Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT

Objective

To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).

Methods

This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method.

Results

The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region.

Conclusion

The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.

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.

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.

Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning

Purpose

The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large‐scale displacement of lung tissues caused by respiratory motion cause great challenges in the deformable registration of 3D lung CT images.

Materials and methods

We proposed an end‐to‐end fast registration method based on unsupervised learning, optimized the classic U‐Net, and added inception modules between skip connections. The inception module attempts to capture and merge information at different spatial scales to generate a high‐precision dense displacement vector field. To solve the problem of voxel folding in flexible registration, we put the Jacobian regularization term into the loss function to directly penalize the singularity of the displacement field during training to ensure a smooth displacement vector field. In the stage of data preprocessing, we segmented the lung fields to eliminate the interference of irrelevant information in the network during training. The existing publicly available datasets cannot implement model training. To alleviate the problem of overfitting caused by limited data resources being available, we proposed a data augmentation method based on the 3D‐TPS (3D thin plate spline) transform to expand the training data.

Results

Compared with the experimental results obtained by using the VoxelMorph deep learning method and registration packages, such as ANTs and Elastix, we achieved a competitive target registration error of 2.09 mm, an optimal Dice score of 0.987, and almost no folding voxels. Additionally, the proposed method was much faster than the traditional methods.

Conclusions

In this study, we have shown that the proposed method was efficient in 3D chest image registration. The promising results demonstrated that our method showed strong robustness in the deformable registration of 3D chest CT images.

FEMOSSA: Patient-specific finite element simulation of the prostate-rectum spacer placement, a predictive model for prostate cancer radiotherapy

PURPOSE

Major advances in delivery systems in recent years have turned radiotherapy (RT) into a more effective way to manage prostate cancer. Still, adjacency of organs at risk (OARs) can severely limit RT benefits. Rectal spacer implant in recto-prostatic space provides sufficient separation between prostate and rectum, and therefore, the opportunity for potential dose escalation to the target and reduction of OAR dose. Pretreatment simulation of spacer placement can potentially provide decision support to reduce the risks and increase the efficacy of the spacer placement procedure.

METHODS

A novel finite element method-oriented spacer simulation algorithm, FEMOSSA, was developed in this study. We used the finite element (FE) method to model and predict the deformation of rectum and prostate wall, stemming from hydrogel injection. Ten cases of prostate cancer, which undergone hydrogel placement before the RT treatment, were included in this study. We used the pre-injection organ contours to create the FE model and post-injection spacer location to estimate the distribution of the virtual spacer. Material properties and boundary conditions specific to each patient's anatomy were assigned. The FE analysis was then performed to determine the displacement vectors of regions of interest (ROIs), and the results were validated by comparing the virtually simulated contours with the real post-injection contours. To evaluate the different aspects of our method's performance, we used three different figures of merit: dice similarity coefficient (DSC), nearest neighbor distance (NND), and overlapped volume histogram (OVH). Finally, to demonstrate a potential dosimetric application of FEMOSSA, the predicted rectal dose after virtual spacer placement was compared against the predicted post-injection rectal dose.

RESULTS

Our simulation showed a realistic deformation of ROIs. The post-simulation (virtual spacer) created the same separation between prostate and rectal wall, as post-injection spacer. The average DSCs for prostate and rectum were 0.87 and 0.74, respectively. Moreover, there was a statistically significant increase in rectal contour similarity coefficient (P < 0.01). Histogram of NNDs showed the same overall shape and a noticeable shift from lower to higher values for both post-simulation and post-injection, indicative of the increase in distance between prostate and rectum. There was less than 2.2- and 2.1-mm averaged difference between the mean and fifth percentile NNDs. The difference between the OVH distances and the corresponding predicted rectal dose was, on average, less than 1 mm and 1.5 Gy, respectively.

CONCLUSIONS

FEMOSSA provides a realistic simulation of the hydrogel injection process that can facilitate spacer placement planning and reduce the associated uncertainties. Consequently, it increases the robustness and success rate of spacer placement procedure that in turn improves prostate cancer RT quality.

Analysis of related factors of radiation pneumonia caused by precise radiotherapy of esophageal cancer based on random forest algorithm

The precise radiotherapy of esophageal cancer may cause different degrees of radiation damage for lung tissues and cause radioactive pneumonia. However, the occurrence of radioactive pneumonia is related to many factors. To further clarify the correlation between the occurrence of radioactive pneumonia and related factors, a random forest model was used to build a risk prediction model for patients with esophageal cancer undergoing radiotherapy. In this study, we retrospectively reviewed 118 patients with esophageal cancer confirmed by pathology in our hospital. The health characteristics and related parameters of all patients were analyzed, and the predictive effect of radiation pneumonia was discussed using the random forest algorithm. After treatment, 71 patients developed radioactive pneumonia (60.17%). In univariate analyses, age, planning target volume length, Karnofsky performance score (KPS), pulmonary emphysema, with or without chemotherapy, and the ratio of planning target volume to planning gross tumor volume (PTV/PGTV) in mediastinum were significantly associated with radioactive pneumonia (P < 0.05 for each comparison). Multivariate analysis revealed that with or without pulmonary emphysema (OR = 7.491, P = 0.001), PTV/PGTV (OR = 0.205, P = 0.007), and KPS (OR = 0.251, P = 0.011) were independent predictors for radiation pneumonia. The results concluded that the analysis of radiation pneumonia-related factors based on the random forest algorithm could build a mathematical prediction model for the easily obtained data. This algorithm also could effectively analyze the risk factors of radiation pneumonia and formulate the appropriate treatment plan for esophageal cancer.

Comparison of the Gross Target Volumes Based on Diagnostic PET/CT for Primary Esophageal Cancer

Background

Clinically, many esophageal cancer patients who planned for radiation therapy have already undergone diagnostic Positron-emission tomography/computed tomography (PET/CT) imaging, but it remains unclear whether these imaging results can be used to delineate the gross target volume (GTV) of the primary tumor for thoracic esophageal cancer (EC).

Methods

Seventy-two patients diagnosed with thoracic EC had undergone prior PET/CT for diagnosis and three-dimensional CT (3DCT) for simulation. The GTV_3D was contoured on the 3DCT image without referencing the PET/CT image. The GTV_PET-ref was contoured on the 3DCT image referencing the PET/CT image. The GTV_PET-reg was contoured on the deformed registration image derived from 3DCT and PET/CT. Differences in the position, volume, length, conformity index (CI), and degree of inclusion (DI) among the target volumes were determined.

Results

The centroid distance in the three directions between two different GTVs showed no significant difference (P > 0.05). No significant difference was found among the groups in the tumor volume (P > 0.05). The median DI values of the GTV_PET-reg and GTV_PET-ref in the GTV_3D were 0.82 and 0.86, respectively (P = 0.006). The median CI values of the GTV_3D in the GTV_PET-reg and GTV_PET-ref were 0.68 and 0.72, respectively (P = 0.006).

Conclusions

PET/CT can be used to optimize the definition of the target volume in EC. However, no significant difference was found between the GTVs delineated based on visual referencing or deformable registration whether using the volume or position. So, in the absence of planning PET–CT images, it is also feasible to delineate the GTV of primary thoracic EC with reference to the diagnostic PET–CT image.

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

Accuracy of registrations between cone-beam computed tomography and conventional computed tomography images and dose mapping methods in RaySearch software for the bladder during brachytherapy of cervical cancer patients

Purpose

The aim of the study was to assess selected methods of image registration available in the RaySearch software and their impact on the accuracy of mapping of doses deposited in the bladder during brachytherapy (BRT) of cervical cancer in images used during external beam radiotherapy (EBRT).

Material and methods

The study was based on data from ten patients. Cone-beam computed tomography (CBCT) images (BRT) were aligned with CT images (EBRT) using four registration methods: Reg_1 (rigid), Reg_2a, Reg_2b (hybrid), and Reg_3 (biomechanical). Image mapping accuracy was evaluated based on bladder’s anatomy. Sørensen-Dice coefficient (DSC) values were analyzed for all the registrations. Discrepancies between triangular mesh points set on the basis of bladder contours were analyzed. Dose distributions from BRT were transformed according to registration results and mapped on CT images. Original BRT doses deposited in 2 cm³ volume of the bladder were compared to those transformed and associated with bladder’s volume determined on CT images.

Results

Mean DSC values amounted to 0.36 (Reg_1), 0.87 and 0.88 (Reg_2a and Reg_2b), and 0.97 (Reg_3). Significant differences were found between DSC for the following comparisons: Reg_3/Reg_1 (p = 0.001), Reg_2a/Reg_1 (p = 0.011), and Reg_2b/Reg_1 (p = 0.014). The lowest discrepancies between triangular mesh points were for Reg_3 (p < 0.001, Reg_3 vs. Reg_1, and p = 0.039, Reg_3 vs. Reg_2b). Finally, the lowest discrepancies between the original and transformed doses were found for Reg_3. Nevertheless, only 5 out of 10 observations for Reg_3 yielded error of less than 5%.

Conclusions

Biomechanical registration (Reg_3) enabled the most accurate alignment between CBCT and CT images. Satisfactory registration results of anatomical structures do not guarantee a correct mapping of primary BRT doses on the bladder delineated on CT images during EBRT. The results of dose transformation based on biomechanical registration had an error of less than 5% for only 50% of the observations.

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

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

The impact of 2D cine MR imaging parameters on automated tumor and organ localization for MR-guided real-time adaptive radiotherapy

2D cine MR imaging may be utilized to monitor rapidly moving tumors and organs-at-risk for real-time adaptive radiotherapy. This study systematically investigates the impact of geometric imaging parameters on the ability of 2D cine MR imaging to guide template-matching-driven autocontouring of lung tumors and abdominal organs. Abdominal 4D MR images were acquired of six healthy volunteers and thoracic 4D MR images were obtained of eight lung cancer patients. At each breathing phase of the images, the left kidney and gallbladder or lung tumor, respectively, were outlined as volumes of interest. These images and contours were used to create artificial 2D cine MR images, while simultaneously serving as 3D ground truth. We explored the impact of five different imaging parameters (pixel size, slice thickness, imaging plane orientation, number and relative alignment of images as well as strategies to create training images). For each possible combination of imaging parameters, we generated artificial 2D cine MR images as training and test images. A template-matching algorithm used the training images to determine the tumor or organ position in the test images. Subsequently, a 3D base contour was shifted to the determined position and compared to the ground truth via centroid distance and Dice similarity coefficient. The median centroid distance between adapted and ground truth contours was 1.56 mm for the kidney, 3.81 mm for the gallbladder and 1.03 mm for the lung tumor (median Dice similarity coefficient: 0.95, 0.72 and 0.93). We observed that a decrease in image resolution led to a modest decrease in localization accuracy, especially for the small gallbladder. However, for all volumes of interest localization accuracy varied substantially more between subjects than due to the different imaging parameters. Automated tumor and organ localization using 2D cine MR imaging and template-matching-based autocontouring is robust against variation of geometric imaging parameters. Future work and optimization efforts of 2D cine MR imaging for real-time adaptive radiotherapy is needed to characterize the influence of sequence- and anatomical site-specific imaging contrast.

Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging

PURPOSE

Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images.

ACQUISITION AND VALIDATION METHODS

Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model.

DATA FORMAT AND USAGE NOTES

The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR.

POTENTIAL APPLICATIONS

The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.

The risk and predictors for severe radiation pneumonitis in lung cancer patients treated with thoracic reirradiation

BACKGROUND

Thoracic reirradiation (re-RT) is increasingly administered. However, radiation pneumonitis (RP) remains to be the most common side effect from retreatment. This study aimed to determine the risk and predictors for severe RP in patients receiving thoracic re-RT.

METHODS

Sixty seven patients with lung cancer received thoracic re-RT for recurrent or metastatic disease. Three-dimensional conformal radiotherapy (3D-CRT)/intensity modulated radiotherapy (IMRT) was used for 60 patients, and stereotactic body radiation therapy (SBRT) was used in 7 patients. Deformable image registration (DIR) was performed to create a composite plan. Severe (grade ≥ 3) RP was graded according to Common Terminology Criteria for Adverse Events version 4.0.

RESULTS

Eighteen patients (26.9%) developed grade ≥ 3 RP (17 of grade 3, and 1 of grade 4). In univariate analyses, V5 and mean lung dose (MLD) of initial RT or re-RT plans, V5 and V20 of composite plans, and the overlap between V5 of initial RT and V5 of re-RT plans/V5 of re-RT plans (overlap-V5/re-V5) were significantly associated with grade ≥ 3 RP (P < 0.05 for each comparison). Multivariate analysis revealed that MLD of the initial RT plans (HR = 14.515, 95%CI:1.778-118.494, P = 0.013), V5 of the composite plans (HR = 7.398, 95%CI:1.319-41.495, P = 0.023), and overlap-V5/re-V5 (P = 0.041) were independent predictors for grade ≥ 3 RP. Out-of-field failures with medium overlap-V5/re-V5 of 0.4-0.8 was associated with higher risk of grade ≥ 3 RP compared with in-field failures (18.3% vs. 50%, P = 0.014).

CONCLUSIONS

The risk of grade ≥ 3 RP could be predicted not only by dose-volume variables from re-RT plan, but also by some from initial-RT and composite plans. Out-of-field failures was associated with higher risk of severe RP compared with in-field failures in some cases.

Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis

Purpose

Validating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic‐MRI and radiation treatment planning‐CT by utilizing a planning‐MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain.

Methods

For thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic‐MRI) and the second on the same day as the planning‐CT (planning‐MRI). The diagnostic‐MRI was deformed to the planning‐CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning‐MRI provided an independent surrogate for the planning‐CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas.

Results

The planning‐MRI provided an excellent surrogate for the planning‐CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013).

Conclusion

By utilizing the planning‐MRI as a surrogate for the planning‐CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.

Fast elastic registration of soft tissues under large deformations

A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance.

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D _0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

PURPOSE

Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error.

METHODS

The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error.

RESULTS

The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images.

CONCLUSIONS

The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

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

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Comparative evaluation of target volumes defined by deformable and rigid registration of diagnostic PET/CT to planning CT in primary esophageal cancer

BACKGROUND

To evaluate the geometrical differences of target volumes propagated by deformable image registration (DIR) and rigid image registration (RIR) to assist target volume delineation between diagnostic Positron emission tomography/computed tomography (PET/CT) and planning CT for primary esophageal cancer (EC).

METHODS

Twenty-five patients with EC sequentially underwent a diagnostic F-fluorodeoxyglucose (F-FDG) PET/CT scan and planning CT simulation. Only 19 patients with maximum standardized uptake value (SUVmax) ≥ 2.0 of the primary volume were available. Gross tumor volumes (GTVs) were delineated using CT and PET display settings. The PET/CT images were then registered with planning CT using MIM software. Subsequently, the PET and CT contours were propagated by RIR and DIR to planning CT. The properties of these volumes were compared.

RESULTS

When GTVCT delineated on CT of PET/CT after both RIR and DIR was compared with GTV contoured on planning CT, significant improvements using DIR were observed in the volume, displacements of the center of mass (COM) in the 3-dimensional (3D) direction, and Dice similarity coefficient (DSC) (P = 0.003; 0.006; 0.014). Although similar improvements were not observed for the same comparison using DIR for propagated PET contours from diagnostic PET/CT to planning CT (P > 0.05), for DSC and displacements of COM in the 3D direction of PET contours, the DIR resulted in the improved volume of a large percentage of patients (73.7%; 68.45%; 63.2%) compared with RIR. For diagnostic CT-based contours or PET contours at SUV2.5 propagated by DIR with planning CT, the DSC and displacements of COM in 3D directions in the distal segment were significantly improved compared to the upper and middle segments (P > 0.05).

CONCLUSION

We observed a trend that deformable registration might improve the overlap for gross target volumes from diagnostic PET/CT to planning CT. The distal EC might benefit more from DIR.

The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu's method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

Lung stereotactic body radiotherapy with an MR-linac - Quantifying the impact of the magnetic field and real-time tumor tracking

BACKGROUND AND PURPOSE

There are concerns that radiotherapy doses delivered in a magnetic field might be distorted due to the Lorentz force deflecting secondary electrons. This study investigates this effect on lung stereotactic body radiotherapy (SBRT) treatments, conducted either with or without multileaf collimator (MLC) tumor tracking.

MATERIAL AND METHODS

Lung SBRT treatments with an MR-linac were simulated for nine patients. Two different treatment techniques were compared: conventional, non-tracked deliveries and deliveries with real-time MLC tumor tracking, each conducted either with or without a 1.5T magnetic field.

RESULTS

Slight dose distortions at air-tissue-interfaces were observed in the presence of the magnetic field. Most prominently, the dose to 2% of the skin increased by 1.4Gy on average. Regardless of the presence of the magnetic field, MLC tracking was able to spare healthy tissue, for example by decreasing the mean lung dose by 0.3Gy on average, while maintaining the target dose.

CONCLUSIONS

Accounting for the magnetic field during treatment plan optimization allowed for design and delivery of clinically acceptable lung SBRT treatments with an MR-linac. Furthermore, the ability of MLC tumor tracking to decrease dose exposure of healthy tissue, was not inhibited by the magnetic field.

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy - how should we evaluate and interpret the result?

The purpose of this study is to evaluate the performance variations in commercial deformable image registration (DIR) tools for adaptive radiation therapy and further to interpret the differences using clinically available terms. Three clinical examples (prostate, head and neck (HN), and cranial spinal irradiation (CSI) with L‐spine boost) were evaluated in this study. Firstly, computerized deformed CT images were generated using simulation QA software with virtual deformations of bladder filling (prostate), neck flexion/bite‐block repositioning/tumor shrinkage (HN), and vertebral body rotation (CSI). The corresponding transformation matrices served as a “reference” for the following comparisons. Three commercialized DIR algorithms: the free‐form deformation from MIMVista 5.5 and the RegRefine from MIMMaestro 6.0, the multipass B‐spline from VelocityAI v3.0.1, and the adaptive demons from OnQ rts 2.1.15, were applied between the initial images and the deformed CT sets. The generated adaptive contours and dose distributions were compared with the “reference” and among each other. The performance in transferring contours was comparable among all three tools with an average Dice similarity coefficient of 0.81 for all the organs. However, the dose warping accuracy appeared to rely on the evaluation end points and methodologies. Point‐dose differences could show a difference of up to 23.3 Gy inside the PTVs and to overestimate up to 13.2 Gy for OARs, which was substantial for a 72 Gy prescription dose. Dosevolume histogram‐based evaluation might not be sensitive enough to illustrate all the detailed variations, while isodose assessment on a slice‐by‐slice basis could be tedious. We further explored the possibility of using 3D gamma index analysis for warping dose variation assessment, and observed differences in dose warping using different DIR tools. Overall, our results demonstrated that evaluation based only on the performance of contour transformation could not guarantee the accuracy in dose warping, while dose‐transferring validation strongly relied on the evaluation endpoint. As dose‐transferring errors could cause misinterpretations when attempting to accumulate dose for adaptive radiation therapy and more DIR tools are available for clinical use, a standard and clinically meaningful quality assurance criterion should be established for DIR QA in the near future.

PACS number(s): 87.57

A non-rigid point matching method with local topology preservation for accurate bladder dose summation in high dose rate cervical brachytherapy

GEC-ESTRO guidelines for high dose rate cervical brachytherapy advocate the reporting of the D2cc (the minimum dose received by the maximally exposed 2cc volume) to organs at risk. Due to large interfractional organ motion, reporting of accurate cumulative D2cc over a multifractional course is a non-trivial task requiring deformable image registration and deformable dose summation. To efficiently and accurately describe the point-to-point correspondence of the bladder wall over all treatment fractions while preserving local topologies, we propose a novel graphic processing unit (GPU)-based non-rigid point matching algorithm. This is achieved by introducing local anatomic information into the iterative update of correspondence matrix computation in the 'thin plate splines-robust point matching' (TPS-RPM) scheme. The performance of the GPU-based TPS-RPM with local topology preservation algorithm (TPS-RPM-LTP) was evaluated using four numerically simulated synthetic bladders having known deformations, a custom-made porcine bladder phantom embedded with twenty one fiducial markers, and 29 fractional computed tomography (CT) images from seven cervical cancer patients. Results show that TPS-RPM-LTP achieved excellent geometric accuracy with landmark residual distance error (RDE) of 0.7  ±  0.3 mm for the numerical synthetic data with different scales of bladder deformation and structure complexity, and 3.7  ±  1.8 mm and 1.6  ±  0.8 mm for the porcine bladder phantom with large and small deformation, respectively. The RDE accuracy of the urethral orifice landmarks in patient bladders was 3.7  ±  2.1 mm. When compared to the original TPS-RPM, the TPS-RPM-LTP improved landmark matching by reducing landmark RDE by 50  ±  19%, 37  ±  11% and 28  ±  11% for the synthetic, porcine phantom and the patient bladders, respectively. This was achieved with a computational time of less than 15 s in all cases with GPU acceleration. The efficiency and accuracy shown with the TPS-RPM-LTP indicate that it is a practical and promising tool for bladder dose summation in adaptive cervical cancer brachytherapy.

A deformable head and neck phantom with in-vivo dosimetry for adaptive radiotherapy quality assurance

PURPOSE

Patients' interfractional anatomic changes can compromise the initial treatment plan quality. To overcome this issue, adaptive radiotherapy (ART) has been introduced. Deformable image registration (DIR) is an important tool for ART and several deformable phantoms have been built to evaluate the algorithms' accuracy. However, there is a lack of deformable phantoms that can also provide dosimetric information to verify the accuracy of the whole ART process. The goal of this work is to design and construct a deformable head and neck (HN) ART quality assurance (QA) phantom with in vivo dosimetry.

METHODS

An axial slice of a HN patient is taken as a model for the phantom construction. Six anatomic materials are considered, with HU numbers similar to a real patient. A filled balloon inside the phantom tissue is inserted to simulate tumor. Deflation of the balloon simulates tumor shrinkage. Nonradiopaque surface markers, which do not influence DIR algorithms, provide the deformation ground truth. Fixed and movable holders are built in the phantom to hold a diode for dosimetric measurements.

RESULTS

The measured deformations at the surface marker positions can be compared with deformations calculated by a DIR algorithm to evaluate its accuracy. In this study, the authors selected a Demons algorithm as a DIR algorithm example for demonstration purposes. The average error magnitude is 2.1 mm. The point dose measurements from the in vivo diode dosimeters show a good agreement with the calculated doses from the treatment planning system with a maximum difference of 3.1% of prescription dose, when the treatment plans are delivered to the phantom with original or deformed geometry.

CONCLUSIONS

In this study, the authors have presented the functionality of this deformable HN phantom for testing the accuracy of DIR algorithms and verifying the ART dosimetric accuracy. The authors' experiments demonstrate the feasibility of this phantom serving as an end-to-end ART QA phantom.

Dosimetric variations due to interfraction organ deformation in cervical cancer brachytherapy

We quantitatively estimated dosimetric variations due to interfraction organ deformation in multi-fractionated high-dose-rate brachytherapy (HDRBT) for cervical cancer using a novel surface-based non-rigid deformable registration. As the number of consecutive HDRBT fractions increased, simple addition of dose-volume histogram parameters significantly overestimated the dose, compared with distribution-based dose addition.

A Feasibility Study of Tumor Motion Estimate With Regional Deformable Registration Method for 4-Dimensional Radiation Therapy of Lung Cancer

This study aims to employ 4-dimensional computed tomography to quantify intrafractional tumor motion for patients with lung cancer to improve target localization in radiation therapy. A multistage regional deformable registration was implemented to calculate the excursion of gross tumor volume (GTV) during a breathing cycle. GTV was initially delineated on 0% phase of 4-dimensional computed tomography manually, and a subregion with 20 mm margin supplemented to GTV was generated with Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California). The structures, together with the 4-dimensional computed tomography set, were exported into an in-house software, with which a 3-stage B-spline deformable registration was carried out to map the subregion and warp GTV contour to other breathing phases. The center of mass of the GTV was computed using the contours, and the tumor motion was appraised as the excursion of the center of mass between 0% phase and other phases. Application of the algorithm to the 10 patients showed that clinically satisfactory outcomes were achievable with a spatial accuracy around 2 mm for GTV contour propagation between adjacent phases and 3 mm between opposite phases. The tumor excursion was determined in the vast range of 1 mm through 1.6 cm, depending on the tumor location and tumor size. Compared to the traditional whole image-based registration, the regional method was found computationally a factor of 5 more efficient. The proposed technique has demonstrated its capability in extracting thoracic tumor motion and should find its application in 4-dimensional radiation therapy in the future to maximally utilize the available spatial-temporal information.

Comparison of adverse effects of proton and X-ray chemoradiotherapy for esophageal cancer using an adaptive dose-volume histogram analysis

Cardiopulmonary late toxicity is of concern in concurrent chemoradiotherapy (CCRT) for esophageal cancer. The aim of this study was to examine the benefit of proton beam therapy (PBT) using clinical data and adaptive dose-volume histogram (DVH) analysis. The subjects were 44 patients with esophageal cancer who underwent definitive CCRT using X-rays (n = 19) or protons (n = 25). Experimental recalculation using protons was performed for the patient actually treated with X-rays, and vice versa. Target coverage and dose constraints of normal tissues were conserved. Lung V5-V20, mean lung dose (MLD), and heart V30-V50 were compared for risk organ doses between experimental plans and actual treatment plans. Potential toxicity was estimated using protons in patients actually treated with X-rays, and vice versa. Pulmonary events of Grade ≥2 occurred in 8/44 cases (18%), and cardiac events were seen in 11 cases (25%). Risk organ doses in patients with events of Grade ≥2 were significantly higher than for those with events of Grade ≤1. Risk organ doses were lower in proton plans compared with X-ray plans. All patients suffering toxicity who were treated with X-rays (n = 13) had reduced predicted doses in lung and heart using protons, while doses in all patients treated with protons (n = 24) with toxicity of Grade ≤1 had worsened predicted toxicity with X-rays. Analysis of normal tissue complication probability showed a potential reduction in toxicity by using proton beams. Irradiation dose, volume and adverse effects on the heart and lung can be reduced using protons. Thus, PBT is a promising treatment modality for the management of esophageal cancer.

Impact of treatment planning with deformable image registration on dose distribution for carbon-ion beam lung treatment using a fixed irradiation port and rotating couch

OBJECTIVE

When using a fixed irradiation port, treatment couch rotation is necessary to increase beam angle selection. We evaluated dose variations associated with positional morphological changes to organs.

METHODS

We retrospectively chose the data sets of ten patients with lung cancer who underwent respiratory-gated CT at three different couch rotation angles (0°, 20° and -20°). The respective CT data sets are referred to as CT0, CT20 and CT-20. Three treatment plans were generated as follows: in Plan 1, all compensating bolus designs and dose distributions were calculated using CT0. To evaluate the rotation effect without considering morphology changes, in Plan 2, the compensating boli designed using CT0 were applied to the CT±20 images. Plan 3 involved compensating boli designed using the CT±20 images. The accumulated dose distributions were calculated using deformable image registration (DIR).

RESULTS

A sufficient prescribed dose was calculated for the planning target volume (PTV) in Plan 1 [minimum dose received by a volume ≥95% (D95) > 95.8%]. By contrast, Plan 2 showed degraded dose conformation to the PTV (D95 > 90%) owing to mismatch of the bolus design to the morphological positional changes in the respective CT. The dose assessment results of Plan 3 were very close to those of Plan 1.

CONCLUSION

Dose distribution is significantly affected by whether or not positional organ morphology changes are factored into dose planning.

ADVANCES IN KNOWLEDGE

In treatment planning using multiple CT scans with different couch positions, it is mandatory to calculate the accumulated dose using DIR.

A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies

BACKGROUND

Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs.

METHODS

A voxel-by-voxel comparison of three DIR algorithms applied to lung patients is presented. Registrations are compared by comparing volume histograms formed both with individual DIR maps and with a voxel-by-voxel subtraction of the two maps. When two DIR maps agree one concludes that both maps are interchangeable in treatment planning applications, though one cannot conclude that either one agrees with the ground truth. If two DIR maps significantly disagree one concludes that at least one of the maps deviates from the ground truth. We use the method to compare 3 DIR algorithms applied to peak inhale-peak exhale registrations of 4DFBCT data obtained from 13 patients.

RESULTS

All three algorithms appear to be nearly equivalent when compared using DICE similarity coefficients. A comparison based on Jacobian volume histograms shows that all three algorithms measure changes in total volume of the lungs with reasonable accuracy, but show large differences in the variance of Jacobian distribution on contoured structures. Analysis of voxel-by-voxel subtraction of DIR maps shows differences between algorithms that exceed a centimeter for some registrations.

CONCLUSION

Deformation maps produced by DIR algorithms must be treated as mathematical approximations of physical tissue deformation that are not self-consistent and may thus be useful only in applications for which they have been specifically validated. The three algorithms tested in this work perform fairly robustly for the task of contour propagation, but produce potentially unreliable results for the task of DVH accumulation or measurement of local volume change. Performance of DIR algorithms varies significantly from one image pair to the next hence validation efforts, which are exhaustive but performed on a small number of image pairs may not reflect the performance of the same algorithm in practical clinical situations. Such efforts should be supplemented by validation based on a longer series of images of clinical quality.

Volume and landmark analysis: comparison of MRI measurements obtained with an endorectal coil and with a phased-array coil

AIM

To compare prostate volumes and distances between anatomical landmarks on MRI images obtained with a phased-array coil (PAC) only and with a PAC and an endorectal coil (ERC).

MATERIALS AND METHODS

Informed consent was waived for this Health Insurance Portability and Accountability Act-compliant study. Fifty-nine men underwent PAC-MRI and ERC-MRI at 1.5 (n = 3) or 3 T (n = 56). On MRI images, two radiologists independently measured prostate volume and distances between the anterior rectal wall (ARW) and symphysis pubis at the level of the verumontanum; ARW and symphysis pubis at the level of the mid-symphysis pubis; and bladder neck and mid-symphysis pubis. Differences between measurements from PAC-MRI and ERC-MRI were assessed with the Wilcoxon RANK SUM test. Inter-reader agreement was assessed using the concordance correlation coefficient (CCC).

RESULTS

Differences in prostate volume between PAC-MRI and ERC-MRI [median: -0.75 mm(3) (p = 0.10) and median: -0.84 mm(3) (p = 0.06) for readers 1 and 2, respectively] were not significant. For readers 1 and 2, median differences between distances were as follows: -10.20 and -12.75 mm, respectively, ARW to symphysis pubis at the level of the verumontanum; -6.60 and -6.08 mm, respectively, ARW to symphysis pubis at the level of the mid-symphysis pubis; -3 and -3 mm respectively, bladder neck to mid-symphysis pubis. All differences in distance were significant for both readers (p ≤ 0.0005). Distances were larger on PAC-MRI (p ≤ 0.0005). Inter-reader agreement regarding prostate volume was almost perfect on PAC-MRI (CCC: 0.99; 95% CI: 0.98-1.00) and ERC-MRI (CCC: 0.99; 95% CI: 0.99-1.00); inter-reader agreement for distance measurements varied (CCCs: 0.54-0.86).

CONCLUSION

Measurements of distances between anatomical landmarks differed significantly between ERC-MRI and PAC-MRI, although prostate volume measurements did not.

Whole prostate volume and shape changes with the use of an inflatable and flexible endorectal coil

Purpose. To determine to what extent an inflatable endorectal coil (ERC) affects whole prostate (WP) volume and shape during prostate MRI. Materials and Methods. 79 consecutive patients underwent T2W MRI at 3T first with a 6-channel surface coil and then with the combination of a 16-channel surface coil and ERC in the same imaging session. WP volume was assessed by manually contouring the prostate in each T2W axial slice. PSA density was also calculated. The maximum anterior-posterior (AP), left-right (LR), and craniocaudal (CC) prostate dimensions were measured. Changes in WP prostate volume, PSA density, and prostate dimensions were then evaluated. Results. In 79 patients, use of an ERC yielded no significant change in whole prostate volume (0.6 ± 5.7%, P = 0.270) and PSA density (−0.2 ± 5.6%, P = 0.768). However, use of an ERC significantly decreased the AP dimension of the prostate by −8.6 ± 7.8% (P < 0.001), increased LR dimension by 4.5 ± 5.8% (P < 0.001), and increased the CC dimension by 8.8 ± 6.9% (P < 0.001). Conclusion. Use of an ERC in prostate MRI results in the shape deformation of the prostate gland with no significant change in the volume of the prostate measured on T2W MRI. Therefore, WP volumes calculated on ERC MRI can be reliably used in clinical workflow.

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry

OBJECTIVES

To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry.

METHODS

The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR.Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD.

RESULTS

In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values < 1.0 mm were observed with a BKS between 10-20 mm for image contrast ≥ ± 250 HU and noise < ± 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD < 1.0 mm.

CONCLUSIONS

For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels < ± 100 HU) will not be effected by the amount of image noise.

The effectiveness of gadolinium MRI to improve target delineation for radiotherapy in hepatocellular carcinoma: a comparative study of rigid image registration techniques

To achieve consistent target delineation in radiotherapy for hepatocellular carcinoma (HCC), image registration between simulation CT and diagnostic MRI was explored. Twenty patients with advanced HCC were included. The median interval between MRI and CT was 11 days. CT was obtained with shallow free breathing and MRI at exhale phase. On each CT and MRI, the liver and the gross target volume (GTV) were drawn. A rigid image registration was taken according to point information of vascular bifurcation (Method[A]) and pixel information of volume of interest only including the periphery of the liver (Method[B]) and manually drawn liver (Method[C]). In nine cases with an indefinite GTV on CT, a virtual sphere was generated at the epicenter of the GTV. The GTV from CT (VGTV[CT]) and MRI (VGTV[MR]) and the expanded GTV from MRI (V+GTV[MR]) considering geometrical registration error were defined. The underestimation (uncovered V[CT] by V[MR]) and the overestimation (excessive V[MR] by V[CT]) were calculated. Through a paired T-test, the difference between image registration techniques was analyzed. For method[A], the underestimation rates of VGTV[MR] and V+GTV[MR] were 16.4 ± 8.9% and 3.2 ± 3.7%, and the overestimation rates were 16.6 ± 8.7% and 28.4 ± 10.3%, respectively. For VGTV[MR] and V+GTV[MR], the underestimation rates and overestimation rates of method[A] were better than method[C]. The underestimation rates and overestimation rates of the VGTV[MR] were better in method[B] than method[C]. By image registration and additional margin, about 97% of HCC could be covered. Method[A] or method[B] could be recommended according to physician preference.

Registration of in vivo prostate MRI and pseudo-whole mount histology using Local Affine Transformations guided by Internal Structures (LATIS)

PURPOSE

To present a novel registration approach called LATIS (Local Affine Transformation guided by Internal Structures) for coregistering post prostatectomy pseudo-whole mount (PWM) pathological sections with in vivo MRI (magnetic resonance imaging) images.

MATERIALS AND METHODS

Thirty-five patients with biopsy-proven prostate cancer were imaged at 3T with an endorectal coil. Excised prostate specimens underwent quarter mount step-section pathologic processing, digitization, annotation, and assembly into a PWM. Manually annotated macro-structures on both pathology and MRI were used to assist registration using a relaxed local affine transformation approximation. Registration accuracy was assessed by calculation of the Dice similarity coefficient (DSC) between transformed and target capsule masks and least-square distance between transformed and target landmark positions.

RESULTS

LATIS registration resulted in a DSC value of 0.991 ± 0.004 and registration accuracy of 1.54 ± 0.64 mm based on identified landmarks common to both datasets. Image registration performed without the use of internal structures led to an 87% increase in landmark-based registration error. Derived transformation matrices were used to map regions of pathologically defined disease to MRI.

CONCLUSION

LATIS was used to successfully coregister digital pathology with in vivo MRI to facilitate improved correlative studies between pathologically identified features of prostate cancer and multiparametric MRI.

Evaluation of image registration spatial accuracy using a Bayesian hierarchical model

To evaluate the utility of automated deformable image registration (DIR) algorithms, it is necessary to evaluate both the registration accuracy of the DIR algorithm itself, as well as the registration accuracy of the human readers from whom the "gold standard" is obtained. We propose a Bayesian hierarchical model to evaluate the spatial accuracy of human readers and automatic DIR methods based on multiple image registration data generated by human readers and automatic DIR methods. To fully account for the locations of landmarks in all images, we treat the true locations of landmarks as latent variables and impose a hierarchical structure on the magnitude of registration errors observed across image pairs. DIR registration errors are modeled using Gaussian processes with reference prior densities on prior parameters that determine the associated covariance matrices. We develop a Gibbs sampling algorithm to efficiently fit our models to high-dimensional data, and apply the proposed method to analyze an image dataset obtained from a 4D thoracic CT study.

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach

PURPOSE

Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy.

METHODS

The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process.

RESULTS

The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed.

CONCLUSIONS

The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.