjects. An accuracy of 0.6–0.7 mm was found for both cardiac and respiratory motion estimates in numerical simulations. Cardiac and respiratory motions were then characterized in 27 patients who underwent LV mapping for treatment of ventricular tachycardia. Mean maximum amplitude of cardiac and respiratory motion was 10.2±2.7 mm (min = 5.5, max = 16.9) and 8.8±2.3 mm (min = 4.3, max = 14.8), respectively. 3D Cardiac and respiratory motions could be estimated from the recorded catheter location and the method does not rely on additional imaging modality such as X-ray fluoroscopy and can be used in conventional electrophysiology laboratory setting.

Image Guided, Motion-free Patient Body Setup using 3D Volumetric Image Registration of Classified Stable Bony Landmarks

Objective To evaluate the image quality and registration accuracy of a three-dimensional (3D) dynamic phantom in four-dimensional computed tomography (4DCT) and four-dimensional cone-beam computed tomography (4DCBCT). Methods The Computerized Imaging Reference Systems Dynamic Thorax Phantom Model 008A was scanned to get 4DCT and 4DCBCT images. Two balls with different diameters (O= 1 cm and O= 2 cm) were used to simulate tumors with different sizes. The motion mode of the balls was 3D sinusoidal motion at 0.25 Hz (the amplitudes along the x, y, and z axes were ±1.0 cm, ±0.4 cm, and ±0.2 cm, respectively). Gross target volumes (GTVs) from 10-phase bins, internal gross target volumes (IGTV), and target volumes on maximum intensity projection (MIP) and mean intensity projection (MeanIP) images were contoured and calculated. Target volumes on 4DCT or 4DCBCT images were compared with the static and dynamic volumes of the balls (VS and VD). The matching index (MI) of target volumes between the 4DCT and 4DCBCT images was analyzed after rigid image registration. Results The GTV in each phase of the image was larger than VS. The difference between the average GTV derived from 10 phases of 4DCT or 4DCBCT images and Vs of the small ball was larger than that of the large ball (35.03% vs. 22.66%; 32.62% vs. 17.00%). All the IGTVs and target volumes on MIP images were slightly larger than VD, but target volumes on MeanIP images were smaller than VD. The average MI of 10-phase bins of the small ball was smaller than that of the large ball (66.76% vs. 82.21%). Moreover, MIs of IGTV, MIP, and MeanIP of the small ball were also smaller than those of the large ball (77.39% vs. 90.29%; 75.90% vs. 89.28%; 74.47% vs. 82.74%). Conclusions In the case of a relatively small tumor volume and a relatively large motion amplitude, 4DCT and 4DCBCT should be used with caution for comparison of image registration. Key words: Tomography, X-ray computed; Tomography, X-ray computed, cone-beam; Imaging registration; Phantom

To assess the impact of motion artefacts and motion-artefact correction on diagnostic accuracy of apical periodontitis (AP) in CBCT images. Based on clinical and radiographic inspection of 40 formalin-fixated human jaw specimens, 77 roots in 45 teeth (molars and premolars), with various disease and treatment state, were selected. The specimens were mounted on a robot simulating 3-mm movement types (nodding, lateral rotation and tremor). CBCT images with and without (controls) movements were acquired in four CBCT units: without motion-artefact correction in Cranex 3Dx, Orthophos SL 3D, and Promax 3D Mid, and with motion-artefact correction in Promax 3D Mid and X1. Three observers blindly assessed (i) whether the images were interpretable and (ii) if AP was present (5-step probability index). Histopathology provided the reference standard for presence of AP. Weighted Kappa statistics described inter-observer agreement. Estimates of diagnostic accuracy were assessed by means of receiver operator characteristic (ROC) curve analysis. Area under the curve (AUC) provided a measure of accuracy, and paired-sample AUC difference tests compared differences amongst the CBCT units and movement types. Observer agreement was substantial for control images, moderate for motion-artefact corrected images and fair for images without motion-artefact correction. When movement was present, motion-artefact correction reduced the percentage of images scored as noninterpretable or with uncertain disease state (score 3 in the 5-step probability index). Control images were not perfectly accurate (both false-positive and false-negative results were present; AUC 0.750-0.799). Images acquired with movement and without motion-artefact correction (AUC 0.541-0.709) were associated with significantly lower accuracy than control images (P<0.05). With motion-artefact correction, accuracy was comparable to that observed in control images (AUC 0.732-0.790). Diagnostic accuracy of apical periodontitis in CBCT images was dependent on the presence of motion artefacts (i.e. lower accuracy associated with the presence of movement). Motion-artefact correction systems positively influenced image interpretability and diagnostic accuracy.

Respiratory motion (RM) and cardiac motion (CM) degrade the quality and resolution in cardiac PET scans. We have developed non-rigid motion estimation methods to estimate both RM and CM based on 4D cardiac gated PET data alone, and compensate the dual respiratory and cardiac (R&C) motions after (MCAR), during (MCDR), and before (MCBR) image reconstruction. In all three R&C motion correction methods, attenuation-activity mismatch effect was modeled by using transformed attenuation maps using the estimated RM. The difference of using activity preserving and non-activity preserving models in R&C correction was also studied. Realistic Monte Carlo simulated 4D cardiac PET data using the 4D XCAT phantom and accurate models of the scanner design parameters and performance characteristics at different noise levels were employed as the known truth and for method development and evaluation. Results from the simulation study suggested that all three dual R&C motion correction methods provide substantial improvement in the quality of 4D cardiac gated PET images as compared with no motion correction. Specifically, the MCDR method yields the best performance for all different noise levels compared with the MCAR and MCBR methods. While MCBR reduces computational time dramatically but the resultant 4D cardiac gated PET images has overall inferior image quality when compared to that from the MCAR and MCDR approaches in the ‘almost’ noise free case. Also, the MCBR method has better noise handling properties when compared with MCAR and provides better quantitative results in high noise cases. When the goal is to reduce scan time or patient radiation dose, MCDR and MCBR provide a good compromise between image quality and computational times.

Usage of the backprojection filtration (BPF) algorithm for reconstructing images from motion-contaminated fan-beam data may result in motion-induced streak artifacts, which appear in the direction of the chords on which images are reconstructed. These streak artifacts, which are most pronounced along chords tangent to the edges of the moving object, may be suppressed by use of the weighted BPF (WBPF) algorithm, which can exploit the inherent redundancies in fan-beam data. More specifically, reconstructions using full-scan and short-scan data can allow for substantial suppression of these streaks, whereas those using reduced-scan data can allow for partial suppression. Since multiple different reconstructions of the same chord can be obtained by varying the amount of redundant data used, we have laid the groundwork for a possible method to characterize the amount of motion encoded within the data used for reconstructing an image on a particular chord. Furthermore, since motion artifacts in WBPF reconstructions using full-scan and short-scan data appear similar to those in corresponding fan-beam filtered backprojection (FFBP) reconstructions for the cases performed in this study, the BPF and WBPF algorithms potentially may be used to arrive at a more fundamental characterization of how motion artifacts appear in FFBP reconstructions.

Cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) acquisition presents novel clinical applications thanks to the combination of viability and metabolic imaging (PET) and functional and structural imaging (MRI). However, the resolution of PET, as well as cardiac and respiratory motion in nongated cardiac imaging acquisition protocols, leads to a reduction in image quality and severe quantitative bias. Respiratory or cardiac motion is customarily addressed with gated reconstruction which results in higher noise. Inspired by a method that has been used in brain PET, a practical correction approach, designed to overcome these existing limitations for quantitative PET imaging, was developed and applied in the context of cardiac PET/MRI. The correction approach for PET data consists of computing the mean density map of each underlying moving region, as obtained with MRI, and translating them to the PET space taking into account the PET spatial and temporal resolution. Using these tissue density maps, the method then constructs a system of linear equations that models the activity recovery and cross-contamination coefficients, which can be solved for the true activity values. Physical and numerical cardiac phantoms were employed in order to quantify the proposed correction. The full correction pipeline was then used to assess differences in metabolic function between scar and healthy myocardium in eight patients with recent acute myocardial infarction using [11 C]-acetate. Data from ten additional patients, injected with [18 F]-FDG, were used to compare the method to the standard electrocardiography (ECG)-gated approach. The proposed method resulted in better recovery (from 32% to 95% on the simulated phantom model) and less residual activity than the standard approach. Higher signal-to-noise and contrast-to-noise ratios than ECG-gating were also witnessed (Signal-to-noise ratio (SNR) increased from 2.92 to 5.24, contrast-to-noise ratio (CNR) increased from 62.9 to 145.9 when compared to a four-gate reconstruction). Finally, the relevance of this correction using [11 C]-acetate PET patient data, for which erroneous physiological conclusions could have been made based on the uncorrected data, was established as the correction led to the expected clinical results. An efficient and simple method to correct for the quantitative biases in PET measurements caused by cardiac motion has been developed. Validation experiments using phantom and patient data showed improved accuracy and reliability with this approach when compared to simpler strategies such as gated acquisition or optimal regions of interest (ROI).

Dose and image quality are two important parameters in CT Scan examination. The radiation dose produced at the CT scan is far greater than the radiation dose of the conventional radiological examination, so the utilization must be as good as possible. On Siemens CT Scan there is an automatic exposure control software, with expectations that the radiation dose received by the patient can be reduced because the software is an auto mA that is adjusted to the thickness of the object, but the impact of using the software causes a decrease in image quality, so it needs to be done image improvement using a back projection filter (BPF). Experimental research by applying a back projection filter to the Scan Head image on axial sections. Image assessment using 2 radiologists with data analysis using statistical processing and image post processing. CT Scan image information Head axial pieces experience differences in image information between before and after the back projection, with the mean value in each group experiencing an increase in image quality in the group after a back projection filter. Where the image after the back projection filter by applying automatic exposure control with kV 110 is the most optimal kV.

This paper presents an improved CT (Computed Tomography) image reconstruction algorithm, called PB-BPF (Parallel-Beam Back projection Filtration). It mains that the image is reconstructed under the parallel beam scanning mode. It doesn't need to resample to get the image, and it simplifies the usual BPF (Back projection Filtration) in helical cone-beam CT. At First, The projection data is differentiated by using the Shepp-Logan model. Then, the image is reconstructed by choosing parallel PI beams to back-projection, Hilbert transform and constant compensation. At last, the image quality is assessed by using peak signal-to-noise ratio and structural similarity. Being compared with FBP(Filtered Back Projection), the results indicate that the improved algorithm can reconstruct the image accurately, at the same time, it is more close to human visual system.

The purpose of this study was to survey the radiation dose used in CT urography (CTU) in routine clinical practice, both before and after implementation of a scanning protocol that uses iterative reconstruction (Adaptive Iterative Dose Reduction 3D [AIDR 3D]). We retrospectively surveyed dose reports from consecutive CTU examinations performed in 2011 with the use of 64- and 320-MDCT scanners that were reconstructed with filtered back projection (FBP) and from CTU examinations performed from May 2012 through November 2013 that were reconstructed with the use of AIDR 3D. Findings from these dose reports were then correlated with such patient characteristics as weight and body mass index (BMI; weight in kilograms divided by the square of height in meters). Only dose reports from single-bolus three-phase CTU examinations were included in the study. The volume CT dose index, dose-length product (DLP), and effective dose were surveyed both per examination and per phase by use of published effective dose DLP conversion factors. Image quality was evaluated subjectively for a subset of patients. The two study cohorts included 82 patients (median patient weight, 75.0 kg; median BMI, 25.3) who underwent CTU with FBP and 85 patients (median patient weight, 78.0 kg; median BMI, 24.5) who underwent CTU with AIDR 3D. The median total DLP and median effective dose were 924 mGy · cm and 13.0 mSv, respectively, in the CTU with the FBP cohort and 433 mGy · cm and 6.1 mSv, respectively, in the CTU with the AIDR 3D cohort. The median DLP in the unenhanced, nephrogenic, and excretory phases was 218, 300, and 441 mGy · cm, respectively, in patients undergoing CTU with FBP and 114, 121, and 190 mGy · cm, respectively, in patients undergoing CTU with AIDR 3D. Image quality was diagnostic in both groups, with relatively fewer artifacts noted on scans obtained using CTU with AIDR 3D. Our study presents detailed dose data from three-phase CTU examinations performed both before and after implementation of iterative reconstruction. Implementation of a CTU protocol using iterative reconstruction resulted in a mean effective dose of 6.1 mSv with preservation of clinical diagnostic image quality.