Dual cardiac and respiratory gated thoracic imaging via adaptive gantry velocity and projection rate modulation on a linear accelerator: A Proof‐of‐Concept Simulation Study

Abstract

Purpose Cardiac motion is typically not accounted for during pretreatment imaging for central lung and mediastinal tumors. However, cardiac induced tumor motion averages 5.8 mm for esophageal tumors and 3–5 mm for some lung tumors, which can result in positioning errors. Our aim is to reduce both cardiac‐ and respiratory‐induced motion artifacts in thoracic cone beam computed tomography (CBCT) images through gantry velocity and projection rate modulation on a standard linear accelerator (linac). Methods The acquisition of dual cardiac and respiratory gated CBCT thoracic images was simulated using the XCAT phantom with patient‐measured respiratory and ECG traces. The gantry velocity and projection rate were modulated based on the cardiac and respiratory signals to maximize the angular consistency between adjacent projections in the gated cardiac‐respiratory bin. The mechanical limitations of a gantry‐mounted CBCT system were investigated. For our protocol, images were acquired during the 60%–80% window of cardiac phase and 20% displacement either side of peak exhale of the respiratory cycle. The comparator method was the respiratory‐only gated CBCT acquisition with constant gantry speed and projection rate in routine use for respiratory correlated four‐dimensional (4D) CBCT. All images were reconstructed using the Feldkamp‐Davis‐Kress (FDK) algorithm. The methods were compared in terms of image sharpness as measured using the edge response width (ERW) and contrast as measured using the contrast to noise ratio (CNR). The effects of the total number of projections acquired and magnitude of cardiac motion on scan time and image quality were also investigated. Results Median total scan times with our protocol ranged from 117 s (40 projections) through to 296 s (100 projections), compared with 240 s for the conventional protocol (1320 projections). The scan times were dictated by the number of projections acquired, heart rate, length of the respiratory cycle and mechanical constraints of the CBCT system. Our protocol was able to provide between 8% and 43% decrease in the median value of the ERW in the anterior/posterior (AP) direction across the 17 traces when there was 0.5 cm of cardiac motion and between 35% and 64% decrease when there was 1.0 cm of cardiac motion over conventional acquisition. In the superior‐inferior (SI) direction, our protocol was able to provide between 22% and 26% decrease in the median value of the ERW across the 17 traces when there was 0.5 cm of cardiac motion and between 30% and 35% decrease when there was 1.0 cm of cardiac motion over conventional acquisition. The magnitude of the cardiac motion did not have an observable effect on the median value of the CNR. Across all 17 traces, our adaptive protocol produced noticeably more consistent, albeit lower CNR values compared with conventional acquisition. Conclusion For the first time, the potential of adapting CBCT image acquisition to changes in the patient's cardiac and respiratory rates simultaneously for applications in radiotherapy was ...

Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT)

“…These studies have shown that improvements in the image quality are observable via an increase in image sharpness (through the metric Edge Response Width) with as few as 40 projections (angular spacing of 5°). 18 Further, image sharpness was also shown to not significantly improve when more than 100 projections (angular spacing of 2°) 17 were acquired. Therefore, for the simulation study and experimental test case, we aim to acquire 100 evenly spaced projections within the desired acquisition window.…”

“…Previously, ACROBEAT has been used to simulate the real-time dynamic adaption of the image acquisition of clinical CBCT imaging systems using either a patient's cardiac signal 17 or the patient's cardiac and respiratory signals. 18 The details of the decision algorithm controlling ACROBEAT are detailed elsewhere. 17 In the present work, ACROBEAT uses the patient's cardiac signal on a robotic C-arm CBCT system.…”

“…17,18 In its current implementation, ACROBEAT uses previous cardiac cycles in a 5-s rolling window to predict future cycles. The 5 s rolling window has proven sufficient for a range of heart rates in our previous simulation studies, 17,18 including considering the effect of arrhythmic heart rates on the algorithm's performance. Of note, however, if the heart rate remains irregular for a long period of time, the scan would be aborted.…”

“…[19][20][21] Here, we select the 60-80% window for the ACROBEAT scans. 17,18 In its current implementation, ACROBEAT uses previous cardiac cycles in a 5-s rolling window to predict future cycles. The 5 s rolling window has proven sufficient for a range of heart rates in our previous simulation studies, 17,18 including considering the effect of arrhythmic heart rates on the algorithm's performance.…”

“…In simulation studies ACROBEAT has demonstrated its potential to significantly reduce the total number of projections and simultaneously improve image quality by reducing cardiac motion blur. 17,18 Here, we use ACROBEAT to adapt the image acquisition to the patient's real-time ECG signal to reduce motion blur in carotid artery imaging. We will estimate the reduction in the total number of projections acquired and improvement in the carotid artery width measurements and image quality compared to currently utilize clinical practices for carotid artery imaging.…”