Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy.

Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography

Respiratory motion management is a huge challenge in radiation therapy. Respiratory motion induces temporal anatomic changes that distort the tumor volume and its position. In this study, a markerless tumor-tracking algorithm was investigated by performing phase recognition during stereotactic body radiation therapy (SBRT) using four-dimensional cone-beam computer tomography (4D-CBCT) obtained at patient registration, and in-treatment cone-beam projection images. The data for 20 treatment sessions (five lung cancer patients) were selected for this study. Three of the patients were treated with conventional flattening filter (FF) beams, and the other two were treated with flattening filter-free (FFF) beams. Prior to treatment, 4D-CBCT was acquired to create the template projection images for 10 phases. In-treatment images were obtained at near real time during treatment. Template-based phase recognition was performed for 4D-CBCT re-projected templates using prior 4D-CBCT based phase recognition algorithm and was compared with the results generated by the Amsterdam Shroud (AS) technique. Visual verification technique was used for the verification of the phase recognition and AS technique at certain tumor-visible angles. Offline template matching analysis using the cross-correlation indicated that phase recognition performed using the prior 4D-CBCT and visual verification matched up to 97.5% in the case of FFF, and 95% in the case of FF, whereas the AS technique matched 83.5% with visual verification for FFF and 93% for FF. Markerless tumor tracking based on phase recognition using prior 4D-CBCT has been developed successfully. This is the first study that reports on the use of prior 4D-CBCT based on normalized cross-correlation technique for phase recognition.

Quantification of the kV X-ray imaging dose during real-time tumor tracking and from three- and four-dimensional cone-beam computed tomography in lung cancer patients using a Monte Carlo simulation

Knowledge of the imaging doses delivered to patients and accurate dosimetry of the radiation to organs from various imaging procedures is becoming increasingly important for clinicians. The purposes of this study were to calculate imaging doses delivered to the organs of lung cancer patients during real-time tumor tracking (RTTT) with three-dimensional (3D), and four-dimensional (4D) cone-beam computed tomography (CBCT), using Monte Carlo techniques to simulate kV X-ray dose distributions delivered using the Vero4DRT. Imaging doses from RTTT, 3D-CBCT and 4D-CBCT were calculated with the planning CT images for nine lung cancer patients who underwent stereotactic body radiotherapy (SBRT) with RTTT. With RTTT, imaging doses from correlation modeling and from monitoring of imaging during beam delivery were calculated. With CBCT, doses from 3D-CBCT and 4D-CBCT were also simulated. The doses covering 2-cc volumes (D2cc) in correlation modeling were up to 9.3 cGy for soft tissues and 48.4 cGy for bone. The values from correlation modeling and monitoring were up to 11.0 cGy for soft tissues and 59.8 cGy for bone. Imaging doses in correlation modeling were larger with RTTT. On a single 4D-CBCT, the skin and bone D2cc values were in the ranges of 7.4-10.5 cGy and 33.5-58.1 cGy, respectively. The D2cc from 4D-CBCT was approximately double that from 3D-CBCT. Clinicians should Figure that the imaging dose increases the cumulative doses to organs.