Applying Multi-Metric Deformable Image Registration for Dose Accumulation in Combined Cervical Cancer Radiotherapy

Abstract

(1) Purpose: Challenges remain in dose accumulation for cervical cancer radiotherapy combined with external beam radiotherapy (EBRT) and brachytherapy (BT) as there are many large and complex organ deformations between different treatments. This study aims to improve deformable image registration (DIR) accuracy with the introduction of multi-metric objectives for dose accumulation of EBRT and BT. (2) Materials and methods: Twenty cervical cancer patients treated with EBRT (45–50 Gy/25 fractions) and high-dose-rate BT (≥20 Gy in 4 fractions) were included for DIR. The multi-metric DIR algorithm included an intensity-based metric, three contour-based metrics, and a penalty term. Nonrigid B-spine transformation was used to transform the planning CT images from EBRT to the first BT, with a six-level resolution registration strategy. To evaluate its performance, the multi-metric DIR was compared with a hybrid DIR provided by commercial software. The DIR accuracy was measured by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) between deformed and reference organ contours. The accumulated maximum dose of 2 cc (D2cc) of the bladder and rectum was calculated and compared to simply addition of D2cc from EBRT and BT (ΔD2cc). (3) Results: The mean DSC of all organ contours for the multi-metric DIR were significantly higher than those for the hybrid DIR (p ≤ 0.011). In total, 70% of patients had DSC > 0.8 using the multi-metric DIR, while 15% of patients had DSC > 0.8 using the commercial hybrid DIR. The mean ΔD2cc of the bladder and rectum for the multi-metric DIR were 3.25 ± 2.29 and 3.54 ± 2.02 GyEQD2, respectively, whereas those for the hybrid DIR were 2.68 ± 2.56 and 2.32 ± 3.25 GyEQD2, respectively. The multi-metric DIR resulted in a much lower proportion of unrealistic D2cc than the hybrid DIR (2.5% vs. 17.5%). (4) Conclusions: Compared with the commercial hybrid DIR, the introduced multi-metric DIR significantly improved the registration accuracy and resulted in a more reasonable accumulated dose distribution.

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

“…The three DIR algorithms MIM‐II, MIM‐MM, MIM‐CC demonstrate an increased order of performance at organ boundaries, as measured with the metrics of DSC and MDA. For the MIM‐II algorithm, the DSC in Table 1 is 0.77 ± 0.15 for abdominal MRIs, which closely aligns with the DSC values of 0.8 ± 0.1 for lung CT images 25 and 0.76 ± 0.15 for pelvis CT images 24 . Furthermore, while DSC‐based assessments are structure‐size‐dependent and could be influenced by the type of evaluated ORAs, 20 the evaluations performed using the two metrics are shown to be consistent in this study.…”

“…For the MIM‐II algorithm, the DSC in Table 1 is 0.77 ± 0.15 for abdominal MRIs, which closely aligns with the DSC values of 0.8 ± 0.1 for lung CT images 25 and 0.76 ± 0.15 for pelvis CT images. 24 Furthermore, while DSC‐based assessments are structure‐size‐dependent and could be influenced by the type of evaluated ORAs, 20 the evaluations performed using the two metrics are shown to be consistent in this study. In interior regions, MIM‐MM shows the best performance, followed by MIM‐CC and then MIM‐II.…”

“…The MIM software version 7.06 includes three image or contour‐based DIR algorithms as documented in. 11 While these algorithms have shown success in registering images such as cervical CTs, 24 thoracic 4DCTs or CTs, 21 , 25 and head and neck MRIs, 26 their performance in registering abdominal MRIs has not been comprehensively evaluated. 27 In this study, we compared these algorithms using metrics recommended by the AAPM task group and other researchers and evaluated their performance at organ boundaries and interior regions, respectively.…”

Improving hybrid image and structure-based deformable image registration for large internal deformations

“…However, their method involves the integration of several transformations into a single vector field, which cannot guarantee one-to-one correspondence between images. Similarly, Fu et al (2023) have proposed a multi-metric solution for combined treatments, emphasizing the lack of commercially available software able to handle the complex deformations common in such contexts. We believe that our proposed approach holds significant promise in the mentioned intricate registration cases and plan to further investigate its performance in such scenarios.…”