Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation

Abstract

Purpose: This study aimed to evaluate the clinical utility of a novel iterative cone beam computed tomography (CBCT) reconstruction algorithm for prostate and head and neck (HN) cancer.

Methods and materials: A total of 10 patients with HN and 10 patients with prostate cancer were analyzed. For each patient, raw CBCT acquisition data were used to reconstruct images with a currently available algorithm (FDK_CBCT) and novel iterative algorithm (Iterative_CBCT). Quantitative contouring variation analysis was performed using structures delineated by several radiation oncologists. For prostate, observers contoured the prostate, proximal 2 cm seminal vesicles, bladder, and rectum. For HN, observers contoured the brain stem, spinal canal, right-left parotid glands, and right-left submandibular glands. Observer contours were combined to form a reference consensus contour using the simultaneous truth and performance level estimation method. All observer contours then were compared with the reference contour to calculate the Dice coefficient, Hausdorff distance, and mean contour distance (prostate contour only). Qualitative image quality analysis was performed using a 5-point scale ranging from 1 (much superior image quality for Iterative_CBCT) to 5 (much inferior image quality for Iterative_CBCT).

Results: The Iterative_CBCT data sets resulted in a prostate contour Dice coefficient improvement of approximately 2.4% (P = .029). The average prostate contour Dice coefficient for the Iterative_CBCT data sets was improved for all patients, with improvements up to approximately 10% for 1 patient. The mean contour distance results indicate an approximate 15% reduction in mean contouring error for all prostate regions. For the parotid contours, Iterative_CBCT data sets resulted in a Hausdorff distance improvement of approximately 2 mm (P < .01) and an approximate 2% improvement in Dice coefficient (P = .03). The Iterative_CBCT data sets were scored as equivalent or of better image quality for 97.3% (prostate) and 90.0% (HN) of the patient data sets.

Conclusions: Observers noted an improvement in image uniformity, noise level, and overall image quality for Iterative_CBCT data sets. In addition, expert observers displayed an improved ability to consistently delineate soft tissue structures, such as the prostate and parotid glands. Thus, the novel iterative reconstruction algorithm analyzed in this study is capable of improving the visualization for prostate and HN cancer image guided radiation therapy.

Fig. 1

Schematic of the framework to calculate mean contour distance. (Left) Axial view. The contour is divided into 3 regions: Anterior, posterior, and lateral. The regions are defined by 2 orthogonal lines with intersection at the center of mass, and oriented 45° relative to the sagittal and coronal planes. (Right) Sagittal view. The superior and inferior regions of the prostate are defined as the superior-most and inferior-most 6 mm regions of the prostate. Figure used with permission.

Fig. 2

Visual contouring analysis for patient 5 of the prostate study, representing the largest Dice coefficient improvement for prostate contours. Prostate observer contours are shown in red and consensus contour in blue. Overall, the patient appeared to exhibit less inherent soft-tissue contrast than other patients within the study data set. (A) Axial and (B) sagittal views of the currently available algorithm (FDK_CBCT) reconstruction, respectively. Note the variation in the prostate-rectal interface on the axial (red arrow) and sagittal (yellow arrow) views of the contouring; (C) axial and (D) sagittal views of the novel iterative algorithm (Iterative_CBCT) reconstruction, respectively. Note the decreased noise and improved uniformity of the Iterative_CBCT image set relative to FDK_CBCT in both views. Also note the improvement in delineation of the prostate-rectal interface in both views relative to the FDK_CBCT image.

Fig. 3

Comparison of image quality for prostate patient 4. Top images (A) and (B) currently available algorithm (FDK_CBCT). Middle images (C) and (D) novel iterative algorithm (Iterative_CBCT). Bottom images (E) and (F): Planning computed tomography (acquired on different day than the cone beam computed tomography [CBCT] data sets). Note the improvement in image intensity homogeneity in the peripheral portion of the axial field of view (FOV; red arrow), central portion of the axial FOV (yellow arrow), and central portion of the sagittal FOV (orange arrow) in the Iterative_CBCT image. Also note the improved sharpness and image intensity uniformity near bony anatomy (green arrow) and the improved overall image noise for the Iterative_CBCT data set.

Fig. 4

Comparison of image quality for patient 7 with head and neck cancer. Top images (A) and (B): currently available algorithm (FDK_CBCT). Middle images (C) and (D) novel iterative algorithm (Iterative_CBCT). Bottom images (E) and (F) Planning computed tomography (acquired on different day than the cone beam computed tomography data sets). Note the improvement in image intensity homogeneity in the peripheral portion of the axial field of view near the left parotid gland (yellow arrow) and inferior portion of the sagittal field of view (orange and red arrows). Note the lack of streaking artifact in the Iterative_CBCT image near the bony anatomy (green arrow). Also note the improved overall image noise for the Iterative_CBCT data set.