Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT

An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans

Objective.Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely on retrospective rescans, which increase radiation exposure and are less effective for patients with irregular heart rhythms. In this study, we proposed a deep learning-based end-to-end reconstruction framework for dynamic cardiac imaging using single-beat rapid CT scanning.Approach.The method used common cardiac CT projections and applied a sliding-window approach to divide the projection data into overlapping short-scan intervals centered on specific cardiac phases. Each short-scan interval was first reconstructed and then processed through a denoising network, before being fed into the registration module to compute deformation vector fields (DVFs). These DVFs were then used to perform motion-compensated reconstruction. The denoising and registration networks were trained end-to-end by minimizing the difference between the reconstructed images and ground truth in a supervised manner. The model was trained using simulated projection data from 30 real patients and validated on simulated datasets from different institutions and XCAT-generated continuous phantoms.Main results.Experimental results showed that the proposed method effectively reduced motion artifacts and restored key anatomical structures such as coronary arteries. On high-resolution test cases, SSIM improved from 0.7234 to 0.7795, peak signal-to-noise ratio from 35.40 to 37.58, and root mean square error (HU) decreased from 63.98 to 49.28. Additional evaluations showed consistent improvements in segmentation accuracy, with Dice similarity coefficient scores for the left ventricle, coronary arteries, and calcified plaques increasing from 0.8025, 0.7347, and 0.5966 to 0.9614, 0.8811, and 0.7774, respectively.Significance.By relying solely on single-cycle scan data and placing no explicit restrictions on heart rate, the method demonstrated strong potential for generalizability and wider clinical applications.

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods heavily rely on large labeled training datasets which are difficult to obtain in practical scenarios. Restricted by this dilemma, DL models often struggle with simultaneously retaining dynamic motions, removing streak degradations, and recovering fine details. To address the above challenging problem, we introduce a Deep Prior Image Constrained Motion Compensation framework (DPI-MoCo) that decouples the 4D CBCT reconstruction into two sub-tasks including coarse image restoration and structural detail fine-tuning. In the first stage, the proposed DPI-MoCo combines the prior image guidance, generative adversarial network, and contrastive learning to globally suppress the artifacts while maintaining the respiratory movements. After that, to further enhance the local anatomical structures, the motion estimation and compensation technique is adopted. Notably, our framework is performed without the need for paired datasets, ensuring practicality in clinical cases. In the Monte Carlo simulation dataset, the DPI-MoCo achieves competitive quantitative performance compared to the state-of-the-art (SOTA) methods. Furthermore, we test DPI-MoCo in clinical lung cancer datasets, and experiments validate that DPI-MoCo not only restores small anatomical structures and lesions but also preserves motion information.

A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications

Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D-CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x-ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low-dose, and high-quality 4D-CBCT. This paper aims to review the technical developments surrounding 4D-CBCT comprehensively. It will explore conventional algorithms and recent deep learning-based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D-CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D-CBCT's capabilities and potential to enhance radiation therapy.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing).Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning.Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy.Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics.

Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction

Objective.Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.Approach.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Main results.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.Significance.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction

BACKGROUND

Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.

PURPOSE

This study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.

METHODS

A 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).

RESULTS

The trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm^-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.

CONCLUSIONS

CNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks

BACKGROUND

Although four-dimensional cone-beam computed tomography (4D-CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D-CBCT scanning time while maintaining high-quality images. Current motion-compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling.

PURPOSE

In this work, we aim to propose an alternative feature-compensated method to realize the fast 4D-CBCT with high-quality images.

METHODS

We proposed a feature-compensated deformable convolutional network (FeaCo-DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo-DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high-quality images of the target phase. The proposed FeaCo-DCN was evaluated using lung cancer patient data.

RESULTS

(1) FeaCo-DCN generated high-quality images with accurate and clear structures for a fast 4D-CBCT scan; (2) 4D-CBCT images reconstructed by FeaCo-DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo-DCN achieved superior performance by all metrics compared to the top-ranked techniques in the AAPM SPARE Challenge.

CONCLUSION

The proposed FeaCo-DCN is effective and efficient in reconstructing 4D-CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image-guided radiotherapy.

Cardio-respiratory motion-corrected 3D cardiac water-fat MRI using model-based image reconstruction

PURPOSE

Myocardial fat infiltrations are associated with a range of cardiomyopathies. The purpose of this study was to perform cardio-respiratory motion-correction for model-based water-fat separation to image fatty infiltrations of the heart in a free-breathing, non-cardiac-triggered high-resolution 3D MRI acquisition.

METHODS

Data were acquired in nine patients using a free-breathing, non-cardiac-triggered high-resolution 3D Dixon gradient-echo sequence and radial phase encoding trajectory. Motion correction was combined with a model-based water-fat reconstruction approach. Respiratory and cardiac motion models were estimated using a dual-mode registration algorithm incorporating both motion-resolved water and fat information. Qualitative comparisons of fat structures were made between 2D clinical routine reference scans and reformatted 3D motion-corrected images. To evaluate the effect of motion correction the local sharpness of epicardial fat structures was analyzed for motion-averaged and motion-corrected fat images.

RESULTS

The reformatted 3D motion-corrected reconstructions yielded qualitatively comparable fat structures and fat structure sharpness in the heart as the standard 2D breath-hold. Respiratory motion correction improved the local sharpness on average by 32% ± 24% with maximum improvements of 81% and cardiac motion correction increased the sharpness further by another 15% ± 11% with maximum increases of 31%. One patient showed a fat infiltration in the myocardium and cardio-respiratory motion correction was able to improve its visualization in 3D.

CONCLUSION

The 3D water-fat separated cardiac images were acquired during free-breathing and in a clinically feasible and predictable scan time. Compared to a motion-averaged reconstruction an increase in sharpness of fat structures by 51% ± 27% using the presented motion correction approach was observed for nine patients.

A deep unsupervised learning framework for the 4D CBCT artifact correction

Objective. Four-dimensional cone-beam computed tomography (4D CBCT) has unique advantages in moving target localization, tracking and therapeutic dose accumulation in adaptive radiotherapy. However, the severe fringe artifacts and noise degradation caused by 4D CBCT reconstruction restrict its clinical application. We propose a novel deep unsupervised learning model to generate the high-quality 4D CBCT from the poor-quality 4D CBCT. Approach. The proposed model uses a contrastive loss function to preserve the anatomical structure in the corrected image. To preserve the relationship between the input and output image, we use a multilayer, patch-based method rather than operate on entire images. Furthermore, we draw negatives from within the input 4D CBCT rather than from the rest of the dataset. Main results. The results showed that the streak and motion artifacts were significantly suppressed. The spatial resolution of the pulmonary vessels and microstructure were also improved. To demonstrate the results in the different directions, we make the animation to show the different views of the predicted correction image in the supplementary animation. Significance. The proposed method can be integrated into any 4D CBCT reconstruction method and maybe a practical way to enhance the image quality of the 4D CBCT.

Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN)

4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images reconstructed by compressed sensing (CS)-based methods suffer from blurred edges. Introducing the average-4D-image constraint to the CS-based reconstruction, such as prior-image-constrained CS (PICCS), can improve the edge sharpness of the stable structures. However, PICCS can lead to motion artifacts in the moving regions. In this study, we proposed a dual-encoder convolutional neural network (DeCNN) to realize the average-image-constrained 4D-CBCT reconstruction. The proposed DeCNN has two parallel encoders to extract features from both the under-sampled target phase images and the average images. The features are then concatenated and fed into the decoder for the high-quality target phase image reconstruction. The reconstructed 4D-CBCT using of the proposed DeCNN from the real lung cancer patient data showed (1) qualitatively, clear and accurate edges for both stable and moving structures; (2) quantitatively, low-intensity errors, high peak signal-to-noise ratio, and high structural similarity compared to the ground truth images; and (3) superior quality to those reconstructed by several other state-of-the-art methods including the back-projection, CS total-variation, PICCS, and the single-encoder CNN. Overall, the proposed DeCNN is effective in exploiting the average-image constraint to improve the 4D-CBCT image quality.

CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.

Evaluation of Ultra-High-Resolution Cone-Beam CT Prototype of Twin Robotic Radiography System for Cadaveric Wrist Imaging

RATIONALE AND OBJECTIVES

Cone-beam CT (CBCT) applications possess potential for dose reduction in musculoskeletal imaging. This study evaluates the ultra-high-resolution CBCT prototype of a twin robotic X-ray system in wrist examinations compared to high-resolution multidetector CT (MDCT).

MATERIALS AND METHODS

Sixteen wrists of body donors were examined with the CBCT scan mode and a 384 slice MDCT system. Radiation-equivalent low-dose (CTDI_vol(16cm)  = 3.3 mGy) and full-dose protocols (CTDI_vol(16cm)  = 13.8 mGy) were used for both systems. Two observers assessed image quality on a seven-point Likert scale. In addition, software-assisted quantification of signal intensity fractions in cancellous bone was performed. Fewer pixels with intermediate signal intensity were considered to indicate superior depiction of bone microarchitecture.

RESULTS

Subjective image quality in CBCT was superior to dose equivalent MDCT with p ≤ 0.03 for full-dose and p < 0.001 for low-dose scans, respectively. Median Likert values were 7/7 (reader 1 / reader 2) in full-dose CBCT, 6/6 in full-dose MDCT, 5/6 in low-dose CBCT and 3/3 in low-dose MDCT. Intraclass correlation coefficient was 0.936 (95% confidence interval, 0.897-0.961; p < 0.001), indicating excellent reliability. Objective analysis displayed smaller fractions of "indecisive" pixels with intermediate signal intensity for full-dose CBCT (0.57 [interquartile range 0.13]) compared to full-dose MDCT (0.68 [0.21]), low-dose CBCT (0.72 [0.19]), and low-dose MDCT (0.80 [0.15]) studies. No significant difference was observed between low-dose CBCT and full-dose MDCT.

CONCLUSION

The new CBCT prototype provides superior image quality for trabecula and bone marrow in cadaveric wrist studies and enables dose reduction up to 75% compared to high-resolution MDCT.

Spatiotemporal structure-aware dictionary learning-based 4D CBCT reconstruction

PURPOSE

Four-dimensional cone-beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase-resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse-view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. The purpose of this paper was to develop a new 4D CBCT reconstruction method to generate a series of high spatiotemporal 4D CBCT images.

METHODS

Considering the advantage of (DL) on representing structural features and correlation between neighboring pixels effectively, we construct a novel DL-based method for the 4D CBCT reconstruction. In this study, both a motion-aware dictionary and a spatially structural 2D dictionary are trained for 4D CBCT by excavating the spatiotemporal correlation among ten phase-resolved images and the spatial information in each image, respectively. Specifically, two reconstruction models are produced in this study. The first one is the motion-aware dictionary learning-based 4D CBCT algorithm, called motion-aware DL based 4D CBCT (MaDL). The second one is the MaDL equipped with a prior knowledge constraint, called pMaDL. Qualitative and quantitative evaluations are performed using a 4D extended cardiac torso (XCAT) phantom, simulated patient data, and two sets of patient data sets. Several state-of-the-art 4D CBCT algorithms, such as the McKinnon-Bates (MKB) algorithm, prior image constrained compressed sensing (PICCS), and the high-quality initial image-guided 4D CBCT reconstruction method (HQI-4DCBCT) are applied for comparison to validate the performance of the proposed MaDL and prior constraint MaDL (pMaDL) pmadl reconstruction frameworks.

RESULTS

Experimental results validate that the proposed MaDL can output the reconstructions with few streaking artifacts but some structural information such as tumors and blood vessels, may still be missed. Meanwhile, the results of the proposed pMaDL demonstrate an improved spatiotemporal resolution of the reconstructed 4D CBCT images. In these improved 4D CBCT reconstructions, streaking artifacts are suppressed primarily and detailed structures are also restored. Regarding the XCAT phantom, quantitative evaluations indicate that an average of 58.70%, 45.25%, and 40.10% decrease in terms of root-mean-square error (RMSE) and an average of 2.10, 1.37, and 1.37 times in terms of structural similarity index (SSIM) are achieved by the proposed pMaDL method when compared with piccs, PICCS, MaDL(2D), and MaDL(2D), respectively. Moreover the proposed pMaDL achieves a comparable performance with HQI-4DCBCT algorithm in terms of RMSE and SSIM metrics. However, pMaDL has a better ability to suppress streaking artifacts than HQI-4DCBCT.

CONCLUSIONS

The proposed algorithm could reconstruct a set of 4D CBCT images with both high spatiotemporal resolution and detailed features preservation. Moreover the proposed pMaDL can effectively suppress the streaking artifacts in the resultant reconstructions, while achieving an overall improved spatiotemporal resolution by incorporating the motion-aware dictionary with a prior constraint into the proposed 4D CBCT iterative framework.

Adjoint image warping using multivariate splines with application to four-dimensional computed tomography

Purpose

Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives.

Methods

Using symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on‐the‐fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D‐CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion.

Results

Our method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general.

Conclusions

Our implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions.

The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose

BACKGROUND AND PURPOSE

We present the first implementation of Adaptive 4D cone beam CT (4DCBCT) that adapts the image hardware (gantry rotation speed and kV projections) in response to the patient's real-time respiratory signal. Adaptive 4DCBCT was applied on lung cancer patients to reduce the scan time and imaging dose in the ADaptive CT Acquisition for Personalised Thoracic imaging (ADAPT) trial.

MATERIALS AND METHODS

The ADAPT technology measures the patient's real-time respiratory signal and uses mathematical optimisation and external circuitry attached to the linear accelerator to modulate the gantry rotation speed and kV projection rate to reduce scan times and imaging dose. For each patient, ADAPT scans were acquired on two treatment fractions and reconstructed with a motion compensated reconstruction algorithm and compared to the current state-of-the-art four-minute 4DCBCT acquisition (conventional 4DCBCT). We report on the scan time, imaging dose and image quality for the first four adaptive 4DCBCT patients.

RESULTS

The ADAPT imaging dose was reduced by 85% and scan times were 73 ± 12 s representing a 70% reduction compared to the 240 s conventional 4DCBCT scan. The contrast-to-noise ratio was improved from 9.2 ± 3.9 with conventional 4DCBCT to 11.7 ± 4.1 with ADAPT.

DISCUSSION

The ADAPT trial represents the first time that gantry rotation speed and projection acquisition have been adapted and optimised in real-time in response to changes in the patient's breathing. ADAPT demonstrates substantially reduced scan times and imaging dose for clinical 4DCBCT imaging that could enable more efficient and optimised lung cancer radiotherapy.

Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT)

PURPOSE

The number of pencil beam scanned proton therapy (PBS-PT) facilities equipped with cone-beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS-PT treatments of thoracic tumors, we assess the performance of Motion-Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA-ROOSTER) four-dimensional CBCT (4DCBCT) reconstruction for sparse-view CBCT data and a realistic data set of patients treated with proton therapy.

METHODS

Daily CBCT projection data for nine non-small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four-dimensional CBCT images were reconstructed applying the MA-ROOSTER and the conventional phase-correlated Feldkamp-Davis-Kress (PC-FDK) method. Image quality was assessed by visual inspection, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated.

RESULTS

Image quality for the 4DCBCT reconstructions using MA-ROOSTER was superior to the PC-FDK reconstructions and close to FDK images (median CNR: 1.23 [PC-FDK], 1.98 [MA-ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC-FDK], 4.76 [MA-ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC-FDK vs FDK], 0.31 [MA-ROOSTER vs FDK]). The improved image quality of MA-ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions.

CONCLUSION

MA-ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS-PT treatments of (N)SCLC patients.

Coronary micro-computed tomography angiography in mice

Coronary computed tomography angiography is an established technique in clinical practice and a valuable tool in the diagnosis of coronary artery disease in humans. Imaging of coronaries in preclinical research, i.e. in small animals, is very difficult due to the high demands on spatial and temporal resolution. Mice exhibit heart rates of up to 600 beats per minute motivating the need for highest detector framerates while the coronaries show diameters below 100 μm indicating the requirement for highest spatial resolution. We herein use a custom built micro-CT equipped with dedicated reconstruction algorithms to illustrate that coronary imaging in mice is possible. The scanner provides a spatial and temporal resolution sufficient for imaging of smallest, moving anatomical structures and the dedicated reconstruction algorithms reduced radiation dose to less than 1 Gy but do not yet allow for longitudinal studies. Imaging studies were performed in ten mice administered with a blood-pool contrast agent. Results show that the course of the left coronary artery can be visualized in all mice and all major branches can be identified for the first time using micro-CT. This reduces the gap in cardiac imaging between clinical practice and preclinical research.

From Mouse to Man and Back: Closing the Correlation Gap between Imaging and Histopathology for Lung Diseases

Lung diseases such as fibrosis, asthma, cystic fibrosis, infection and cancer are life-threatening conditions that slowly deteriorate quality of life and for which our diagnostic power is high, but our knowledge on etiology and/or effective treatment options still contains important gaps. In the context of day-to-day practice, clinical and preclinical studies, clinicians and basic researchers team up and continuously strive to increase insights into lung disease progression, diagnostic and treatment options. To unravel disease processes and to test novel therapeutic approaches, investigators typically rely on end-stage procedures such as serum analysis, cyto-/chemokine profiles and selective tissue histology from animal models. These techniques are useful but provide only a snapshot of disease processes that are essentially dynamic in time and space. Technology allowing evaluation of live animals repeatedly is indispensable to gain a better insight into the dynamics of lung disease progression and treatment effects. Computed tomography (CT) is a clinical diagnostic imaging technique that can have enormous benefits in a research context too. Yet, the implementation of imaging techniques in laboratories lags behind. In this review we want to showcase the integrated approaches and novel developments in imaging, lung functional testing and pathological techniques that are used to assess, diagnose, quantify and treat lung disease and that may be employed in research on patients and animals. Imaging approaches result in often novel anatomical and functional biomarkers, resulting in many advantages, such as better insight in disease progression and a reduction in the numbers of animals necessary. We here showcase integrated assessment of lung disease with imaging and histopathological technologies, applied to the example of lung fibrosis. Better integration of clinical and preclinical imaging technologies with pathology will ultimately result in improved clinical translation of (therapy) study results.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

High-quality initial image-guided 4D CBCT reconstruction

PURPOSE

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution.

METHODS

The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method.

RESULTS

The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method.

CONCLUSIONS

The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion-induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan

PURPOSE

Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms.

METHODS

A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV.

RESULTS

Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy.

CONCLUSION

The SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Data-driven respiratory motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) using groupwise deformable registration

PURPOSE

To demonstrate the feasibility of using a purely data-driven, a posteriori respiratory motion modeling and reconstruction compensation method to improve 4D-CBCT image quality under clinically relevant image acquisition conditions.

METHODS

Evaluated workflows that utilized a combination of groupwise deformable image registration and motion-compensated image reconstruction algorithms. Groupwise registration is an approach that simultaneously registers all temporal frames of a 4D image to a common reference instead of one at a time so as to minimize the influence of any individual time point on the global smoothness or accuracy of the resulting deformation model. Four-dimensional cone-beam CT (4D-CBCT) Feldkamp-Davis-Kress (FDK) reconstructions were registered to either iteratively computed mean respiratory phase (mean-frame) or preselected respiratory phase (fixed-frame) reference images to model respiratory motion. The resulting 4D transformations were used to deform projection data during the FDK backprojection operation to create motion-compensated reconstructions. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to a mobile tissue boundary and was used to evaluate image quality in regions susceptible to motion artifacts. Image quality improvement was assessed for 19 clinical cases by evaluating mitigation of view aliasing artifacts, TIS, image noise reduction, and contrast for implanted fiducial markers.

RESULTS

Average (standard deviation) diaphragm TIS recovery relative to initial 4D-CBCT reconstructions was observed to be 87% (46%) using fixed-frame registration alone; 87% (47%) using fixed frame with motion-compensated reconstruction; 101% (68%) using mean-frame registration alone; and 99% (65%) using mean frame with motion-compensated reconstruction. Noise was reduced in sampled soft tissue ROIs by 58% for both fixed-frame registration and registration with motion compensation and by 57% and 58% on average for the corresponding mean-frame methods, respectively. Average improvement in local CNR was observed to be respectively 93% and 98% for fixed-frame registration and registration with motion compensation methods and 116% and 111% for the corresponding mean-frame methods.

CONCLUSION

Data-driven groupwise registration and motion-compensated reconstruction offer a feasible means of improving the quality of 4D-CBCT images acquired under clinical conditions. The addition of motion compensation reconstruction after groupwise registration visibly reduced the impact of view aliasing artifacts for the clinical image datasets studied.

Evaluation of respiratory motion-corrected cone-beam CT at end expiration in abdominal radiotherapy sites: a prospective study

BACKGROUND

Cone beam computed tomography (CBCT) for radiotherapy image guidance suffers from respiratory motion artifacts. This limits soft tissue visualization and localization accuracy, particularly in abdominal sites. We report on a prospective study of respiratory motion-corrected (RMC)-CBCT to evaluate its efficacy in localizing abdominal organs and improving soft tissue visibility at end expiration.

MATERIAL AND METHODS

In an IRB approved study, 11 patients with gastroesophageal junction (GEJ) cancer and five with pancreatic cancer underwent a respiration-correlated CT (4DCT), a respiration-gated CBCT (G-CBCT) near end expiration and a one-minute free-breathing CBCT scan on a single treatment day. Respiration was recorded with an external monitor. An RMC-CBCT and an uncorrected CBCT (NC-CBCT) were computed from the free-breathing scan, based on a respiratory model of deformations derived from the 4DCT. Localization discrepancy was computed as the 3D displacement of the GEJ region (GEJ patients), or gross tumor volume (GTV) and kidneys (pancreas patients) in the NC-CBCT and RMC-CBCT relative to their positions in the G-CBCT. Similarity of soft-tissue features was measured using a normalized cross correlation (NCC) function.

RESULTS

Localization discrepancy from the end-expiration G-CBCT was reduced for RMC-CBCT compared to NC-CBCT in eight of eleven GEJ cases (mean ± standard deviation, respectively, 0.21 ± 0.11 and 0.43 ± 0.28 cm), in all five pancreatic GTVs (0.26 ± 0.21 and 0.42 ± 0.29 cm) and all ten kidneys (0.19 ± 0.13 and 0.51 ± 0.25 cm). Soft-tissue feature similarity around GEJ was higher with RMC-CBCT in nine of eleven cases (NCC =0.48 ± 0.20 and 0.43 ± 0.21), and eight of ten kidneys (0.44 ± 0.16 and 0.40 ± 0.17).

CONCLUSIONS

In a prospective study of motion-corrected CBCT in GEJ and pancreas, RMC-CBCT yielded improved organ visibility and localization accuracy for gated treatment at end expiration in the majority of cases.

Two methods for reducing moving metal artifacts in cone-beam CT

PURPOSE

In image-guided radiation therapy, fiducial markers or clips are often used to determine the position of the tumor. These markers lead to streak artifacts in cone-beam CT (CBCT) scans. Standard inpainting-based metal artifact reduction (MAR) methods fail to remove these artifacts in cases of large motion. We propose two methods to effectively reduce artifacts caused by moving metal inserts.

METHODS

The first method (MMAR) utilizes a coarse metal segmentation in the image domain and a refined segmentation in the rawdata domain. After an initial reconstruction, metal is segmented and forward projected giving a coarse metal mask in the rawdata domain. Inside the coarse mask, metal is segmented by utilizing a 2D Sobel filter. Metal is removed by linear interpolation in the refined metal mask. The second method (MoCoMAR) utilizes a motion compensation (MoCo) algorithm [Med Phys. 2013;40:101913] that provides us with a motion-free volume (3D) or with a time series of motion-free volumes (4D). We then apply the normalized metal artifact reduction (NMAR) [Med Phys. 2010;37:5482-5493] to these MoCo volumes. Both methods were applied to three CBCT data sets of patients with metal inserts in the thorax or abdomen region and a 4D thorax simulation. The results were compared to volumes corrected by a standard MAR1 [Radiology. 1987;164:576-577].

RESULTS

MMAR and MoCoMAR were able to remove all artifacts caused by moving metal inserts for the patients and the simulation. Both new methods outperformed the standard MAR1, which was only able to remove artifacts caused by metal inserts with little or no motion.

CONCLUSIONS

In this work, two new methods to remove artifacts caused by moving metal inserts are introduced. Both methods showed good results for a simulation and three patients. While the first method (MMAR) works without any prior knowledge, the second method (MoCoMAR) requires a respiratory signal for the MoCo step and is computationally more demanding and gives no benefit over MMAR, unless MoCo images are desired.

A modified McKinnon-Bates (MKB) algorithm for improved 4D cone-beam computed tomography (CBCT) of the lung

PURPOSE

Four-dimensional (4D) cone-beam computed tomography (CBCT) of the lung is an effective tool for motion management in radiotherapy but presents a challenge because of slow gantry rotation times. Sorting the individual projections by breathing phase and using an established technique such as Feldkamp-Davis-Kress (FDK) to generate corresponding phase-correlated (PC) three-dimensional (3D) images results in reconstructions (FDK-PC) that often contain severe streaking artifacts due to the sparse angular sampling distributions. These can be reduced by further slowing down the gantry at the expense of incurring unwanted increases in scan times and dose. A computationally efficient alternative is the McKinnon-Bates (MKB) reconstruction algorithm that has shown promise in reducing view aliasing-induced streaking but can produce ghosting artifacts that reduce contrast and impede the determination of motion trajectories. The purpose of this work was to identify and correct shortcomings in the MKB algorithm.

METHODS

In the general MKB approach, a time-averaged 3D prior image is first reconstructed. The prior is then forward-projected at the same angles as the original projection data creating time-averaged reprojections. These reprojections are subsequently subtracted from the original (unblurred) projections to create motion-encoded difference projections. The difference projections are reconstructed into PC difference images that are added to the well-sampled 3D prior to create the higher quality 4D image. The cause of the ghosting in the traditional 4D MKB images was studied and traced to motion-induced streaking in the prior that, when reprojected, has the undesirable effect of re-encoding for motion in what should be a purely time-averaged reprojection. A new method, designated as the modified McKinnon-Bates (mMKB) algorithm, was developed based on destreaking the prior. This was coupled with a postprocessing 4D bilateral filter for noise suppression and edge preservation (mMKBbf ). The algorithms were tested with the 4D XCAT phantom using four simulated scan times (57, 60, 120, 180 s) and with two in vivo thorax studies (acquisition time of 60 and 90 s). Contrast-to-noise ratios (CNRs) of the target lesions and overall visual quality of the images were assessed.

RESULTS

Prior destreaking (mMKB algorithm) reduced ghosting artifacts and increased CNRs for all cases, with the biggest impacts seen in the end inhale (EI) and end exhale (EE) phases of the respiratory cycle. For the XCAT phantom, mMKB lesion CNR was 44% higher than the MKB lesion CNR and was 81% higher than the FDK-PC lesion CNR (EI and EE phases). The bilateral filter provided a further average CNR improvement of 87% with the highest increases associated with longer scan times. Across all phases and scan times, the maximum mMKBbf -to-FDK-PC CNR improvement was over 300%. In vivo results agreed with XCAT results. Significantly less ghosting was observed throughout the mMKB images including near the lesions-of-interest and the diaphragm allowing for, in one case, visualization of a small tumor with nearly 30 mm of motion. The maximum FDK-PC-to-MKBbf CNR improvement for Patient 1's lesion was 261% and for Patient 2's lesion was 318%.

CONCLUSIONS

The 4D mMKB algorithm yields good quality coronal and sagittal images in the thorax that may provide sufficient information for patient verification.

Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT

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 TrueBeam^TM 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.

Correction of patient motion in cone-beam CT using 3D-2D registration

Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (~5-60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D-2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptation-evolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5-15 cm translations and 5-15° rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was  >0.995, with significant improvement (p  <  0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D-2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.

High quality 4D cone-beam CT reconstruction using motion-compensated total variation regularization

Four dimensional cone-beam computed tomography (4D-CBCT) has great potential clinical value because of its ability to describe tumor and organ motion. But the challenge in 4D-CBCT reconstruction is the limited number of projections at each phase, which result in a reconstruction full of noise and streak artifacts with the conventional analytical algorithms. To address this problem, in this paper, we propose a motion compensated total variation regularization approach which tries to fully explore the temporal coherence of the spatial structures among the 4D-CBCT phases. In this work, we additionally conduct motion estimation/motion compensation (ME/MC) on the 4D-CBCT volume by using inter-phase deformation vector fields (DVFs). The motion compensated 4D-CBCT volume is then viewed as a pseudo-static sequence, of which the regularization function was imposed on. The regularization used in this work is the 3D spatial total variation minimization combined with 1D temporal total variation minimization. We subsequently construct a cost function for a reconstruction pass, and minimize this cost function using a variable splitting algorithm. Simulation and real patient data were used to evaluate the proposed algorithm. Results show that the introduction of additional temporal correlation along the phase direction can improve the 4D-CBCT image quality.

Coping with real world data: Artifact reduction and denoising for motion-compensated cardiac C-arm CT

PURPOSE

Detailed analysis of cardiac motion would be helpful for supporting clinical workflow in the interventional suite. With an angiographic C-arm system, multiple heart phases can be reconstructed using electrocardiogram gating. However, the resulting angular undersampling is highly detrimental to the quality of the reconstructed images, especially in nonideal intraprocedural imaging conditions. Motion-compensated reconstruction has previously been shown to alleviate this problem, but it heavily relies on a preliminary reconstruction suitable for motion estimation. In this work, the authors propose a processing pipeline tailored to augment these initial images for the purpose of motion estimation and assess how it affects the final images after motion compensation.

METHODS

The following combination of simple, direct methods inspired by the core ideas of existing approaches proved beneficial: (a) Streak reduction by masking high-intensity components in projection domain after filtering. (b) Streak reduction by subtraction of estimated artifact volumes in reconstruction domain. (c) Denoising in spatial domain using a joint bilateral filter guided by an uncompensated reconstruction. (d) Denoising in temporal domain using an adaptive Gaussian smoothing based on a novel motion detection scheme.

RESULTS

Experiments on a numerical heart phantom yield a reduction of the relative root-mean-square error from 89.9% to 3.6% and an increase of correlation with the ground truth from 95.763% to 99.995% for the motion-compensated reconstruction when the authors' processing is applied to the initial images. In three clinical patient data sets, the signal-to-noise ratio measured in an ideally homogeneous region is increased by 37.7% on average. Overall visual appearance is improved notably and some anatomical features are more readily discernible.

CONCLUSIONS

The authors' findings suggest that the proposed sequence of steps provides a clear advantage over an arbitrary sequence of individual image enhancement methods and is fit to overcome the issue of lacking image quality in motion-compensated C-arm imaging of the heart. As for future work, the obtained results pave the way for investigating how accurately cardiac functional motion parameters can be determined with this modality.

Image-Based Motion Compensation for High-Resolution Extremities Cone-Beam CT

PURPOSE

Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness.

METHODS

Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT.

RESULTS

Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data.

CONCLUSION

The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.

4D respiratory motion-compensated image reconstruction of free-breathing radial MR data with very high undersampling

PURPOSE

To develop four-dimensional (4D) respiratory time-resolved MRI based on free-breathing acquisition of radial MR data with very high undersampling.

METHODS

We propose the 4D joint motion-compensated high-dimensional total variation (4D joint MoCo-HDTV) algorithm, which alternates between motion-compensated image reconstruction and artifact-robust motion estimation at multiple resolution levels. The algorithm is applied to radial MR data of the thorax and upper abdomen of 12 free-breathing subjects with acquisition times between 37 and 41 s and undersampling factors of 16.8. Resulting images are compared with compressed sensing-based 4D motion-adaptive spatio-temporal regularization (MASTeR) and 4D high-dimensional total variation (HDTV) reconstructions.

RESULTS

For all subjects, 4D joint MoCo-HDTV achieves higher similarity in terms of normalized mutual information and cross-correlation than 4D MASTeR and 4D HDTV when compared with reference 4D gated gridding reconstructions with 8.4 ± 1.1 times longer acquisition times. In a qualitative assessment of artifact level and image sharpness by two radiologists, 4D joint MoCo-HDTV reveals higher scores (P < 0.05) than 4D HDTV and 4D MASTeR at the same undersampling factor and the reference 4D gated gridding reconstructions, respectively.

CONCLUSIONS

4D joint MoCo-HDTV enables time-resolved image reconstruction of free-breathing radial MR data with undersampling factors of 16.8 while achieving low-streak artifact levels and high image sharpness. Magn Reson Med 77:1170-1183, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Cardiorespiratory motion-compensated micro-CT image reconstruction using an artifact model-based motion estimation

PURPOSE

Cardiac in vivo micro-CT imaging of small animals typically requires double gating due to long scan times and high respiratory rates. The simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. In any case, for true 5D imaging, i.e., three spatial dimensions plus one respiratory-temporal dimension plus one cardiac temporal dimension, the amount of information corresponding to a given respiratory and cardiac phase is orders of magnitude lower than the total amount of information acquired. Achieving similar image quality for 5D than for usual 3D investigations would require increasing the amount of data and thus the applied dose to the animal. Therefore, the goal is phase-correlated imaging with high image quality but without increasing the dose level.

METHODS

To achieve this, the authors propose a new image reconstruction algorithm that makes use of all available projection data, also of that corresponding to other motion windows. In particular, the authors apply a motion-compensated image reconstruction approach that sequentially compensates for respiratory and cardiac motion to decrease the impact of sparsification. In that process, all projection data are used no matter which motion phase they were acquired in. Respiratory and cardiac motion are compensated for by using motion vector fields. These motion vector fields are estimated from initial phase-correlated reconstructions based on a deformable registration approach. To decrease the sensitivity of the registration to sparse-view artifacts, an artifact model-based approach is used including a cyclic consistent nonrigid registration algorithm.

RESULTS

The preliminary results indicate that the authors' approach removes the sparse-view artifacts of conventional phase-correlated reconstructions while maintaining temporal resolution. In addition, it achieves noise levels and spatial resolution comparable to that of nongated reconstructions due to the improved dose usage. By using the proposed motion estimation, no sensitivity to streaking artifacts has been observed.

CONCLUSIONS

Using sequential double gating combined with artifact model-based motion estimation allows to accurately estimate respiratory and cardiac motion from highly undersampled data. No sensitivity to streaking artifacts introduced by sparse angular sampling has been observed for the investigated dose levels. The motion-compensated image reconstruction was able to correct for both, respiratory and cardiac motion, by applying the estimated motion vector fields. The administered dose per animal can thus be reduced for 5D imaging allowing for longitudinal studies at the highest image quality.