Regularized 4D-CT reconstruction from a single dataset with a spatio-temporal prior

X-ray Computerized Tomography (CT) reconstructions can be severely impaired by the patient’s respiratory motion and cardiac beating. Motion must thus be recovered in addition to the 3D reconstruction problem. The approach generally followed to reconstruct dynamic volumes consists of largely increasing the number of projections so that independent reconstructions be possible using only subsets of projections from the same phase of the cyclic movement. Apart from this major trend, motion compensation (MC) aims at recovering the object of interest and its motion by accurately modeling its deformation over time, allowing to use the whole dataset for 4D reconstruction in a coherent way.We consider a different approach for dynamic reconstruction based on inverse problems, without any additional measurements, nor explicit knowledge of the motion. The dynamic sequence is reconstructed out of a single data set, only assuming the motion’s continuity and periodicity. This inverse problem is solved by the minimization of the sum of a data-fidelity term, consistent with the dynamic nature of the data, and a regularization term which implements an efficient spatio-temporal version of the total variation (TV). We demonstrate the potential of this approach and its practical feasibility on 2D and 3D+t reconstructions of a mechanical phantom and patient data

Evaluation of Motion Artifact Metrics for Coronary CT Angiography

Purpose This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best‐phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground‐truth motion artifact scores from a series of pairwise comparisons. Method Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low‐Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine‐filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground‐truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground‐truth reader score. The Kendall\u27s Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed. Results On phantom images, the Kendall\u27s Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall\u27s Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall\u27s Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall\u27s Tau coefficient of 0.65. Conclusion The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images

An image-based method to synchronize cone-beam CT and optical surface tracking

open5siThe integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.Fassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, GuidoFassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, Guid

Surrogate driven respiratory motion model derived from CBCT projection data

Cone Beam Computed Tomography (CBCT) is the most common imaging method for Image Guided Radiation Therapy (IGRT). However due to the slow rotating gantry, the image quality of CBCT can be adversely affected by respiratory motion, as it blurs the tumour and nearby organs at risk (OARs), which makes visualization of organ boundaries difficult, in particular for organs in the thoracic region. Currently one approach to tackle the problem of respiratory motion is the use of respiratory motion model to compensate for the motion during CBCT image reconstruction. The overall goal of this work is to estimate the 3D motion, including the breath-to-breath variability, on the day of treatment directly from the CBCT projection data, without requiring any external devices. The work presented here consist of two main parts: firstly, we introduce a novel data driven method based on Principal Component Analysis PCA, with the goal to extract a surrogate signal related to the internal anatomy from the CBCT projections. Secondly, using the extracted signals, we use surrogate-driven respiratory motion models to estimate the patient’s 3D respiratory motion. We utilized a recently developed generalized framework that unifies image registration and correspondence model fitting into a single optimization. This enables the model to be fitted directly to unsorted/unreconstructed data (CBCT projection data), thereby allowing an estimate of the patient’s respiratory motion on the day of treatment. To evaluate our methods, we have used an anthropomorphic software phantom combined with CBCT projection simulations. We have also tested the proposed method on clinical data with promising results obtained