Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

A review of deep learning-based deformable medical image registration

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

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

Purpose

This study aimed to develop a routine quality assurance method for a dose accumulation technique provided by a radiation therapy platform for online treatment adaptation.

Methods and Materials

Two commonly used phantoms were selected for the dose accumulation QA: Electron density and anthropomorphic pelvis. On a computed tomography (CT) scan of the electron density phantom, 1 target (gross tumor volume [GTV]; insert at 6 o'clock), a subvolume within this target, and 7 organs at risk (OARs; other inserts) were contoured in the treatment planning system (TPS). Two adaptation sessions were performed in which the GTV was recontoured, first at 7 o'clock and then at 5 o'clock. The accumulated dose was exported from the TPS after delivery. Deformable vector fields were also exported to manually accumulate doses for comparison. For the pelvis phantom, synthetic Gaussian deformations were applied to the planning CT image to simulate organ changes. Two single-fraction adaptive plans were created based on the deformed planning CT and cone beam CT images acquired onboard the radiation therapy platform. A manual dose accumulation was performed after delivery using the exported deformable vector fields for comparison with the system-generated result.

Results

All plans were successfully delivered, and the accumulated dose was both manually calculated and derived from the TPS. For the electron density phantom, the average mean dose differences in the GTV, boost volume, and OARs 1 to 7 were 0.0%, -0.2%, 92.0%, 78.4%, 1.8%, 1.9%, 0.0%, 0.0%, and 2.3%, respectively, between the manually summed and platform-accumulated doses. The gamma passing rates for the 3-dimensional dose comparison between the manually generated and TPS-provided dose accumulations were >99% for both phantoms.

Conclusions

This study demonstrated agreement between manually obtained and TPS-generated accumulated doses in terms of both mean structure doses and local 3-dimensional dose distributions. Large disagreements were observed for OAR1 and OAR2 defined on the electron density phantom due to OARs having lower deformation priority over the target in addition to artificially large changes in position induced for these structures fraction-by-fraction. The tests applied in this study to a commercial platform provide a straightforward approach toward the development of routine quality assurance of dose accumulation in online adaptation.

Automatic breast lesion segmentation in phase preserved DCE-MRIs

We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

A clinically relevant online patient QA solution with daily CT scans and EPID-based in vivo dosimetry: a feasibility study on rectal cancer

Objective. Adaptive radiation therapy (ART) could protect organs at risk (OARs) while maintain high dose coverage to targets. However, there is still a lack of efficient online patient quality assurance (QA) methods, which is an obstacle to large-scale adoption of ART. We aim to develop a clinically relevant online patient QA solution for ART using daily CT scans and EPID-based in vivo dosimetry. Approach. Ten patients with rectal cancer at our center were included. Patients’ daily CT scans and portal images were collected to generate reconstructed 3D dose distributions. Contours of targets and OARs were recontoured on these daily CT scans by a clinician or an auto-segmentation algorithm, then dose-volume indices were calculated, and the percent deviation of these indices to their original plans were determined. This deviation was regarded as the metric for clinically relevant patient QA. The tolerance level was obtained using a 95% confidence interval of the QA metric distribution. These deviations could be further divided into anatomically relevant or delivery relevant indicators for error source analysis. Finally, our QA solution was validated on an additional six clinical patients. Main results. In rectal cancer, the 95% confidence intervals of the QA metric for PTV ΔD 95 (%) were [−3.11%, 2.35%], and for PTV ΔD 2 (%) were [−0.78%, 3.23%]. In validation, 68% for PTV ΔD 95 (%), and 79% for PTV ΔD 2 (%) of the 28 fractions are within tolerances of the QA metrics. one patient’s dosimetric impact of anatomical variations during treatment were observed through the source of error analysis. Significance. The online patient QA solution using daily CT scans and EPID-based in vivo dosimetry is clinically feasible. Source of error analysis has the potential for distinguishing sources of error and guiding ART for future treatments.

Integrating external beam and prostate seed implant dosimetry for intermediate and high-risk prostate cancer using biologically effective dose: Impact of image registration technique

PURPOSE

Combining external beam radiation therapy (EBRT) and prostate seed implant (PSI) is efficacious in treating intermediate- and high-risk prostate cancer at the cost of increased genitourinary toxicity. Accurate combined dosimetry remains elusive due to lack of registration between treatment plans and different biological effect. The current work proposes a method to convert physical dose to biological effective dose (BED) and spatially register the dose distributions for more accurate combined dosimetry.

METHODS AND MATERIALS

A PSI phantom was CT scanned with and without seeds under rigid and deformed transformations. The resulting CTs were registered using image-based rigid registration (RI), fiducial-based rigid registration (RF), or b-spline deformable image registration (DIR) to determine which was most accurate. Physical EBRT and PSI dose distributions from a sample of 91 previously-treated combined-modality prostate cancer patients were converted to BED and registered using RI, RF, and DIR. Forty-eight (48) previously-treated patients whose PSI occurred before EBRT were included as a "control" group due to inherent registration. Dose-volume histogram (DVH) parameters were compared for RI, RF, DIR, DICOM, and scalar addition of DVH parameters using ANOVA or independent Student's t tests (α = 0.05).

RESULTS

In the phantom study, DIR was the most accurate registration algorithm, especially in the case of deformation. In the patient study, dosimetry from RI was significantly different than the other registration algorithms, including the control group. Dosimetry from RF and DIR were not significantly different from the control group or each other.

CONCLUSIONS

Combined dosimetry with BED and image registration is feasible. Future work will utilize this method to correlate dosimetry with clinical outcomes.

[Evaluation of Accuracy of Deformable Image Registration with Newly Developed Treatment Planning Support Software for Thoracic Images]

This study evaluated accuracy of deformable image registration (DIR) with twelve parameter settings for thoracic images. We used peak-inhale and peak-exhale images for ten patients provided by DIR-lab. We used a prototype version of iCView software (ITEM Corporation) with DIR to perform intensity, structure, and hybrid-based DIR with the twelve parameter settings. DIR accuracy was evaluated by a target registration error (TRE) using 300 bronchial bifurcations and the Dice similarity coefficient (DSC) of the lungs. For twelve parameter settings, TRE ranged from 2.83 mm to 5.27 mm, whereas DSC ranged from 0.96 to 0.98. These results demonstrated that DIR accuracy differed among parameter settings and show that appropriate parameter settings are required for clinical practice.

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

BACKGROUND AND PURPOSE

For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy.

MATERIALS AND METHODS

Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study.

RESULTS

For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary.

CONCLUSIONS

Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.

Feasibility of 4D VMAT-CT

Objective.Feasibility of three-dimensional (3D) tracking of volumetric modulated arc therapy (VMAT) based on VMAT-computed tomography (VMAT-CT) has been shown previously by our group. However, 3D VMAT-CT is not suitable for treatments that involve significant target movement due to patient breathing. The goal of this study was to reconstruct four-dimensional (4D) VMAT-CT and evaluate the feasibility of tracking based on 4D VMAT-CT.Approach.Synchronized portal images of phantoms and linac log were both sorted into four phases, and VMAT-CT+ was generated in each phase by fusing reconstructed VMAT-CT and planning CT using rigid or deformable registration. Dose was calculated in each phase and was registered to the mean position planning CT for 4D dose reconstruction. Trackings based on 4D VMAT-CT+ and 4D cone beam CT (CBCT) were compared. Potential uncertainties were also evaluated.Main results.Tracking based on 4D VMAT-CT+ was accurate, could detect phantom deformation and/or change of breathing pattern, and was superior to that based on 4D CBCT. The impact of uncertainties on tracking was minimal.Significance.Our study shows it is feasible to accurately track position and dose based on 4D VMAT-CT for patients whose VMAT treatments are subject to respiratory motion. It will significantly increase the confidence of VMAT and is a clinically viable solution to daily patient positioning,in vivodosimetry and treatment monitoring.

COMP Report: CPQR technical quality control guidelines for use of positron emission tomography/computed tomography in radiation treatment planning

Positron emission tomography with x-ray computed tomography (PET/CT) is increasingly being utilized for radiation treatment planning (RTP). Accurate delivery of RT therefore depends on quality PET/CT data. This study covers quality control (QC) procedures required for PET/CT for diagnostic imaging and incremental QC required for RTP. Based on a review of the literature, it compiles a list of recommended tests, performance frequencies, and tolerances, as well as references to documents detailing how to perform each test. The report was commissioned by the Canadian Organization of Medical Physicists as part of the Canadian Partnership for Quality Radiotherapy initiative.

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging

PURPOSE

Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans.

METHODS AND MATERIALS

A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 patients (65 for training and 10 for testing). The DL-SAS model performance was compared with 3 common autocontouring methods (linear interpolation, rigid propagation, and a full 3-dimensional DL autosegmentation model trained with the same training data set) based on various quantitative metrics including the Dice similarity coefficient (DSC) and ratio of acceptable slices (ROA) using paired t tests.

RESULTS

For the 10 testing cases, the DL-SAS model performed best with the slice interval (SI) of 1, resulting in an average DSC of 0.93 ± 0.02, 0.92 ± 0.02, 0.91 ± 0.02, 0.88 ± 0.03, and 0.87 ± 0.02 for the large bowel, stomach, small bowel, duodenum, and pancreas, respectively. The performance decreased with increased SIs from the guidance slice. The DL-SAS method performed significantly better (P < .05) than the other 3 methods. The ROA values were in the range of 48% to 66% for all the organs with an SI of 1 for DL-SAS, higher than those for linear interpolation (31%-57% for an SI of 1) and DL auto-segmentation (16%-51%).

CONCLUSIONS

The developed DL-SAS model segmented complex abdominal structures on MRI with high accuracy and efficiency and may be implemented as an interactive manual contouring tool or a contour editing tool in conjunction with a full autosegmentation process, facilitating fast and accurate segmentation for MRI-guided online adaptive radiation therapy.

Evaluation of dose accumulation methods and workflows utilising cone beam computed tomography images

INTRODUCTION

Various adaptive radiation therapy (ART) methods have emerged, with little consensus amongst the literature as to which is most appropriate. This study aimed to compare dose mapping (DM) versus Monte Carlo recalculation (MCR), using cone beam computed tomography (CBCT) images when utilised in automated ART dose accumulation workflows in the MIM Maestro software package.

METHODS

The treatment plans for 38 cancer patients (19 prostate and 19 head and neck cases) were used to perform DM or MCR retrospectively upon CBCTs acquired during treatment, which were then deformably registered to the planning CT (DR-pCT) to facilitate dose accumulation. Dose-volume and region-of-interest data were extracted for the planning target volumes and organs at risk. Intraclass correlation (ICC) values and Bland-Altman plots were utilised to compare DM versus MCR doses on the CBCT images as well as CBCT versus DR-pCT doses.

RESULTS

When comparing DM and MCR on CBCTs, the differences across dose level mean dose differences were mostly within a ±5% level of agreement based on the Bland-Altman plots, with over 67% of ICC values over 0.9 and indicative of good correlation. When these distributions were deformed back to the planning CT, the agreement was reduced considerably, with larger differences (exceeding ±5%) resulting from workflow-related issues.

CONCLUSION

The results emphasise the need to consider and make adaptations to minimise the effect of workflows on algorithm performance. Manual user intervention, refined departmental protocols and further developments to the MIM Maestro software will enhance the use of this tool.

Treatment-integrated imaging, radiomics, and personalised radiotherapy: the future is at hand

Since the introduction of computed tomography for planning purposes in the 1970s, we have been observing a continuous development of different imaging methods in radiotherapy. The current achievements of imaging technologies in radiotherapy enable more than just improvement of accuracy on the planning stage. Through integrating imaging with treatment machines, they allow advanced control methods of dose delivery during the treatment. This article reviews how the integration of existing and novel forms of imaging changes radiotherapy and how these advances can allow a more individualised approach to cancer therapy. We believe that the significant challenge for the next decade is the continued integration of a range of different imaging devices into linear accelerators. These imaging modalities should show intra-fraction changes in body morphology and inter-fraction metabolic changes. As the use of these more advanced, integrated machines grows, radiotherapy delivery will become more accurate, thus resulting in better clinical outcomes: higher cure rates with fewer side effects.

The impact of an educational tool in cervix image registration across three imaging modalities

OBJECTIVES

Accurate image registration is vital in cervical cancer where changes in both planning target volume (PTV) and organs at risk (OARs) can make decisions regarding image registration complicated. This work aims to determine the impact of a dedicated educational tool compared with experience gained in MR-guided radiotherapy (MRgRT).

METHODS

10 therapeutic radiographers acted as observers and were split into two groups based on previous experience with MRgRT and Monaco treatment planning system. Three CBCT-CT, three MR-CT and two MR-MR registrations were completed per patient by each observer. Observers recorded translations, time to complete image registration and confidence. Data were collected in two phases; prior to and following the introduction of a cervix registration guide.

RESULTS

No statistically significant differences were noted between imaging modalities. Each group was assessed independently pre- and post-education, no statistically significant differences were noted in either CBCT-CT or MR-CT imaging. Group 1 MR-MR imaging showed a statistically significant reduction in interobserver variability (p=0.04), in Group 2, the result was not statistically significant (p=0.06). Statistically significant increases in confidence were seen in all three modalities (p≤0.05).

CONCLUSIONS

At The Christie NHS Foundation Trust, radiographers consistently registered images across three different imaging modalities regardless of their previous experience. The implementation of an image registration guide had limited impact on inter- and intraobserver variability. Radiographers' confidence showed statistically significant improvements following the use of the registration manual.

ADVANCES IN KNOWLEDGE

This work helps evaluate training methods for novel roles that are developing in MRgRT.

CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration

BACKGROUND AND OBJECTIVE

Computer tomography (CT) to cone-beam computed tomography (CBCT) image registration plays an important role in radiotherapy treatment placement, dose verification, and anatomic changes monitoring during radiotherapy. However, fast and accurate CT-to-CBCT image registration is still very challenging due to the intensity differences, the poor image quality of CBCT images, and inconsistent structure information.

METHODS

To address these problems, a novel unsupervised network named cross-domain fusion registration network (CDFRegNet) is proposed. First, a novel edge-guided attention module (EGAM) is designed, aiming at capturing edge information based on the gradient prior images and guiding the network to model the spatial correspondence between two image domains. Moreover, a novel cross-domain attention module (CDAM) is proposed to improve the network's ability to guide the network to effectively map and fuse the domain-specific features.

RESULTS

Extensive experiments on a real clinical dataset were carried out, and the experimental results verify that the proposed CDFRegNet can register CT to CBCT images effectively and obtain the best performance, while compared with other representative methods, with a mean DSC of 80.01±7.16%, a mean TRE of 2.27±0.62 mm, and a mean MHD of 1.50±0.32 mm. The ablation experiments also proved that our EGAM and CDAM can further improve the accuracy of the registration network and they can generalize well to other registration networks.

CONCLUSION

This paper proposed a novel CT-to-CBCT registration method based on EGAM and CDAM, which has the potential to improve the accuracy of multi-domain image registration.

Auto-segmentation for total marrow irradiation

Purpose

To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis.

Methods

An AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen-Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures.

Results

A total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%).

Conclusions

AI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.

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.

Reirradiation: A complex situation

Reirradiation of a tumor recurrence or second cancer in a previously irradiated area is challenging due to lack of high-quality physical, radiobiological, clinical data and inherent substantial risks of toxicity with cumulative dose and uncertain tissue recovery. Yet, major advances have been made in radiotherapy techniques, that have the potential to achieve cure while limiting severe toxicity rates, but still much research is necessary to better appraise the therapeutic index in such a complex situation.

Abdominopelvic MR to CT registration using a synthetic CT intermediate

Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long‐standing interest in multimodality co‐registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT‐CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT‐CT DIR is applied to the MRI to register it with the CT. Twenty‐five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI‐ and LPD‐based methods, and the multimodality DIR provided by a state of the art, commercially available FDA‐cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD‐ and MI‐based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI‐ and LPD‐based methods.

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.

Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet)

Purpose. Real-time three-dimensional (3D) magnetic resonance (MR) imaging is challenging because of slow MR signal acquisition, leading to highly under-sampled k-space data. Here, we proposed a deep learning-based, k-space-driven deformable registration network (KS-RegNet) for real-time 3D MR imaging. By incorporating prior information, KS-RegNet performs a deformable image registration between a fully-sampled prior image and on-board images acquired from highly-under-sampled k-space data, to generate high-quality on-board images for real-time motion tracking. Methods. KS-RegNet is an end-to-end, unsupervised network consisting of an input data generation block, a subsequent U-Net core block, and following operations to compute data fidelity and regularization losses. The input data involved a fully-sampled, complex-valued prior image, and the k-space data of an on-board, real-time MR image (MRI). From the k-space data, under-sampled real-time MRI was reconstructed by the data generation block to input into the U-Net core. In addition, to train the U-Net core to learn the under-sampling artifacts, the k-space data of the prior image was intentionally under-sampled using the same readout trajectory as the real-time MRI, and reconstructed to serve an additional input. The U-Net core predicted a deformation vector field that deforms the prior MRI to on-board real-time MRI. To avoid adverse effects of quantifying image similarity on the artifacts-ridden images, the data fidelity loss of deformation was evaluated directly in k-space. Results. Compared with Elastix and other deep learning network architectures, KS-RegNet demonstrated better and more stable performance. The average (±s.d.) DICE coefficients of KS-RegNet on a cardiac dataset for the 5- , 9- , and 13-spoke k-space acquisitions were 0.884 ± 0.025, 0.889 ± 0.024, and 0.894 ± 0.022, respectively; and the corresponding average (±s.d.) center-of-mass errors (COMEs) were 1.21 ± 1.09, 1.29 ± 1.22, and 1.01 ± 0.86 mm, respectively. KS-RegNet also provided the best performance on an abdominal dataset. Conclusion. KS-RegNet allows real-time MRI generation with sub-second latency. It enables potential real-time MR-guided soft tissue tracking, tumor localization, and radiotherapy plan adaptation.

Estimating medical image registration error and confidence: A taxonomy and scoping review

Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.

Interobserver variability of Gross Tumour Volume delineation for colorectal liver metastases using CT and MRI

Purpose

The purpose of this study was to evaluate the interobserver variability in the contouring of the gross tumor volume (GTV) on magnetic resonance (MR) imaging and computed tomography (CT) for colorectal liver metastases in the setting of SABR.

Methods and Materials

Three expert radiation oncologists contoured 10 GTV volumes on 3 MR imaging sequences and on the CT image data set. Three metrics were chosen to evaluate the interobserver variability: the conformity index, the DICE coefficient, and the maximum Hausdorff distance (HDmax). Statistical analysis of the results was performed using a 1-sided permutation test.

Results

For all 3 metrics, the MR liver acquisition volume acquisition (MR LAVA) showed the lowest interobserver variability. Analysis showed a significant difference (P < .01) in the mean DICE, an overlap metric, for MR LAVA (0.82) and CT (0.74). The HDmax that highlights boundary errors also showed a significant difference (P = .04) with MR LAVA having a lower mean HDmax (7.2 mm) compared with CT (5.7 mm). The mean HDmax for both MR single shot fast spin echo (SSFSE) (19.3 mm) and diffusion weighted image (9.5 mm) showed large interobserver variability with MR SSFSE having a mean HDmax of 19.3 mm. A volume comparison between MR LAVA and CT showed a significantly higher volume for small GTVs (<5 cm³) when using MR LAVA for contouring in comparison to CT.

Conclusions

This study reported the lowest interobserver variability for the MR LAVA, thus indicating the benefit of using MR to complement CT when contouring GTV for colorectal liver metastases.

Comparison of compressed sensing and controlled aliasing in parallel imaging acceleration for 3D magnetic resonance imaging for radiotherapy preparation

Magnetic resonance imaging (MRI) for radiotherapy is often based on 3D acquisitions, but suffers from low signal-to-noise ratio due to immobilization device and flexible coil use. The aim of this study was to investigate if Compressed Sensing (CS) improves image quality for 3D Turbo Spin Echo acquisitions compared with Controlled Aliasing k-space-based parallel imaging in equivalent acquisition time for intracranial T1, T2-Fluid-Attenuated Inversion Recovery (FLAIR) and pelvic T2 imaging. Qualitative ratings suffered from large inter-rater variability. CS-T1 brain MRI was superior numerically and qualitatively. CS-T2-FLAIR brain MRI was numerically superior, but rater equivalent. CS-T2 pelvic MRI was equivalent without gain.

Using 4D dose accumulation to calculate organ-at-risk dose deviations from motion-synchronized liver and lung tomotherapy treatments

Tracking systems such as Radixact Synchrony change the planned delivery of radiation during treatment to follow the target. This is typically achieved without considering the location changes of organs at risk (OARs). The goal of this work was to develop a novel 4D dose accumulation framework to quantify OAR dose deviations due to the motion and tracked treatment. The framework obtains deformation information and the target motion pattern from a four-dimensional computed tomography dataset. The helical tomotherapy treatment plan is split into 10 plans and motion correction is applied separately to the jaw pattern and multi-leaf collimator (MLC) sinogram for each phase based on the location of the target in each phase. Deformable image registration (DIR) is calculated from each phase to the references phase using a commercial algorithm, and doses are accumulated according to the DIR. The effect of motion synchronization on OAR dose was analyzed for five lung and five liver subjects by comparing planned versus synchrony-accumulated dose. The motion was compensated by an average of 1.6 cm of jaw sway and by an average of 5.7% of leaf openings modified, indicating that most of the motion compensation was from jaw sway and not MLC changes. OAR dose deviations as large as 19 Gy were observed, and for all 10 cases, dose deviations greater than 7 Gy were observed. Target dose remained relatively constant (D95% within 3 Gy), confirming that motion-synchronization achieved the goal of maintaining target dose. Dose deviations provided by the framework can be leveraged during the treatment planning process by identifying cases where OAR doses may change significantly from their planned values with respect to the critical constraints. The framework is specific to synchronized helical tomotherapy treatments, but the OAR dose deviations apply to any real-time tracking technique that does not consider location changes of OARs.

Co-registration of radiotherapy planning and recurrence scans with different imaging modalities in head and neck cancer

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Head and neck magnetic resonance imaging and computed tomography co-registration.

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Validation of planning and recurrence scan co-registration separated in time.

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Mean distances to agreement for regions of interest/normal tissue were tolerable.

MRI (magnetic resonance imaging) scans are frequently used in follow-up after radiotherapy for head and neck cancer. With the overall aim of enabling MRI-based pattern of failure analysis, this study evaluated the accuracy of recurrence MRI (rMRI) deformable co-registration with planning CT (computed tomography)-scans (pCT). Uncertainty of anatomical changes between pCT and rMRI was assessed by similarity metric analyses of co-registered image structures from 19 patients. Average mean distance to agreement and Dice similarity coefficient performed adequately. Our findings provide proof of concept for reliable co-registration of pCT and rMRI months to years apart for MRI-based pattern of failure analysis.

Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients

Purpose

This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.

Methods

We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V₉₅<95%) and adaptation (V₉₅<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE₁₀) were set for all D_max and D_mean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI₉₅).

Results

RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid D_mean at EOT. Twelve PTVs had V₉₅<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx= 6 (range, 1–16). Seventeen parotids were flagged for exceeding D_mean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE₁₀. The differences between predicted and calculated PTV V₉₅ and parotid D_mean was up to 7.6% (mean ± CI₉₅, −2.7 ± 4.1%) and 5 Gy (mean ± CI₉₅, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.

Conclusion

Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.

Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT

Purpose

To determine the dosimetric impact of using unedited autocontours in daily plan adaptation of patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy using tumor tracking.

Materials and Methods

The study included 98 daily CT scans of 35 LAPC patients. All scans were manually contoured (MAN), and included the PTV and main organs-at-risk (OAR): stomach, duodenum and bowel. Precision and MIM deformable image registration (DIR) methods followed by contour propagation were used to generate autocontour sets on the daily CT scans. Autocontours remained unedited, and were compared to MAN on the whole organs and at 3, 1 and 0.5 cm from the PTV. Manual and autocontoured OAR were used to generate daily plans using the VOLO™ optimizer, and were compared to non-adapted plans. Resulting planned doses were compared based on PTV coverage and OAR dose-constraints.

Results

Overall, both algorithms reported a high agreement between unclipped MAN and autocontours, but showed worse results when being evaluated on the clipped structures at 1 cm and 0.5 cm from the PTV. Replanning with unedited autocontours resulted in better OAR sparing than non-adapted plans for 95% and 84% plans optimized using Precision and MIM autocontours, respectively, and obeyed OAR constraints in 64% and 56% of replans.

Conclusion

For the majority of fractions, manual correction of autocontours could be avoided or be limited to the region closest to the PTV. This practice could further reduce the overall timings of adaptive radiotherapy workflows for patients with LAPC.

Development and evaluation of an automated EPTN-consensus based organ at risk atlas in the brain on MRI

BACKGROUND AND PURPOSE

During radiotherapy treatment planning, avoidance of organs at risk (OARs) is important. An international consensus-based delineation guideline was recently published with 34 OARs in the brain. We developed an MR-based OAR autosegmentation atlas and evaluated its performance compared to manual delineation.

MATERIALS AND METHODS

Anonymized cerebral T1-weighted MR scans (voxel size 0.9x0.9x0.9mm 3) were available. OARs were manually delineated according to international consensus. Fifty MR scans were used to develop the autosegmentation atlas in a commercially available treatment planning system (Raystation®). The performance of this atlas was tested on another 40 MR scans by automatically delineating 34 OARs, as defined by the 2018 EPTN consensus. Spatial overlap between manual and automated delineations was determined by calculating the Dice similarity coefficient (DSC). Two radiation oncologists determined the quality of each automatically delineated OAR. The time needed to delineate all OARs manually or to adjust automatically delineated OARs was determined.

RESULTS

DSC was ≥0.75 in 31 (91%) out of 34 automated OAR delineations. Delineations were rated by radiation oncologists as excellent or good in 29 (85%) out 34 OAR delineations, while 4 were rated fair (12%) and 1 was rated poor (3%). Interobserver agreement between the radiation oncologists ranged from 77-100% per OAR. The time to manually delineate all OARs was 88.5 minutes, while the time needed to adjust automatically delineated OARs was 15.8 minutes.

CONCLUSION

Autosegmentation of OARs enables high-quality contouring within a limited time. Accurate OAR delineation helps to define OAR constraints to mitigate serious complications and helps with the development of NTCP models.

Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system.Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey's honestly significant difference post-hoc tests were utilized for statistical analysis.Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results.Significance. An overall improvement in image quality for metrics most important to IGRT and ART-mainly image noise and contrast-was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.

AAPM Task Group Report 303 endorsed by the ABS: MRI Implementation in HDR Brachytherapy-Considerations from Simulation to Treatment

The Task Group (TG) on Magnetic Resonance Imaging (MRI) Implementation in High Dose Rate (HDR) Brachytherapy - Considerations from Simulation to Treatment, TG 303, was constituted by the American Association of Physicists in Medicine's (AAPM's) Science Council under the direction of the Therapy Physics Committee, the Brachytherapy Subcommittee, and the Working Group on Brachytherapy Clinical Applications. The TG was charged with developing recommendations for commissioning, clinical implementation, and on-going quality assurance (QA). Additionally, the TG was charged with describing HDR brachytherapy (BT) workflows and evaluating practical consideration that arise when implementing MR imaging. For brevity, the report is focused on the treatment of gynecologic and prostate cancer. The TG report provides an introduction and rationale for MRI implementation in BT, a review of previous publications on topics including available applicators, clinical trials, previously published BT related TG reports, and new image guided recommendations beyond CT based practices. The report describes MRI protocols and methodologies, including recommendations for the clinical implementation and logical considerations for MR imaging for HDR BT. Given the evolution from prescriptive to risk-based QA,¹ an example of a risk-based analysis using MRI-based, prostate HDR BT is presented. In summary, the TG report is intended to provide clear and comprehensive guidelines and recommendations for commissioning, clinical implementation, and QA for MRI-based HDR BT that may be utilized by the medical physics community to streamline this process. This report is endorsed by the American Brachytherapy Society (ABS). This article is protected by copyright. All rights reserved.

MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy

The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the 'Deforming to Best Practice' MIRSIG publication to provide guidelines on the use of DIR for advanced applications.

Adaptive replanning using cone beam CT for deformation of original CT simulation

BACKGROUND

During a course of radiation therapy, anatomical changes such as a decrease in tumour size or weight loss can trigger the need for repeating a computed tomography (CT) simulation scan in order to generate a new treatment plan. This adaptive approach requires a separate appointment for an additional CT scan which generates additional burden, cost, and radiation exposure for patients.

CASE PRESENTATION

Here, we present a case of a head and neck cancer patient who required palliative radiation for a large neck mass. During treatment, he had a remarkable response which required a replan due to rapid tumour downsizing. In this case, we used a novel technique to avoid repeating the planning CT simulation by using a mid-treatment high-quality cone beam CT (CBCT) to deform the secondary image (plan CT) of the original planning CT and generate a new adapted treatment plan.

CONCLUSION

This is the first report to our knowledge using a Halcyon CBCT to deform the original planning CT in order to generate a new radiation treatment plan, and this novel technique represents a new potential method of adaptive replanning for select patients.

Adaptive Proton Therapy of Pediatric Head and Neck Cases Using MRI-Based Synthetic CTs: Initial Experience of the Prospective KiAPT Study

BACKGROUND AND PURPOSE

Interfractional anatomical changes might affect the outcome of proton therapy (PT). We aimed to prospectively evaluate the role of Magnetic Resonance Imaging (MRI) based adaptive PT for children with tumors of the head and neck and base of skull.

METHODS

MRI verification images were acquired at half of the treatment course. A synthetic computed tomography (CT) image was created using this MRI and a deformable image registration (DIR) to the reference MRI. The methodology was verified with in-silico phantoms and validated using a clinical case with a shrinking cystic hygroma on the basis of dosimetric quantities of contoured structures. The dose distributions on the verification X-ray CT and on the synthetic CT were compared with a gamma-index test using global 2 mm/2% criteria.

RESULTS

Regarding the clinical validation case, the gamma-index pass rate was 98.3%. Eleven patients were included in the clinical study. The most common diagnosis was rhabdomyosarcoma (73%). Craniofacial tumor site was predominant in 64% of patients, followed by base of skull (18%). For one individual case the synthetic CT showed an increase in the median D2 and Dmax dose on the spinal cord from 20.5 GyRBE to 24.8 GyRBE and 14.7 GyRBE to 25.1 GyRBE, respectively. Otherwise, doses received by OARs remained relatively stable. Similarly, the target volume coverage seen by D95% and V95% remained unchanged.

CONCLUSIONS

The method of transferring anatomical changes from MRIs to a synthetic CTs was successfully implemented and validated with simple, commonly available tools. In the frame of our early results on a small cohort, no clinical relevant deterioration for neither PTV coverage nor an increased dose burden to OARs occurred. However, the study will be continued to identify a pediatric patient cohort, which benefits from adaptive treatment planning.

Accuracy of MRI-CT registration in brain stereotactic radiotherapy: Impact of MRI acquisition setup and registration method

BACKGROUND

In MR-based radiotherapy (RT), MRI images are co-registered to the planning CT to leverage MR image information for RT planning. Especially in brain stereotactic RT, where typical CTV-PTV margins are 1-2 mm, high registration accuracy is critical. Several factors influence the registration accuracy, including the acquisition setup during MR simulation and the registration methods.

PURPOSE

In this work, the impact of the MRI acquisition setup and registration method was evaluated in the context of brain RT, both geometrically and dosimetrically.

METHODS AND MATERIALS

MRI of 20 brain radiotherapy patients was acquired in two MRI acquisition setups (RT and diagnostic). Three different automatic registration tools provided by three treatment planning systems were used to rigidly register both MRIs and CT in addition to the clinical registration. Segmentation-based evaluation using Hausdorff Distance (HD)/Dice Similarity Coefficient and landmark-based evaluation were used as evaluation metrics. Dose-volume-histograms were evaluated for target volumes and various organs at risks.

RESULTS

MRI acquisition in the RT setup provided a similar head extension as compared to the planning CT. The registration method had a more significant influence than the acquisition setup (Wilcoxon signed-rank test, p<0.05). When registering using a less optimal registration method, the RT setup improved the registration accuracy compared to the diagnostic setup (Difference: ΔMHD = 0.16 mm, ΔHD_P95 = 0.64 mm, mean Euclidean distance (ΔmEuD) = 2.65 mm). Different registration methods and acquisition setups lead to the variation of the clinical DVH. Acquiring MRI in the RT setup can improve PTV and GTV coverage compared to the diagnostic setup.

CONCLUSIONS

Both MRI acquisition setup and registration method influence the MRI-CT registration accuracy in brain RT patients geometrically and dosimetrically. MR-simulation in the RT setup assures optimal registration accuracy if automatic registration is impaired, and therefore recommended for brain RT.

Geometric distortion assessment in 3T MR images used for treatment planning in cranial Stereotactic Radiosurgery and Radiotherapy

Magnetic Resonance images (MRIs) are employed in brain Stereotactic Radiosurgery and Radiotherapy (SRS/SRT) for target and/or critical organ localization and delineation. However, MRIs are inherently distorted, which also impacts the accuracy of the Magnetic Resonance Imaging/Computed Tomography (MRI/CT) co-registration process. In this phantom-based study, geometric distortion is assessed in 3T T2-weighted images (T2WIs), while the efficacy of an MRI distortion correction technique is also evaluated. A homogeneous polymer gel-filled phantom was CT-imaged before being irradiated with 26 4-mm Gamma Knife shots at predefined locations (reference control points). The irradiated phantom was MRI-scanned at 3T, implementing a T2-weighted protocol suitable for SRS/SRT treatment planning. The centers of mass of all shots were identified in the 3D image space by implementing an iterative localization algorithm and served as the evaluated control points for MRI distortion detection. MRIs and CT images were spatially co-registered using a mutual information algorithm. The inverse transformation matrix was applied to the reference control points and compared with the corresponding MRI-identified ones to evaluate the overall spatial accuracy of the MRI/CT dataset. The mean image distortion correction technique was implemented, and resulting MRI-corrected control points were compared against the corresponding reference ones. For the scanning parameters used, increased MRI distortion (>1mm) was detected at areas distant from the MRI isocenter (>5cm), while median radial distortion was 0.76mm. Detected offsets were slightly higher for the MRI/CT dataset (0.92mm median distortion). The mean image distortion correction improves geometric accuracy, but residual distortion cannot be considered negligible (0.51mm median distortion). For all three datasets studied, a statistically significant positive correlation between detected spatial offsets and their distance from the MRI isocenter was revealed. This work contributes towards the wider adoption of 3T imaging in SRS/SRT treatment planning. The presented methodology can be employed in commissioning and quality assurance programmes of corresponding treatment workflows.

[Material Investigation for the Development of Non-rigid Phantoms for CT-MRI Image Registration]

PURPOSE

In radiotherapy, deformable image registration (DIR) has been frequently used in different imaging examinations in recent years. However, no phantom has been established for quality assurance for DIR. In order to develop a non-rigid phantom for accuracy control between CT and MRI images, we investigated the suitability of 3D printing materials and gel materials in this study.

METHODS

We measured CT values, T₁ values, T₂ values, and the proton densities of 31 3D printer materials-purchased from three manufacturers-and one gel material. The dice coefficient after DIR was calculated for the CT-MRI images using a prototype phantom made of a gel material compatible with CT-MRI.

RESULTS

The CT number of the 3D printing materials ranged from -6.8 to 146.4 HU. On MRI, T₁ values were not measurable in most cases, whereas T₂ values were not measurable in all cases; proton density (PD) ranged from 2.51% to 4.9%. The gel material had a CT number of 111.16 HU, T₁ value of 813.65 ms, and T₂ value of 27.19 ms. The prototype phantom was flexible, and the usefulness of DIR with CT and MRI images was demonstrated using this phantom.

CONCLUSION

The CT number and T₁ and T₂ values of the gel material are close to those of the human body and may therefore be developed as a DIR verification phantom between CT and MRI. These findings may contribute to the development of non-rigid phantoms for DIR in the future.

Stereotactic Ablative Radiotherapy of Ventricular Tachycardia Using Tracking: Optimized Target Definition Workflow

Background and Purpose

Stereotactic arrhythmia radioablation (STAR) has been suggested as a promising therapeutic alternative in cases of failed catheter ablation for recurrent ventricular tachycardias in patients with structural heart disease. Cyberknife^® robotic radiosurgery system utilizing target tracking technology is one of the available STAR treatment platforms. Tracking using implantable cardioverter-defibrillator lead tip as target surrogate marker is affected by the deformation of marker–target geometry. A simple method to account for the deformation in the target definition process is proposed.

Methods

Radiotherapy planning CT series include scans at expiration and inspiration breath hold, and three free-breathing scans. All secondary series are triple registered to the primary CT: 6D/spine + 3D translation/marker + 3D translation/target surrogate—a heterogeneous structure around the left main coronary artery. The 3D translation difference between the last two registrations reflects the deformation between the marker and the target (surrogate) for the respective respiratory phase. Maximum translation differences in each direction form an anisotropic geometry deformation margin (GDM) to expand the initial single-phase clinical target volume (CTV) to create an internal target volume (ITV) in the dynamic coordinates of the marker. Alternative GDM-based target volumes were created for seven recent STAR patients and compared to the original treated planning target volumes (PTVs) as well as to analogical volumes created using deformable image registration (DIR) by MIM^® and Velocity^® software. Intra- and inter-observer variabilities of the triple registration process were tested as components of the final ITV to PTV margin.

Results

A margin of 2 mm has been found to cover the image registration observer variability. GDM-based target volumes are larger and shifted toward the inspiration phase relative to the original clinical volumes based on a 3-mm isotropic margin without deformation consideration. GDM-based targets are similar (mean DICE similarity coefficient range 0.80–0.87) to their equivalents based on the DIR of the primary target volume delineated by dedicated software.

Conclusion

The proposed GDM method is a simple way to account for marker–target deformation-related uncertainty for tracking with Cyberknife^® and better control of the risk of target underdose. The principle applies to general radiotherapy as well.

Evaluation of clinically involved lymph nodes with deformable registration in breast cancer radiotherapy

OBJECTIVES

Modern radiotherapy (RT) techniques require careful delineation of the target. There is no particular RT contouring guideline for patients receiving neoadjuvant chemotherapy (NACT). In this study, we examined the distribution of pre-chemotherapy clinically positive nodal metastases.

METHODS

We explored the coverage rate of the RTOG breast contouring guideline by deformable fusion of 18-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) scan. We retrospectively evaluated neoadjuvant chemotherapy patients. All PET-CT images were imported into the planning software. We combined the planning CT and the CT images of PET-CT with rigid and then a deformable registration. We manually contoured positive lymph nodes on the CT component of the PET-CT data set and transferred them to planning CT after fusion. We evaluated whether previously contoured lymphatic CTVs, according to the RTOG breast atlas, include GTV-LNs.

RESULTS

All breast cancer patients between October 2018 and February 2021 were evaluated from the electronic database. There were 142 radiologically defined positive lymph nodes in 31 patients who were irradiated after NACT. Most LNs (70%) were in the level I axilla. Only 71.1% (n:101) of the whole lymph nodes in 10 patients were totally covered, 22.5% (n:32) partially covered and 6.4% %(n:9) totally undercovered.

CONCLUSIONS

The extent of regional nodal areas in the RTOG atlas may be insufficient to cover positive lymph nodes adequately. For patients with nodal involvement undergoing neoadjuvant chemotherapy, PET-CT image fusions can be helpful to be sure that positive lymph nodes are in the treatment volume.

ADVANCES IN KNOWLEDGE

RTOG contouring atlas may be insufficient to cover all involved lymph nodes after NACT. For patients with nodal involvement undergoing neoadjuvant chemotherapy, PET-CT image fusions may help to be sure that positive lymph nodes are in the treatment volume.