Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system

Ablative margin quantification using deformable versus rigid image registration in colorectal liver metastasis thermal ablation: a retrospective single-center study

PURPOSE

To investigate the correlation of minimal ablative margin (MAM) quantification using biomechanical deformable (DIR) versus intensity-based rigid image registration (RIR) with local outcomes following colorectal liver metastasis (CLM) thermal ablation.

METHODS

This retrospective single-institution study included consecutive patients undergoing thermal ablation between May 2016 and October 2021. Patients who did not have intraprocedural pre- and post-ablation contrast-enhanced CT images for MAM quantification or follow-up period less than 1 year without residual tumor or local tumor progression (LTP) were excluded. DIR and RIR methods were used to quantify the MAM. The registration accuracy was compared using Dice similarity coefficient (DSC). Area under the receiver operating characteristic curve (AUC) was used to test MAM in predicting local tumor outcomes.

RESULTS

A total of 72 patients (mean age 57; 44 men) with 139 tumors (mean diameter 1.5 cm ± 0.8 (SD)) were included. During a median follow-up of 29.4 months, there was one residual unablated tumor and the LTP rate was 17% (24/138). The ranges of DSC were 0.96-0.98 and 0.67-0.98 for DIR and RIR, respectively (p < 0.001). When using DIR, 27 (19%) tumors were partially or totally registered outside the liver, compared to 46 (33%) with RIR. Using DIR versus RIR, the corresponding median MAM was 4.7 mm versus 4.0 mm, respectively (p = 0.5). The AUC in predicting residual tumor and 1-year LTP for DIR versus RIR was 0.89 versus 0.72, respectively (p < 0.001).

CONCLUSION

Ablative margin quantified on intra-procedural CT imaging using DIR method outperformed RIR for predicting local outcomes of CLM thermal ablation.

CLINICAL RELEVANCE STATEMENT

The study supports the role of biomechanical deformable image registration as the preferred image registration method over rigid image registration for quantifying minimal ablative margins using intraprocedural contrast-enhanced CT images.

KEY POINTS

• Accurate and reproducible image registration is a prerequisite for clinical application of image-based ablation confirmation methods. • When compared to intensity-based rigid image registration, biomechanical deformable image registration for minimal ablative margin quantification was more accurate for liver registration using intraprocedural contrast-enhanced CT images. • Biomechanical deformable image registration outperformed intensity-based rigid image registration for predicting local tumor outcomes following colorectal liver metastasis thermal ablation.

Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients

Objective.To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system.Approach.DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline's input included the adapt-to-shape (ATS) plan-i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution-and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients).Main results.The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum.Significance.The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.

Evaluation of the availability of single-position treatment with a rotating gantry and the validity of deformable image registration dose assessment for pancreatic cancer carbon-ion radiotherapy

BACKGROUND

This study aimed to evaluate the clinical acceptability of rotational gantry-based single-position carbon-ion radiotherapy (CIRT) to reduce the gastrointestinal (GI) dose in pancreatic cancer. We also evaluated the usefulness of the deformable image registration (DIR)-based dosimetry method for CIRT.

MATERIAL AND METHODS

Fifteen patients with pancreatic cancer were analyzed. The treatment plans were developed for four beam angles in the supine (SP plan) and prone (PR plan) positions. In the case of using multiple positions, the treatment plan was created with two angles for each of the supine and prone position (SP + PR plan). Dose evaluation for multiple positions was performed in two ways: by directly adding the values of the DVH parameters for each position treatment plan (DVH sum), and by calculating the DVH parameters from the accumulative dose distribution created using DIR (DIR sum). The D2cc and D6cc of the stomach and duodenum were recorded for each treatment plan and dosimetry method and compared.

RESULTS

There were no significant differences among any of the treatment planning and dosimetry methods (p > 0.05). The DVH parameters for the stomach and duodenum were higher in the PR plan and SP plan, respectively, and DVH sum tended to be between the SP and PR plans. DVH sum and DIR sum, DVH sum tended to be higher for D2cc and DIR sum tended to be higher for D6cc.

CONCLUSION

There were no significant differences in the GI dose, which suggests that treatment with a simple workflow performed in one position should be clinically acceptable. In CIRT, DIR-based dosimetry should be carefully considered because of the potential for increased uncertainty due to the steep dose distributions.

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

PURPOSE

We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties.

MATERIALS AND METHODS

Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications.

RESULTS

For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC.

CONCLUSIONS

Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

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

Objective.Deformable image registration (DIR) is a widely used technique in radiotherapy. Complex deformations, resulting from large anatomical changes, are a regular challenge. DIR algorithms generally seek a balance between capturing large deformations and preserving a smooth deformation vector field (DVF). We propose a novel structure-based term that can enhance the registration efficacy while ensuring a smooth DVF.Approach.The proposed novel similarity metric for controlling structures was introduced as a new term into a commercially available algorithm. Its performance was compared to the original algorithm using a dataset of 46 patients who received pelvic re-irradiation, many of which exhibited complex deformations.Main results.The mean Dice Similarity Coefficient (DSC) under the improved algorithm was 0.96, 0.94, 0.76, and 0.91 for bladder, rectum, colon, and bone respectively, compared to 0.69, 0.89, 0.62, and 0.88 for the original algorithm. The improvement was more pronounced for complex deformations.Significance.With this work, we have demonstrated that the proposed term is able to improve registration accuracy for complex cases while maintaining realistic deformations.

Deformable image registration for composite planned doses during adaptive radiation therapy

INTRODUCTION

Some patients have significant anatomic changes during radiotherapy, necessitating an adaptive repeat CT-simulation and re-planning. This yields two unique planning datasets that introduce uncertainty into total dose records. This study explored the impact of using deformable image registration (DIR) to spatially align repeat CT-simulation images and calculate total planned dose distributions.

MATERIALS & METHODS

Data from 5 head-and-neck, 5 lung, and 5 sarcoma patients who had unanticipated re-planning during radiotherapy were analyzed in a treatment planning system (RayStation v6.1 RaySearch Laboratories). Total planned doses to normal tissues were calculated using two methods and the previously generated manual contours defined on each CT. The first method, termed 'parameter addition', simply sums the relevant DVH metrics from the initial and re-planned distributions without spatially registering the CTs. The second, termed 'dose accumulation', uses a validated hybrid contour/intensity-based DIR algorithm to deform initial CT and dose distribution onto the repeat CT and re-planning dose distribution. DVH metrics from the summed distribution on the repeat CT are then calculated. Dose differences for organs-at-risk between parameter addition and dose accumulation ≥100 cGy were assumed to be clinically relevant. To elucidate whether relevant differences were due to registration accuracy or contouring variability between CTs, the analysis was repeated using contours on the first CT and the same contours deformed to the repeat CT with DIR.

RESULTS

For all patients, high overall DIR accuracy was verified visually (qualitatively) and numerically (quantitatively) using image similarity and contour-based metrics. All head-and-neck and lung patients, and one sarcoma patient (11 of 15 total) had dose differences between parameter addition and dose accumulation ≥100 cGy, with absolute mean differences of 160 cGy (range 101-436 cGy) seen in 41 of 205 total DVH criteria. In 22 of these 41 criteria, these differences were attributed to contouring variability between CTs. After correcting for contouring variations using DIR, the mean absolute differences in 7 of these 22 criteria with a relevant result (across 6 patients) was 146 cGy (range 100-502 cGy). In only 4 DVH criteria, the DIR mapped contours had higher variations than the original contours. One lung patient had a DVH criteria exceeding the clinical dose constraint by 125 cGy with parameter addition, and with accurate DIR and dose accumulation, the criteria was actually 97 cGy lower than the constraint.

CONCLUSIONS

The use of DIR to generate total planned dose records revealed substantial dose differences in most cases compared to commonly used clinical methods (i.e. parameter addition), and altered the planned acceptance criteria in a minority. DIR is recommended to be used for future adaptive re-plans to generate total planned dose records and facilitate accurate re-contouring. More accurate dose records may also improve our understanding of clinical outcomes.

Incidental Ring-hyperenhancing Liver Micronodules at CT Hepatic Arteriography-guided Percutaneous Thermal Ablation of Colorectal Liver Metastases

CT during hepatic arteriography (CTHA) is a highly sensitive imaging method for detecting colorectal liver metastases (CLMs), which supports its use during percutaneous thermal liver ablation. In contrast to its high sensitivity, its specificity for incidental small CLMs not detected at preablation cross-sectional imaging is believed to be low given the absence of specific imaging signatures and the common presence of pseudolesions. In this retrospective study of 22 patients (mean age, 55 years ± 10.6 [SD]; 63.6% male, 36.4% female) with CLMs undergoing CTHA-guided microwave percutaneous thermal ablation between November 2017 and October 2022, the authors provided a definition of incidental ring-hyperenhancing liver micronodules (RHLMs) and investigated whether there is a correlation of RHLMs with histologic analysis or intrahepatic tumor progression at imaging follow-up after applying a biomechanical deformable image registration method. The analysis revealed 25 incidental RHLMs in 41.7% (10 of 24) of the CTHA images from the respective guided ablation sessions. Of those, four RHLMs were ablated. Among the remaining 21 RHLMs, 71.4% (15 of 21) were confirmed to be CLM with either histology (n = 3) or imaging follow-up (n = 12). The remaining 28.6% (six of 21) of RHLMs were not observed at follow-up imaging. This suggests that RHLMs at CTHA may be an early indicator of incidental small CLMs. Keywords: Colorectal Neoplasms, Liver, Angiography, CT, Incidental Findings, Ablation Supplemental material is available for this article. © RSNA, 2024.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT

BACKGROUND

Adaptive radiation treatment (ART) for locally advanced pancreatic cancer (LAPC) requires consistently accurate segmentation of the extremely mobile gastrointestinal (GI) organs at risk (OAR) including the stomach, duodenum, large and small bowel. Also, due to lack of sufficiently accurate and fast deformable image registration (DIR), accumulated dose to the GI OARs is currently only approximated, further limiting the ability to more precisely adapt treatments.

PURPOSE

Develop a 3-D Progressively refined joint Registration-Segmentation (ProRSeg) deep network to deformably align and segment treatment fraction magnetic resonance images (MRI)s, then evaluate segmentation accuracy, registration consistency, and feasibility for OAR dose accumulation.

METHOD

ProRSeg was trained using five-fold cross-validation with 110 T2-weighted MRI acquired at five treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Statistical comparison to other deep learning and iterative registration methods were done using the Kruskal-Wallis test, followed by pair-wise comparisons with Bonferroni correction applied for multiple testing. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans was evaluated on a publicly available dataset of 80 scans using five-fold cross-validation.

RESULTS

ProRSeg processed 3D volumes (128 × 192 × 128) in 3 s on a NVIDIA Tesla V100 GPU. It's segmentations were significantly more accurate (p < 0.001 $p<0.001$ ) than compared methods, achieving a DSC of 0.94 ±0.02 for liver, 0.88±0.04 for large bowel, 0.78±0.03 for small bowel and 0.82±0.04 for stomach-duodenum from MRI. ProRSeg achieved a DSC of 0.72±0.01 for small bowel and 0.76±0.03 for stomach-duodenum from public CBCT dataset. ProRSeg registrations resulted in the lowest CV in displacement (stomach-duodenumC V x $CV_{x}$ : 0.75%,C V y $CV_{y}$ : 0.73%, andC V z $CV_{z}$ : 0.81%; small bowelC V x $CV_{x}$ : 0.80%,C V y $CV_{y}$ : 0.80%, andC V z $CV_{z}$ : 0.68%; large bowelC V x $CV_{x}$ : 0.71%,C V y $CV_{y}$ : 0.81%, andC V z $CV_{z}$ : 0.75%). ProRSeg based dose accumulation accounting for intra-fraction (pre-treatment to post-treatment MRI scan) and inter-fraction motion showed that the organ dose constraints were violated in four patients for stomach-duodenum and for three patients for small bowel. Study limitations include lack of independent testing and ground truth phantom datasets to measure dose accumulation accuracy.

CONCLUSIONS

ProRSeg produced more accurate and consistent GI OARs segmentation and DIR of MRI and CBCTs compared to multiple methods. Preliminary results indicates feasibility for OAR dose accumulation using ProRSeg.

Experimental validation of a novel method of dose accumulation for the rectum

BACKGROUND

Dose-surface maps (DSMs) are an increasingly popular tool to evaluate spatial dose-outcome relationships for the rectum. Recently, DSM addition has been proposed as an alternative method of dose accumulation from deformable registration-based techniques. In this study, we performed the first experimental investigation of the accuracy at which DSM accumulation can capture the total dose delivered to a rectum's surface in the presence of inter-fraction motion.

MATERIAL AND METHODS

A custom PVC rectum phantom capable of representing typical rectum inter-fraction motion and filling variations was constructed for this project. The phantom allowed for the placement of EBT3 film sheets on the representative rectum surface to measure rectum surface dose. A multi-fraction prostate VMAT treatment was designed and delivered to the phantom in a water tank for a variety of inter-fraction motion scenarios. DSMs for each fraction were calculated in two ways using CBCT images acquired during delivery and summed to produce accumulated DSMs. Accumulated DSMs were then compared to film measurements using gamma analysis (3%/2 mm criteria). Similarity of isodose clusters between films and DSMs was also investigated.

RESULTS

Baseline agreement between film measurements and accumulated DSMs for a stationary rectum was 95.6%. Agreement between film and accumulated DSMs in the presence of different types of inter.-fraction motion was ≥92%, and isodose cluster mean distance to agreement was within 1.5 mm for most scenarios. Overall, DSM accumulation performed the best when using DSMs that accounted for changes in rectum path orientation.

CONCLUSION

Dose accumulation performed with DSMs was found to accurately replicate total delivered dose to a rectum phantom in the presence of inter-fraction motion.

Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN

PURPOSES

To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring.

METHODS

With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts' contours evaluated the image synthesis quality.

RESULTS

The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91.

CONCLUSION

We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation

Background Confirming ablation completeness with sufficient ablative margin is critical for local tumor control following colorectal liver metastasis (CLM) ablation. An image-based confirmation method considering patient- and ablation-related biomechanical deformation is an unmet need. Purpose To evaluate a biomechanical deformable image registration (DIR) method for three-dimensional (3D) minimal ablative margin (MAM) quantification and the association with local disease progression following CT-guided CLM ablation. Materials and Methods This single-institution retrospective study included patients with CLM treated with CT-guided microwave or radiofrequency ablation from October 2015 to March 2020. A biomechanical DIR method with AI-based autosegmentation of liver, tumors, and ablation zones on CT images was applied for MAM quantification retrospectively. The per-tumor incidence of local disease progression was defined as residual tumor or local tumor progression. Factors associated with local disease progression were evaluated using the multivariable Fine-Gray subdistribution hazard model. Local disease progression sites were spatially localized with the tissue at risk for tumor progression (<5 mm) using a 3D ray-tracing method. Results Overall, 213 ablated CLMs (mean diameter, 1.4 cm) in 124 consecutive patients (mean age, 57 years ± 12 [SD]; 69 women) were evaluated, with a median follow-up interval of 25.8 months. In ablated CLMs, an MAM of 0 mm was depicted in 14.6% (31 of 213), from greater than 0 to less than 5 mm in 40.4% (86 of 213), and greater than or equal to 5 mm in 45.1% (96 of 213). The 2-year cumulative incidence of local disease progression was 72% for 0 mm and 12% for greater than 0 to less than 5 mm. No local disease progression was observed for an MAM greater than or equal to 5 mm. Among 117 tumors with an MAM less than 5 mm, 36 had local disease progression and 30 were spatially localized within the tissue at risk for tumor progression. On multivariable analysis, an MAM of 0 mm (subdistribution hazard ratio, 23.3; 95% CI: 10.8, 50.5; P < .001) was independently associated with local disease progression. Conclusion Biomechanical deformable image registration and autosegmentation on CT images enabled identification and spatial localization of colorectal liver metastases at risk for local disease progression following ablation, with a minimal ablative margin greater than or equal to 5 mm as the optimal end point. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sofocleous in this issue.

Liver SBRT dose accumulation to assess the impact of anatomic variations on normal tissue doses and toxicity in patients treated with concurrent sorafenib

BACKGROUND AND PURPOSE

Unexpected liver volume reductions occurred during trials of liver SBRT and concurrent sorafenib. The aims were to accumulate liver SBRT doses to assess the impact of these anatomic variations on normal tissue dose parameters and toxicity.

MATERIALS AND METHODS

Thirty-two patients with hepatocellular carcinoma (HCC) or metastases treated on trials of liver SBRT (30-57 Gy, 6 fractions) and concurrent sorafenib were analyzed. SBRT doses were accumulated using biomechanical deformable registration of daily cone-beam CT. Dose deviations (accumulated-planned) for normal tissues were compared for patients with liver volume reductions > 100 cc versus stable volumes, and accumulated doses were reported for three patients with grade 3-5 luminal gastrointestinal toxicities.

RESULTS

Patients with reduced (N = 12) liver volumes had larger mean deviations of 0.4-1.3 Gy in normal tissues, versus -0.2-0.4 Gy for stable cases (N = 20), P > 0.05. Deviations > 5% of the prescribed dose occurred in both groups. Two HCC patients with toxicities to small and large bowel had liver volume reductions and deviations to the maximum dose of 4% (accumulated 36.9 Gy) and 3% (accumulated 33.4 Gy) to these organs respectively. Another HCC patient with a toxicity of unknown location plus tumor rupture, had stable liver volumes and deviations to luminal organs of -6% to 4.5% (accumulated < 30.5 Gy).

CONCLUSION

Liver volume reductions during SBRT and concurrent sorafenib were associated with larger increases in accumulated dose to normal tissues versus stable liver volumes. These dosimetric changes may have further contributed to toxicities in HCC patients who have higher baseline risks.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration

BACKGROUND

Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow.

PURPOSE

To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes.

METHODS

Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis.

RESULTS

Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively.

CONCLUSION

The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures

PURPOSE

Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms.

MATERIALS AND METHODS

Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses.

RESULTS

Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach.

CONCLUSION

This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

PURPOSE

The performance of deformable medical image registration (DIR) algorithms has become a major concern.

METHODS

We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines.

RESULTS

One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed.

CONCLUSIONS

This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.

Accuracy and consistency of intensity-based deformable image registration in 4DCT for tumor motion estimation in liver radiotherapy planning

We investigate the accuracy of intensity-based deformable image registration (DIR) for tumor localization in liver stereotactic body radiotherapy (SBRT). We included 4DCT scans to capture the breathing motion of eight patients receiving SBRT for liver metastases within a retrospective clinical study. Each patient had three fiducial markers implanted. The liver and the tumor were delineated in the mid-ventilation phase, and their positions in the other phases were estimated with deformable image registration. We tested referenced and sequential registrations strategies. The fiducial markers were the gold standard to evaluate registration accuracy. The registration errors related to measured versus estimated fiducial markers showed a mean value less than 1.6mm. The positions of some fiducial markers appeared not stable on the 4DCT throughout the respiratory phases. Markers’ center of mass tends to be a more reliable measurement. Distance errors of tumor location based on registration versus markers center of mass were less than 2mm. There were no statistically significant differences between the reference and the sequential registration, i.e., consistency and errors were comparable to resolution errors. We demonstrated that intensity-based DIR is accurate up to resolution level for locating the tumor in the liver during breathing motion.

Interfraction dose deviation and catheter position in cervical interstitial and intracavitary image guided HDR brachytherapy

Interstitial and intracavitary gynecological HDR brachytherapy involve precise, localized delivery to targets with high dose gradients, sparing adjacent organs at risk (OAR). Due to the proximity of the rectum, bowel and bladder to the target, deviations in the applicator or catheter with respect to patient anatomy can significantly increase dose to OAR. The magnitude and direction of applicator and catheter migration at each fraction was assessed for template interstitial and tandem and ring (T&R) cohorts. The cohort included twelve gynecological patients with intact cervical lesions treated with external beam and brachytherapy. Pre-treatment CT images were registered to the simulation CT with respect to the target. Treatment catheter positions transformed into the planning CT coordinate system to evaluate localized catheter displacement and dose distributions calculated at each fraction. Dose was evaluated on the planning CT with planning contours and dwell locations at treatment position. Absolute deviation, depth and deflection angle for all patients were 4.6 ± 4.2 mm, -1.4 ± 4.0 mm, and 3.1 ± 2.3° respectively (n = 516 catheter positions for all treatment fractions and patients, mean ± SD). Absolute catheter deviation and deflection magnitude for interstitial treatments increased overall with each subsequent fraction with an overall increase of catheter retraction at each fraction (p < 0.005, n = 492 catheters, Kruskal-Wallis). A target EQD2 D90 reduction of 10 ± 10% and 7.7 ± 8.7% of the planned dose for interstitial and T&R cohorts respectively. There was an overall increase in bladder and rectal doses at each fraction. Catheter tracking in interstitial and intracavitary gynecological treatments with CT imaging revealed significant changes in catheter positioning with respect to the target volume. Overall deviations increased in magnitude with each subsequent fraction in the interstitial treatments. This caused patient dosimetry deviations, including target dose reduction and adjacent OAR doses changes.

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio)

PURPOSE

Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board tumor localizations. Biomechanical modeling has also been introduced to fine-tune the intra-liver deformation-vector-fields (DVFs) solved by 2D-3D deformable registration, especially at low-contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone-beam projections, which degrades the accuracy of the intensity-based 2D-3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep-learning (DL)-based method to optimize the liver boundary DVFs after 2D-3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization.

METHODS

The DL-based network was built based on the U-Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D-3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient-specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra-liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D-3D deformable registration, 2D-3D-Bio (2D-3D deformable registration with biomechanical modeling), and DL-Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center-of-mass-errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented "gold-standard" contours.

RESULTS

The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D-3D, 2D-3D-Bio, and DL-Bio techniques were 4.7 ± 1.9 mm, 2.9 ± 1.0 mm, and 1.7 ± 0.4 mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6 mm, 5.4 ± 1.5 mm, and 4.5 ± 1.3 mm, respectively.

CONCLUSION

DL-Bio solves a general correlation model to improve the accuracy of the DVFs at the liver boundary. With improved boundary conditions, the accuracy of biomechanical modeling can be further increased for accurate intra-liver low-contrast tumor localization.

Robust Treatment Planning in Intrafraction Motion Using TomoDirect™ Intensity-modulated Radiotherapy for Breast Cancer

BACKGROUND/AIM

To evaluate the robustness of radiotherapy treatment planning optimization for respiratory-moving breast cancer using fixed-angle beams planning TomoDirect™ intensity-modulated radiotherapy (IMRT).

MATERIALS AND METHODS

A minimax optimisation algorithm was applied to 10 breast cancer patients. Two sets of treatment plans with or without robust techniques were prepared considering anterior-posterior and head-tail movements due to respiration. Parameters were compared between treatment plans: 95% planned target volume (PTV) dose, conformal index and homogeneity index (HI), and organs at risk (OAR) parameters including the lung volume receiving 20 Gy or more (V20) and 5 Gy (V5).

RESULTS

Robust planning significantly improved parameters of 95% PTV dose and HI, without deteriorating V20 or V5 in the anterior-posterior movement, while it slightly improved 95% PTV and slightly deteriorated V20 in the head-tail movement.

CONCLUSION

Robust treatment planning improves coverage of targets moving because of respiration in the treatment of breast cancer using TomoDirect; however, normal lung doses should be cautiously evaluated on a case-by-case basis.

A novel use of biomechanical model-based deformable image registration (DIR) for assessing colorectal liver metastases ablation outcomes

Purpose:

Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%–51% overall 5-year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model-based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP-free patients.

Methods:

We retrospectively acquired 30 patients (16 LTP and 14 LTP-free) at our institution who had undergone PTA and had a contrast-enhanced pre-treatment and post-ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model-based DIR between the pre-treatment and post-ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in-house cone-tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved.

Results:

Mann–Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP-free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume.

Conclusions:

Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe.

Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation

PURPOSE

The registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning.

METHODS AND MATERIALS

The American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology.

RESULTS AND CONCLUSIONS

The NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.

High-dose hypofractionated pencil beam scanning carbon ion radiotherapy for lung tumors: Dosimetric impact of different spot sizes and robustness to interfractional uncertainties

PURPOSE

The robustness against setup and motion uncertainties of gated four-dimensional restricted robust optimization (4DRRO) was investigated for hypofractionated carbon ion radiotherapy (CIRT) of lung tumors.

METHODS

CIRT plans of 9 patients were optimized using 4DRRO strategy with 3 mm setup errors, 3% density errors and 3 breathing phases related to the gate window. The prescription was 60 Gy(RBE) in 4 fractions. Standard spots (SS) were compared to big spots (BS). Plans were recalculated on multiple 4DCTs acquired within 3 weeks from treatment simulation and rigidly registered with planning images using bone matching. Warped dose distributions were generated using deformable image registration and accumulated on the planning 4DCTs. Target coverage (D98%, D95% and V95%) and dose to lung were evaluated in the recalculated and accumulated dose distributions.

RESULTS

Comparable target coverage was obtained with both spot sizes (p = 0.53 for D95%). The mean lung dose increased of 0.6 Gy(RBE) with BS (p = 0.0078), still respecting the dose constraint of a 4-fraction stereotactic treatment for the risk of radiation pneumonitis. Statistically significant differences were found in the recalculated and accumulated D95% (p = 0.048 and p = 0.024), with BS showing to be more robust. Using BS, the average degradations of the D98%, D95% and V95% in the accumulated doses were -2.7%, -1.6% and -1.5%.

CONCLUSIONS

Gated 4DRRO was highly robust against setup and motion uncertainties. BS increased the dose to healthy tissues but were more robust than SS. The selected optimization settings guaranteed adequate target coverage during the simulated treatment course with acceptable risk of toxicity.

Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

PURPOSE

A major burden of introducing an online daily adaptive proton therapy (DAPT) workflow is the time and resources needed to correct the daily propagated contours. In this study, we evaluated the dosimetric impact of neglecting the online correction of the propagated contours in a DAPT workflow.

MATERIAL AND METHODS

For five NSCLC patients with nine repeated deep-inspiration breath-hold CTs, proton therapy plans were optimised on the planning CT to deliver 60 Gy-RBE in 30 fractions. All repeated CTs were registered with six different clinically used deformable image registration (DIR) algorithms to the corresponding planning CT. Structures were propagated rigidly and with each DIR algorithm and reference structures were contoured on each repeated CT. DAPT plans were optimised with the uncorrected, propagated structures (propagated DAPT doses) and on the reference structures (ideal DAPT doses), non-adapted doses were recalculated on all repeated CTs.

RESULTS

Due to anatomical changes occurring during the therapy, the clinical target volume (CTV) coverage of the non-adapted doses reduces on average by 9.7% (V95) compared to an ideal DAPT doses. For the propagated DAPT doses, the CTV coverage was always restored (average differences in the CTV V95 < 1% compared to the ideal DAPT doses). Hotspots were always reduced with any DAPT approach.

CONCLUSION

For the patients presented here, a benefit of online DAPT was shown, even if the daily optimisation is based on propagated structures with some residual uncertainties. However, a careful (offline) structure review is necessary and corrections can be included in an offline adaption.

Simulated daily plan adaptation for magnetic resonance-guided liver stereotactic body radiotherapy

INTRODUCTION

Liver cancers are challenging to treat using image-guided radiotherapy (IGRT) due to motion and deformation of target volumes and organs at risk (OARs), as well as difficulties in visualising liver tumours using cone-beam computed tomography (CBCT) based IGRT. Liver cancer patients may thus benefit from magnetic resonance (MR)-guided daily adaptive re-planning. We evaluated the dosimetric impact of a daily plan adaptation strategy based on daily MR imaging versus CBCT-based IGRT.

METHODS

Ten patients were studied who were treated with CBCT-guided five-fraction stereotactic body radiotherapy (SBRT) and underwent MR imaging before each fraction. Simulated reference plans were created on computer tomography (CT) images and adapted plans were created on the daily MR images. Two plan adaptation strategies were retrospectively simulated: (1) translational couch shifts to match liver, mimicking standard CBCT guidance and (2) daily plan adaptation based on reference plan clinical goals and daily target and OAR contours. Dose statistics were calculated for both strategies and compared.

RESULTS

Couch shifts resulted in an average reduction in GTV D99% relative to reference plan values of 5.2 Gy (-12.5% of reference values). Daily plan adaptation reduced this to 0.8 Gy (-2.0%). For six patients who were OAR dose-limited on reference plans, couch shifts resulted in OAR dose violations in 28 out of 28 simulated fractions, respectively; no violations occurred using daily plan adaptation. No OAR dose violations occurred using either strategy for the four cases not OAR dose-limited at reference planning.

CONCLUSIONS

MR-guided daily plan adaptation ensured OAR dose constraints were met at all simulated treatment fractions while CBCT-based IGRT resulted in a systematic over-dosing of OARs in patients whose doses were limited by OAR dose at the time of reference planning.

MRI evaluation of normal tissue deformation and breathing motion under an abdominal compression device

Purpose

Abdominal compression can minimize breathing motion in stereotactic radiotherapy, though it may impact the positioning of dose‐limiting normal tissues. This study quantified the reproducibility of abdominal normal tissues and respiratory motion with the use of an abdominal compression device using MR imaging.

Methods

Twenty healthy volunteers had repeat MR over 3 days under an abdominal compression plate device. Normal tissues were delineated on daily axial T2‐weighted MR and compared on days 2 and 3 relative to day 1, after adjusting for baseline shifts relative to bony anatomy. Inter‐fraction organ deformation was computed using deformable registration of axial T2 images. Deformation > 5 mm was assumed to be clinically relevant. Inter‐fraction respiratory amplitude changes and intra‐fraction baseline drifts during imaging were quantified on daily orthogonal cine‐MR (70 s each), and changes > 3 mm were assumed to be relevant.

Results

On axial MR, the mean inter‐fraction normal tissue deformation was > 5 mm for all organs (range 5.1–13.4 mm). Inter‐fraction compression device misplacements > 5 mm and changes in stomach volume > 50% occurred at a rate of 93% and 38%, respectively, in one or more directions and were associated with larger adjacent organ deformation, in particular for the duodenum. On cine‐MR, inter‐fraction amplitude changes > 3 mm on day 2 and 3 relative to day 1 occurred at a rate of < 12.5% (mean superior–inferior change was 1.6 mm). Intra‐fraction baseline drifts > 3 mm during any cine‐MR acquisition occurred at a rate of 23% (mean superior–inferior changes was 2.4 mm).

Conclusions

Respiratory motion under abdominal compression is reproducible in most subjects within 3 mm. However, inter‐fraction deformations greater than 5 mm in normal tissues were common and larger than inter‐ and intra‐fraction respiratory changes. Deformations were driven mostly by variable stomach contents and device positioning. The magnitude of this motion may impact normal tissue dosimetry during stereotactic radiotherapy.

Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks

PURPOSE

The deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling.

METHODS AND MATERIALS

Manual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours.

RESULTS

The mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: μContrast(N = 17): 0.99 mm (0.47-2.2), μNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and μMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician.

CONCLUSIONS

The autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks

BACKGROUND AND PURPOSE

Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy.

METHODS AND MATERIALS

We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three-dimensional (3D) point cloud (PC) matching network. Image intensity-based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network's motion prediction with biomechanical constraints, finite element (FE) modeling-generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field.

RESULTS

The mean and standard deviation of our method were 0.93 ± 0.01, 1.66 ± 0.10 mm, and 2.68 ± 1.91 mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity-based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality-independent neighborhood descriptor (MIND) registration, respectively.

CONCLUSION

We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.

Radiomics for liver tumours

Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.

Multimodal imaging in radiotherapy: Focus on adaptive therapy and quality control

Improved computer resources in radiation oncology department have greatly facilitated the integration of multimodal imaging into the workflow of radiation therapy. Nowadays, physicians have highly informative imaging modalities of the anatomical region to be treated. These images contribute to the targeting accuracy with the current treatment device, impacting both segmentation or patient's positioning. Additionally, in a constant effort to deliver personalized care, many teams seek to confirm the benefits of adaptive radiotherapy. The published works highlight the importance of registration algorithms, particularly those of elastic or deformable registration necessary to take into account the anatomical evolutions of the patients during the course of their therapy. These algorithms, often considered as "black boxes", tend to be better controlled and understood by physicists and physicians thanks to the generalization of evaluation and validation methods. Given the still significant development of medical imaging techniques, it is foreseeable that multimodal registration needs require more efficient algorithms well integrated within the flow of data.

Associations between voxel-level accumulated dose and rectal toxicity in prostate radiotherapy

Background and Purpose

Associations between dose and rectal toxicity in prostate radiotherapy are generally poorly understood. Evaluating spatial dose distributions to the rectal wall (RW) may lead to improvements in dose-toxicity modelling by incorporating geometric information, masked by dose-volume histograms. Furthermore, predictive power may be strengthened by incorporating the effects of interfraction motion into delivered dose calculations.Here we interrogate 3D dose distributions for patients with and without toxicity to identify rectal subregions at risk (SRR), and compare the discriminatory ability of planned and delivered dose.

Material and Methods

Daily delivered dose to the rectum was calculated using image guidance scans, and accumulated at the voxel level using biomechanical finite element modelling. SRRs were statistically determined for rectal bleeding, proctitis, faecal incontinence and stool frequency from a training set (n = 139), and tested on a validation set (n = 47).

Results

SRR patterns differed per endpoint. Analysing dose to SRRs improved discriminative ability with respect to the full RW for three of four endpoints. Training set AUC and OR analysis produced stronger toxicity associations from accumulated dose than planned dose. For rectal bleeding in particular, accumulated dose to the SRR (AUC 0.76) improved upon dose-toxicity associations derived from planned dose to the RW (AUC 0.63). However, validation results could not be considered significant.

Conclusions

Voxel-level analysis of dose to the RW revealed SRRs associated with rectal toxicity, suggesting non-homogeneous intra-organ radiosensitivity. Incorporating spatial features of accumulated delivered dose improved dose-toxicity associations. This may be an important tool for adaptive radiotherapy in the future.

A quantitative evaluation of deformable image registration based on MV cone beam CT images: Impact of deformation magnitudes and image modalities

BACKGROUND AND PURPOSE

To evaluate the impact of deformation magnitude and image modality on deformable-image-registration (DIR) accuracy using Halcyon megavoltage cone beam CT images (MVCBCT).

MATERIALS AND METHODS

Planning CT images of an anthropomorphic Head phantom were aligned rigidly with MVCBCT and re-sampled to achieve the same resolution, denoted as pCT. MVCBCT was warped with twenty simulated pre-known virtual deformation fields (T_i, i = 1-20) with increasing deformation magnitudes, yielding warped CBCT (wCBCT). The pCT and MVCBCT were registered to wCBCT respectively (Multi-modality and Uni-modality DIR), generating deformation vector fields V_i and V_i' (i = 1-20). V_i and V_i' were compared with T_i respectively to assess the DIR accuracy geometrically. In addition, V_i, T_i, and V_i' were applied to pCT, generating deformed CT (dCT_i), ground-truth CT (G_i) and deformed CT' (dCT_i') respectively. The Hounsfield Unit (HU) on these virtual CT images were also compared.

RESULTS

The mean errors of vector displacement increased with the deformation magnitude. For deformation magnitudes between 2.82 mm and 7.71 mm, the errors of uni-modality DIR were 1.16 mm ~ 1.73 mm smaller than that of multi-modality (p = 0.0001, Wilcoxon signed rank test). DIR could reduce the maximum signed and absolute HU deviations from 70.8 HU to 11.4 HU and 208 HU to 46.2 HU respectively.

CONCLUSIONS

As deformation magnitude increases, DIR accuracy continues to deteriorate and uni-modality DIR consistently outperformed multi-modality DIR. DIR-based adaptive radiotherapy utilizing the noisy MVCBCT images is only conditionally applicable with caution.

Volumetric cine magnetic resonance imaging (VC-MRI) using motion modeling, free-form deformation and multi-slice undersampled 2D cine MRI reconstructed with spatio-temporal low-rank decomposition

Background

The purpose of this study is to improve on-board volumetric cine magnetic resonance imaging (VC-MRI) using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D-MRI, motion modeling (MM) and free-form deformation (FD) for real-time 3D target verification of liver and lung radiotherapy.

Methods

A previous method was developed to generate on-board VC-MRI by deforming prior MRI images based on a MM and a single-slice on-board 2D-cine image. The two major improvements over the previous method are: (I) FD was introduced to estimate VC-MRI to correct for inaccuracies in the MM; (II) multi-slice undersampled 2D-cine images reconstructed by a k-t SLR reconstruction method were used for FD-based estimation to maintain the temporal resolution while improving the accuracy of VC-MRI. The method was evaluated using XCAT lung simulation and four liver patients' data.

Results

For XCAT, VC-MRI estimated using ten undersampled sagittal 2D-cine MRIs resulted in volume percent difference/volume dice coefficient/center-of-mass shift of 9.77%±3.71%/0.95±0.02/0.75±0.26 mm among all scenarios based on estimation with MM and FD. Adding FD optimization improved VC-MRI accuracy substantially for scenarios with anatomical changes. For patient data, the mean tumor tracking errors were 0.64±0.51, 0.62±0.47 and 0.24±0.24 mm along the superior-inferior (SI), anterior-posterior (AP) and lateral directions, respectively, across all liver patients.

Conclusions

It is feasible to improve VC-MRI accuracy while maintaining high temporal resolution using FD and multi-slice undersampled 2D cine images for real-time 3D target verification.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

Deformable image registration for dose mapping between external beam radiotherapy and brachytherapy images of cervical cancer

For locally advanced cervical cancer (LACC), anatomy correspondence with and without BT applicator needs to be quantified to merge the delivered doses of external beam radiation therapy (EBRT) and brachytherapy (BT). This study proposed and evaluated different deformable image registration (DIR) methods for this application. Twenty patients who underwent EBRT and BT for LACC were retrospectively analyzed. Each patient had a pre-BT CT at EBRT boost (without applicator) and a CT and MRI at BT (with applicator). The evaluated DIR methods were the diffeomorphic Demons, commercial intensity and hybrid methods, and three different biomechanical models. The biomechanical models considered different boundary conditions (BCs). The impact of the BT devices insertion on the anatomy was quantified. DIR method performances were quantified using geometric criteria between the original and deformed contours. The BT dose was deformed toward the pre-CT BT by each DIR method. The impact of boundary conditions to drive the biomechanical model was evaluated based on the deformation vector field and dose differences. The GEC-ESTRO guideline dose indices were reported. Large organ displacements, deformations, and volume variations were observed between the pre-BT and BT anatomies. Rigid registration and intensity-based DIR resulted in poor geometric accuracy with mean Dice similarity coefficient (DSC) inferior to 0.57, 0.63, 0.42, 0.32, and 0.43 for the rectum, bladder, vagina, cervix and uterus, respectively. Biomechanical models provided a mean DSC of 0.96 for all the organs. By considering the cervix-uterus as one single structure, biomechanical models provided a mean DSC of 0.88 and 0.94 for the cervix and uterus, respectively. The deformed doses were represented for each DIR method. Caution should be used when performing DIR for this application as standard techniques may have unacceptable results. The biomechanical model with the cervix-uterus as one structure provided the most realistic deformations to propagate the BT dose toward the EBRT boost anatomy.

Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System

PURPOSE

Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated.

METHODS

Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry.

RESULTS

The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs.

CONCLUSIONS

After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.

Practical quantification of image registration accuracy following the AAPM TG-132 report framework

The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step description of the quantitative tests’ execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs’ contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task‐specific accuracy quantification should be expected from the vendors.

Quantitative analysis of respiration-induced motion of each liver segment with helical computed tomography and 4-dimensional computed tomography

Background

To analyze the respiratory-induced motion of each liver segment using helical computed tomography (helical CT) and 4-dimensional computed tomography (4DCT), and to establish the individual segment expansion margin of internal target volume (ITV) to facilitate target delineation of tumors in different liver segments.

Methods

Twenty patients who received radiotherapy with CT-simulation scanning of the whole liver in both helical CT and 10-phase-gated 4DCT were investigated, including 2 patients with esophagus cancer, 4 with lung cancer, 10 with breast cancer, 2 with liver cancer, 1 with thymoma, and 1 with gastric diffuse large B-cell lymphoma (DLBCL). For each patient, 9 representative points were drawn on the helical CT images of liver segments 1, 2, 3, 4a, 4b, 5, 6, 7, and 8, respectively, and adaptively deformed to 2 phases of the 4DCT images at the end of inspiration (phase 0 CT) and expiration (phase 50 CT) in the treatment planning system. Using the amplitude of each point between phase 0 CT and phase 50 CT, we established quantitative data for the respiration-induced motion of each liver segment in 3-dimensional directions. Moreover, using the amplitude between the original helical CT and both 4DCT images, we rendered the individual segment expansion margin of ITV for hepatic target delineation to cover more than 95% of each tumor.

Results

The average amplitude (mean ± standard deviation) was 0.6 ± 3.0 mm in the left-right (LR) direction, 2.3 ± 2.4 mm in the anterior-posterior (AP) direction, and 5.7 ± 3.4 mm in the superior-inferior (SI) direction, respectively. All of the segments moved posteriorly and superiorly during expiration. Segment 7 had the largest amplitude in the SI direction, at 8.6 ± 3.4 mm. Otherwise, the segments over the lateral side, including segments 2, 3, 6, and 7, had greater excursion in the SI direction compared to the medial segments. To cover more than 95% of each tumor, the required expansion margin of ITV in the LR, AP, and SI directions were at least 2.5 mm, 2.5 mm, and 5.0 mm on average, respectively, with variations between different segments.

Conclusions

The greatest excursion occurred in liver segment 7, followed by the segments over the lateral side in the SI direction. The individual segment expansion margin of ITV is required to delineate targets for each segment and direction.

Influence of deformable image registration on 4D dose simulation for extracranial SBRT: A multi-registration framework study

BACKGROUND AND PURPOSE

To evaluate the influence of deformable image registration approaches on correspondence model-based 4D dose simulation in extracranial SBRT by means of open source deformable image registration (DIR) frameworks.

MATERIAL AND METHODS

Established DIR algorithms of six different open source DIR frameworks were considered and registration accuracy evaluated using freely available 4D image data. Furthermore, correspondence models (regression-based correlation of external breathing signal measurements and internal structure motion field) were built and model accuracy evaluated. Finally, the DIR algorithms were applied for motion field estimation in radiotherapy planning 4D CT data of five lung and five liver lesion patients, correspondence model formation, and model-based 4D dose simulation. Deviations between the original, statically planned and the 4D-simulated VMAT dose distributions were analyzed and correlated to DIR accuracy differences.

RESULTS

Registration errors varied among the DIR approaches, with lower DIR accuracy translating into lower correspondence modeling accuracy. Yet, for lung metastases, indices of 4D-simulated dose distributions widely agreed, irrespective of DIR accuracy differences. In contrast, liver metastases 4D dose simulation results strongly vary for the different DIR approaches.

CONCLUSIONS

Especially in treatment areas with low image contrast (e.g. the liver), DIR-based 4D dose simulation results strongly depend on the applied DIR algorithm, drawing resulting dose simulations and indices questionable.

Technical Note: Solving the "Chinese postman problem" for effective contour deformation

PURPOSE

To develop a practical approach for accurate contour deformation when deformable image registration (DIR) is used for atlas-based segmentation or contour propagation in image-guided radiotherapy.

METHODS

We developed a contour deformation approach based on 3D mesh operations. The 2D contours represented by a series of points in each slice were first converted to a 3D triangular mesh, which was deformed by the deformation vectors resulting from DIR. A set of parallel 2D planes then cut through the deformed 3D mesh, generating unordered points and line segments, to be reorganized into a set of 2D contour points. The reorganization problem was equivalent to solving the "Chinese postman problem" (CPP) by traversing a graph built from the unordered points with the least cost. Alternatively, deformation could be applied to a binary image converted from the original contours. The deformed binary image was then converted back into contours at the CT slice locations. We validated the mesh-based contour deformation approach using lung and heart contours from 10 patients with thoracic cancer.

RESULTS

DIR could change the 3D mesh considerably, complicating 2D contour representations after deformation. CPP could effectively reorganize the points in 2D planes regardless of how complicated the 2D contours were. Among the 10 patients, the Dice similarity coefficient between the mesh-based contour and binary image-based contour was 97.6% ± 0.3% for lung and 97.5% ± 0.7% for heart, and the Hausdoroff distance between them was 19.8 ± 5.1 mm for lung and 6.1 ± 2.2 mm for heart. Subjective evaluation showed that the mesh-based approach could keep fine details, especially for the lung. The image-based approach seemed to overprocess contours and suffered from image resolution limits.

CONCLUSION

We developed a practical approach for accurate contour deformation and demonstrated its effectiveness for both clinical and research applications.