Deformable motion compensation for interventional cone-beam CT

Abstract

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Context-aware, reference-free local motion metric for CBCT deformable motion compensation

“…Previous work showed successful application of autofocus optimization for rigid motion compensation [1] using only the acquired CBCT data, with extension to complex deformable motion in abdominal CBCT [2]. Autofocus methods estimate a motion trajectory by minimizing an image-based metric that encourages properties associated to motion-free images (e.g, sharpness or piece-wise constancy).…”

Simulation of random deformable motion in soft-tissue cone-beam CT with learned models

“…Motion compensation for interventional CBCT has gained significant attention, with image-based approaches including autofocus methods based on handcrafted metrics [1][2][3], and methods leveraging deep convolutional neural networks (CNNs) to directly learn motion trajectories from distortion patterns [4], or to learn features associated to motion effects that are aggregated into deep autofocus metrics [5,6]. Common to those approaches is the need for simulation methods that allow the generation of large amounts of realistic, motion-corrupted, CBCT data to enable training and evaluation.…”

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT‐guided radiotherapy

“…The effects of patient motion on CT image quality have been investigated in many previous studies for both diagnostic FBCT [6][7][8][9] and interventional CBCT. [10][11][12][13] Active motion compensation strategies that seek to eliminate motion are also used routinely for radiotherapy planning and delivery, including deep inspiration breathe hold, 14,15 abdominal compression, 16 and gating. 17 However, these strategies are associated with other operational challenges and uncertainties and may not be consistently employed for motion below ±2.5 mm (5-mm peak-to-peak).…”

“…Differences in spatiotemporal sampling between CT scanner designs lead to important differences in image quality, particularly in the presence of patient motion, which interacts dynamically with sampling. The effects of patient motion on CT image quality have been investigated in many previous studies for both diagnostic FBCT 6–9 and interventional CBCT 10–13 . Active motion compensation strategies that seek to eliminate motion are also used routinely for radiotherapy planning and delivery, including deep inspiration breathe hold, 14,15 abdominal compression, 16 and gating 17 .…”