Structure guided deformable image registration for treatment planning CT and post stereotactic body radiation therapy (SBRT) Primovist^® (Gd‐EOB‐DTPA) enhanced MRI

Abstract

The purpose of this study was to assess the performance of structure‐guided deformable image registration (SG‐DIR) relative to rigid registration and DIR using TG‐132 recommendations. This assessment was performed for image registration of treatment planning computed tomography (CT) and magnetic resonance imaging (MRI) scans with Primovist® contrast agent acquired post stereotactic body radiation therapy (SBRT). SBRT treatment planning CT scans and posttreatment Primovist® MRI scans were obtained for 14 patients. The liver was delineated on both sets of images and matching anatomical landmarks were chosen by a radiation oncologist. Rigid registration, DIR, and two types of SG‐DIR (using liver contours only; and using liver structures along with anatomical landmarks) were performed for each set of scans. TG‐132 recommended metrics were estimated which included Dice Similarity Coefficient (DSC), Mean Distance to Agreement (MDA), Target Registration Error (TRE), and Jacobian determinant. Statistical analysis was performed using Wilcoxon Signed Rank test. The median (range) DSC for rigid registration was 0.88 (0.77–0.89), 0.89 (0.81–0.93) for DIR, and 0.90 (0.86–0.94) for both types of SG‐DIR tested in this study. The median MDA was 4.8 mm (3.7–6.8 mm) for rigid registration, 3.4 mm (2.4–8.7 mm) for DIR, 3.2 mm (2.0–5.2 mm) for SG‐DIR where liver structures were used to guide the registration, and 2.8 mm (2.1–4.2 mm) for the SG‐DIR where liver structures and anatomical landmarks were used to guide the registration. The median TRE for rigid registration was 7.2 mm (0.5–23 mm), 6.8 mm (0.7–30.7 mm) for DIR, 6.1 mm (1.1–20.5 mm) for the SG‐DIR guided by only the liver structures, and 4.1 mm (0.8–19.7 mm) for SG‐DIR guided by liver contours and anatomical landmarks. The SG‐DIR shows higher liver conformality as per TG‐132 metrics and lowest TRE compared to rigid registration and DIR in Velocity AI software for the purpose of registering treatment planning CT and post‐SBRT MRI for the liver region. It was found that TRE decreases when liver contours and corresponding anatomical landmarks guide SG‐DIR.

Direct visualization and correlation of liver stereotactic body radiation therapy treatment delivery accuracy with interfractional motion

“…Deformable image registration (DIR) in Velocity AI (Varian Medical Systems, Inc, Palo Alto, CA, USA) was used to register the two image sets. The details regarding the image registration implementation and performance assessment have been previously described 23,24 . Following image registration, in‐house software was used for localization of the hypo‐intensity.…”

“…The details regarding the image registration implementation and performance assessment have been previously described. 23 , 24 Following image registration, in‐house software was used for localization of the hypo‐intensity. The custom program was developed in MatLab R2016a.…”

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

“…Results showed a mean registration accuracy of 10.9 mm ± 4.2 mm (manual) vs 13.9 mm ± 4.4 mm (semi-automatic), hence significantly improving the manual registration. Kuznetsova et al [ 48 ] assessed the performance of structure-guided deformable image registration (SG-DIR) relative to rigid registration and DIR using TG-132 recommendations for 14 patients with liver tumors to whom stereotactic body radiation therapy (SBRT) was applied. The median DSC for rigid registration was 88% and 89% for DIR, and 90% for both SG-DIR using liver contours only and using liver structures along with anatomical landmarks.…”

Phantom-based Quality Assurance of a Clinical Dose Accumulation Technique Used in an Online Adaptive Radiation Therapy Platform

“… 9 , 10 , 11 , 12 , 13 , 14 , 15 Certain metrics directly compare reference and warped volumes, 16 , 17 , 18 while others measure registration accuracy for landmarks or contours. 13 , 19 , 20 , 21 , 22 , 23 Although useful for image-based DIR applications (eg, auto-segmentation), these metrics do not directly provide insight on the fidelity of an accumulated dose volume. Most efforts to quantifiably assess DIR in application to dose accumulation have relied on custom deformable phantoms with integrated, yet complicated, dosimeter arrangements.…”