Automatic error correction using adaptive weighting for vessel-based deformable image registration.

PurposeKilovoltage cone beam computed tomography (kVCBCT)‐guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART.MethodsWe evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity‐based deformable registration, (2) hybrid deformable registration, and (3) a two‐step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity‐based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal‐to‐noise ratio (SNR), uniformity, and contrast‐to‐noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal‐to‐noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose‐volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference.ResultsThe dCT images generated by the hybrid (dCTH) and two‐step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two‐step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12 mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9 mm and average DSC of 0.902. The DVH showed that the dCTC‐based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC‐based treatment plans were >95% at all points, whereas they were <95% for kVCBCT‐based treatment plans.ConclusionThe two‐step registration method outperforms the intensity‐based and hybrid registration methods. While the hybrid and two‐step‐based methods improved the image quality of kVCBCT in a linear accelerator, only the two‐step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two‐step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. The CLD in the entire prostatic urethra was 2.09±0.89mm (our proposed method), 2.77±0.99mm (Acosta et al., P=0.022), and 3.47±1.19mm (Waterman et al., P<0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49±1.78mm (our proposed method), 2.95±1.75mm (Acosta et al., P=0.42), and 5.76±3.09mm (Waterman et al., P<0.001). We developed a DIR accuracy prediction model-based multiatlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.

The aim of this study is to evaluate anatomic lung tumor changes and dosimetric consequences utilizing the deformable daily kilovolt (KV) cone-beam computer tomography (CBCT) image registration. Five patients diagnosed with NSCLC were treated with three-dimensional conformal radiotherapy (3D CRT) and 10 daily KV CBCT image sets were acquired for each patient. Each CBCT image and plan CT were imported into the deformable image registration (DIR) system. The plan CT image was deformed by the DIR system and a new contour on CBCT was obtained by using the auto-contouring function of the DIR. These contours were individually marked as CBCT f1, CBCT f2,..., and CBCT f10, and imported into a treatment planning system (TPS). The daily CBCT plan was individually generated with the same planning criteria based on new contours. These plans were individually marked as CBCTp1, CBCTp2,..., and CBCTp10, followed by generating a dose accumulation plan (DA plan) in original pCT image contour sets by adding all CBCT plans using Varian Eclipse TPS. The maximum, minimum and mean doses to the plan target volume (PTV) in the 5 DA plans were the same with the CT plans. However, the volume of radiation 5, 10, 20, 30, and 50 Gy of the total lungs in DA plans were less than those of the CT plans. The maximum dose of the spinal cord in the DA plans were average 27.96% less than the CT plans. The mean dose for the left, right, and total lungs in the DA plans were reduced by 13.80%, 23.65%, and 12.96%, respectively. The adaptive 3D CRT based on the deformable registration can reduce the dose to the lung and the spinal cord with the same PTV dose coverage. Moreover, it provides a method for further adaptive radiotherapy exploration.

Fast, accurate, deformable image registration is an important aspect of image-guided interventions. Among the factors that can confound registration is the presence of additional material in the intraoperative image - e.g., contrast bolus or a surgical implant - that was not present in the prior image. Existing deformable registration methods generally fail to account for tissue excised between image acquisitions and typically simply "move" voxels within the images with no ability to account for tissue that is removed or introduced between scans. We present a variant of the Demons algorithm to accommodate such content mismatch. The approach combines segmentation of mismatched content with deformable registration featuring an extra pseudo-spatial dimension representing a reservoir from which material can be drawn into the registered image. Previous work tested the registration method in the presence of tissue excision ("missing tissue"). The current paper tests the method in the presence of additional material in the target image and presents a general method by which either missing or additional material can be accommodated. The method was tested in phantom studies, simulations, and cadaver models in the context of intraoperative cone-beam CT with three examples of content mismatch: a variable-diameter bolus (contrast injection); surgical device (rod), and additional material (bone cement). Registration accuracy was assessed in terms of difference images and normalized cross correlation (NCC). We identify the difficulties that traditional registration algorithms encounter when faced with content mismatch and evaluate the ability of the proposed method to overcome these challenges.

Landmark-guided deformable image registration for accurate cumulative dose evaluation in lung reirradiation.

To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT, which might reduce registration accuracy. We have investigated the effect of image quality differences on the deformable registration of fan-beam CTs and CTs with simulated cone-beam noise. Our study used three CT studies for each of five prostate patients. Each CT was contoured by three experienced radiation oncologists. For each patient, one CT was designated the source image and the other two were target images. A deformable image registration process was used to register each source CT to each target CT and then transfer the manually drawn treatment planning contours from the source CT to the target CTs. The accuracy of the automatically transferred contours (and thus of the deformable registration process) was assessed by comparing them to the manual contours on the target CTs, with the differences evaluated with respect to interobserver variability in the manual contours. Then each of the target CTs was modified to include increased noise characteristic of cone-beam CT and the tests were repeated. Changes in registration accuracy due to increased noise were detected by monitoring changes in the automatically transferred contours. We found that the additional noise caused no significant loss of registration accuracy at magnitudes that exceeded what would normally be found in an actual cone-beam CT. We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy.

Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple registration steps were iterated with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime. However, existing NICE registration methods mainly focus on deformable registration, while affine registration, a common prerequisite, is still reliant on time-consuming traditional optimization-based methods or extra affine registration networks. In addition, existing NICE registration methods are limited by the intrinsic locality of convolution operations. Transformers may address this limitation for their capabilities to capture long-range dependency, but the benefits of using transformers for NICE registration have not been explored. In this study, we propose a Non-Iterative Coarse-to-finE Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the first deep registration method that (i) performs joint affine and deformable coarse-to-fine registration within a single network, and (ii) embeds transformers into a NICE registration framework to model long-range relevance between images. Extensive experiments with seven public datasets show that our NICE-Trans outperforms stateof-the-art registration methods on both registration accuracy and runtime.

Tissue-specific deformable image registration using a spatial-contextual filter.

Deformable image registration is currently predominantly solved by optimizing a weighted linear combination of objectives. Successfully tuning the weights associated with these objectives is not trivial, leading to trial-and-error approaches. Such an approach assumes an intuitive interplay between weights, optimization objectives, and target registration errors. However, it is not known whether this always holds for existing registration methods. To investigate the interplay between weights, optimization objectives, and registration errors, we employ multi-objective optimization. Here, objectives of interest are optimized simultaneously, causing a set of multiple optimal solutions to exist, called the optimal Pareto front. Our medical application is in breast cancer and includes the challenging prone-supine registration problem. In total, we studied the interplay in three different ways. First, we ran many random linear combinations of objectives using the well-known registration software elastix. Second, since the optimization algorithms used in registration are typically of a local-search nature, final solutions may not always form a Pareto front. We therefore employed a multi-objective evolutionary algorithm that finds weights that correspond to registration outcomes that do form a Pareto front. Third, we examined how the interplay differs if a true multi-objective (i.e., weight-free) image registration method is used. Results indicate that a trial-and-error weight-adaptation approach can be successful for the easy prone to prone breast image registration case, due to the absence of many local optima. With increasing problem difficulty the use of more advanced approaches can be of value in finding and selecting the optimal registration outcomes.

Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems. TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results. The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt® and Velocity™. The DIR algorithms of SmartAdapt® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.

Thoracic aortic aneurysm (TAA) growth is currently assessed by changes in maximal aortic diameter (i.e., radial growth of the vessel). However, there is growing awareness that longitudinal aortic growth (i.e., elongation) is an important metric of disease status, albeit one that is difficult to measure using current clinical techniques. Previously we have proposed a method to assess 3D changes in aortic wall growth/deformation using deformable image registration with interpolation of the spatial Jacobian determinant to the aortic surface. Here we propose a method to re-orient the Jacobian into directional components relative to the aortic surface rather than the image space, allowing clinicians and researchers to isolate and study the pathologic effects of each directional component of growth separately. To this end, we first perform a deformable image registration between two aortic geometries. Second, we segment the aortic surface and centerline in the fixed image, and use the resulting geometry to construct anatomically-based local coordinate systems at each voxel of the aortic surface. Using the Jacobian matrix field resulting from the deformable registration, we can obtain the anatomically oriented Jacobian components by rotating the Jacobian matrix at each voxel so that it is aligned with the anatomically based local coordinate system. Through experiments on toy cylinders and real clinical cases, we show clear differences between the Jacobian determinant and its directional components, with the directional Jacobian component being able to remove one directional change (e.g., longitudinal) while maintaining the other (e.g., cross-sectional).

Background/Introduction Arterial stiffness is one of the earliest detectable markers of functional changes in the aortic wall and a powerful predictor of adverse events [1]. However, its current assessment requires significant expertise and effort and provides only global or two-dimensional stiffness descriptors, limiting the ability to evaluate local heterogeneity of aortic properties. As deformable image registration applied to serial computed tomography angiographies (CTA) provides accurate 3D aortic growth assessment [2,3], we hypothesize that it would offer reliable 3D aortic strain maps once applied to time-resolved CTA. Purpose To develop and validate an automated method for computing 3D aortic strain maps from four-dimensional (4D) CTA using deep learning (DL) pre-processing and deformable registration. Methods Twenty-nine patients with bicuspid aortic valve (70% male, aged 41 to 78 years) and available multiphase CTA acquisitions were included. Aortic segmentation and anatomical landmarks were automatically obtained at end-diastole via DL algorithms and tracked across the 4D CTA phases using deformable image registration. Additionally, manual expert annotations were obtained in 15 patients for comparison. From the time-resolved aortic meshes, ascending (AAo) and descending (DAo) aorta deformation maps (longitudinal and circumferential strain, and aortic surface relative change (SARC)) were computed. Maximum (systolic to diastolic) deformation was extracted and used for validation by comparing (i) automatic vs manual strain measurements (n=15), (ii) strain dependency with age (n=29), and cross-modality comparison with AAo circumferential (n=22) and longitudinal (n=18) strain by cardiac magnetic resonance. Results Excellent correlation was obtained between automatic and manual strain for all deformation parameters (R&amp;gt;0.95, p&amp;lt;0.0001). The well-known stiffening of the AAo and DAo (Figure 1) with age was obtained for all parameters and confirmed in the single patient with two available time-resolved CTAs acquired 6 years apart (Figure 2). In cross-modality validation with CMR, 4D CTA-based measurements showed good correlation with CMR for longitudinal strain in the AAo (R=0.608, p=0.007) and circumferential strain in the AAo (R=0.765, p&amp;lt;0.001) and DAo (R=0.747, p&amp;lt;0.001). Conclusion Accurate and reproducible 3D aortic strain maps can be obtained automatically from 4D CTA via DL and deformable image registration. This approach enables the local assessment of aortic stiffness, thus extending the possibilities in the study of the pathophysiology and prognosis of aortic diseases.Figure 1 Figure 2

Purpose: The purpose of this study is to investigate the effects of different patient positions on the PET and CTimage registration, and to delineate more accurate PET metabolic tumor volumes (MTVs) using a deformable image registration in lungcancer. When PET and CT systems are used as stand alone modalities, the patient arm positions are usually different. We used a deformable image registration method to fuse PET and CTimages with different patient positions. Method and Materials: PET and CTimages were acquired for 10 lungcancer patients. Both arms were placed at sides for PET imaging, but arms were up for CTimaging. The images were registered using an iterative non‐rigid algorithm (REVEAL‐MVS) based on voxel intensities. The displacements after the image registration were measured in the lung boundaries with axial and coronal images. The MTV and its mean standardized uptake value (SUV) in the PET images were measured before and after the non‐rigid image registration.Results: The displacements in the lung boundaries and diaphragm ranged from 1.85 to 3.89 cm for coronal images and from 1.57 to 2.42 cm for axial images. In 5 out of 10 cases the PET‐MTV became greater and the mean SUV became smaller after the non‐rigid image registration. The PET MTVs before and after the deformable image registration resulted in the volume differences of −1.8 cm3 to +29.3 cm3 and in the percent difference of −25% to ±41%. Conclusion: The different patient positions (arms‐down for PET; arms‐up for CT) in stand alone PET and CT systems yielded different PET tumor volume, extent and location. The non‐rigid image registration should be used to obtain more accurate PET MTV and its location. The PET MTV using a deformable registration method may be utilized for CT‐based radiation treatment planning in lungcancer.