Deformable Registration Approach Research Articles

Indirect deformable image registration using synthetic image generated by unsupervised deep learning

Background and purpose3D image registration is now common in many medical domains. Multimodal registration implies the use of different imaging modalities, which results in lower accuracy compared to monomodal registration. The aim of this study was to propose a novel approach for deformable image registration (DIR) that incorporates an unsupervised deep learning (DL)-based generation step. The objective was to reduce the challenge of multimodal registration to monomodal registration. Material and methodsTwo datasets from prostate radiotherapy patients were used to evaluate the proposed method. The first dataset consisted of Computed Tomography (CT)/ Cone Beam Computed Tomography (CBCT) pairs from 23 patients using different CBCT devices. The second dataset included Magnetic Resonance Imaging (MRI)/CT pairs from two different care centers, utilizing different MRI devices (0.35 T MRIdian MR-Linac, 1.5 T GE lightspeed MRI). Following a preprocessing step essential for ensuring DL synthesis accuracy and standardizing the database, synthetic CTs (sCTreg) were generated using an unsupervised conditional Generative Adversarial Network (cGAN). The generated sCTs from CBCT or MRI were then utilized for deformable registration with CT scans. This registration method was compared to three standard methods: rigid registration, Elastix registration based on BSplines, and VoxelMorph-based registration (applied exclusively to CBCT/CT). The endpoints of comparison were the dice coefficients calculated between delineated structures for both datasets. ResultsFor both datasets, intermediary sCT generation provided the highest dice coefficients. Dices reached 0.85, 0.85 and 0.75 for the prostate, bladder and rectum for the dataset 1 and 0.90, 0.95 and 0.87 respectively for the dataset 2. When the sCT were not used, dices reached 0.66, 0.78, 0.66 for the dataset 1 and 0.93, 0.87 and 0.84 for the dataset 2. Furthermore, the evaluation of the impact of registration on sCT generation showed that lower Mean Absolute Errors were obtained when the registration was conducted with a sCT. ConclusionsUsing unsupervised deep learning to synthesize intermediate sCT has led to improved registration accuracy in radiotherapy applications employing two distinct imaging modalities.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted -norm to regularize the deformation field instead of the traditional -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical -test results also proved that these improvements of our method have statisticalsignificance. In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

How the Rigid and Deformable Image Registration Approaches Affect the Absorbed Dose Estimation Using Images Collected before and after Transarterial Radioembolization with 90Y Resin Microspheres in a Clinical Setting

Background: Transarterial radioembolization (TARE) relies on directly injected 90Y- or 166Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE 99mTc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., DT and DNTL, respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on 90Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and DT/DNTL/DL were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson’s (PCC), concordance (CCCo) and Bland–Altman analyses. The Kaplan–Meier curves of overall survival (OS) were calculated according to DT. Results: All computed CCs indicated very good (&gt;0.92) agreement for volume comparison, while they suggested good (ICC ≥ 0.869, PCC ≥ 0.876 and CCCo ≥ 0.790) and moderate agreement in the intra- and inter-modality DT/DNTL/DL comparisons, respectively. Bland–Altman analyses showed percentage differences between the manual and deformable approaches of up to about −31%, 9% and 62% for tumoral volumes, DT and DNTL, respectively. The overall survival analysis showed statistically significant differences using DT cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusions: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic DT/DNTL constraints.

Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography.

Accurate and reproducible tissue identification is essential for understanding structural and functional changes that may occur naturally with aging, or because of a chronic disease, or in response to intervention therapies. Peripheral quantitative computed tomography (pQCT) is regularly employed for body composition studies, especially for the structural and material properties of the bone. Furthermore, pQCT acquisition requires low radiation dose and the scanner is compact and portable. However, pQCT scans have limited spatial resolution and moderate SNR. pQCT image quality is frequently degraded by involuntary subject movement during image acquisition. These limitations may often compromise the accuracy of tissue quantification, and emphasize the need for automated and robust quantification methods. We propose a tissue identification and quantification methodology that addresses image quality limitations and artifacts, with increased interest in subject movement. We introduce a multi-atlas image segmentation (MAIS) framework for semantic segmentation of hard and soft tissues in pQCT scans at multiple levels of the lower leg. We describe the stages of statistical atlas generation, deformable registration and multi-tissue classifier fusion. We evaluated the performance of our methodology using multiple deformable registration approaches against reference tissue masks. We also evaluated the performance of conventional model-based segmentation against the same reference data to facilitate comparisons. We studied the effect of subject movement on tissue segmentation quality. We also applied the top performing method to a larger out-of-sample dataset and report the quantification results. The results show that multi-atlas image segmentation with diffeomorphic deformation and probabilistic label fusion produces very good quality over all tissues, even for scans with significant quality degradation. The application of our technique to the larger dataset reveals trends of age-related body composition changes that are consistent with the literature. Because of its robustness to subject motion artifacts, our MAIS methodology enables analysis of larger number of scans than conventional state-of-the-art methods. Automated analysis of both soft and hard tissues in pQCT is another contribution of this work.

Deformable 3D-2D registration for high-precision guidance and verification of neuroelectrode placement

Purpose. Accurate neuroelectrode placement is essential to effective monitoring or stimulation of neurosurgery targets. This work presents and evaluates a method that combines deep learning and model-based deformable 3D-2D registration to guide and verify neuroelectrode placement using intraoperative imaging. Methods. The registration method consists of three stages: (1) detection of neuroelectrodes in a pair of fluoroscopy images using a deep learning approach; (2) determination of correspondence and initial 3D localization among neuroelectrode detections in the two projection images; and (3) deformable 3D-2D registration of neuroelectrodes according to a physical device model. The method was evaluated in phantom, cadaver, and clinical studies in terms of (a) the accuracy of neuroelectrode registration and (b) the quality of metal artifact reduction (MAR) in cone-beam CT (CBCT) in which the deformably registered neuroelectrode models are taken as input to the MAR. Results. The combined deep learning and model-based deformable 3D-2D registration approach achieved 0.2 ± 0.1 mm accuracy in cadaver studies and 0.6 ± 0.3 mm accuracy in clinical studies. The detection network and 3D correspondence provided initialization of 3D-2D registration within 2 mm, which facilitated end-to-end registration runtime within 10 s. Metal artifacts, quantified as the standard deviation in voxel values in tissue adjacent to neuroelectrodes, were reduced by 72% in phantom studies and by 60% in first clinical studies. Conclusions. The method combines the speed and generalizability of deep learning (for initialization) with the precision and reliability of physical model-based registration to achieve accurate deformable 3D-2D registration and MAR in functional neurosurgery. Accurate 3D-2D guidance from fluoroscopy could overcome limitations associated with deformation in conventional navigation, and improved MAR could improve CBCT verification of neuroelectrode placement.

Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.

Direct reconstruction of anatomical change in low-dose lung nodule surveillance.

Purpose: In sequential imaging studies, there exists rich information from past studies that can be used in prior-image-based reconstruction (PIBR) as a form of improved regularization to yield higher-quality images in subsequent studies. PIBR methods, such as reconstruction of difference (RoD), have demonstrated great improvements in the image quality of subsequent anatomy reconstruction even when CT data are acquired at very low-exposure settings. Approach: However, to effectively use information from past studies, two major elements are required: (1)registration, usually deformable, must be applied between the current and prior scans. Such registration is greatly complicated by potential ambiguity between patient motion and anatomical change-which is often the target of the followup study. (2)One must select regularization parameters for reliable and robust reconstruction of features. Results: We address these two major issues and apply a modified RoD framework to the clinical problem of lung nodule surveillance. Specifically, we develop a modified deformable registration approach that enforces a locally smooth/rigid registration around the change region and extend previous analytic expressions relating reconstructed contrast to the regularization parameter and other system dependencies for reliable representation of image features. We demonstrate the efficacy of this approach using a combination of realistic digital phantoms and clinical projection data. Performance is characterized as a function of the size of the locally smooth registration region of interest as well as x-ray exposure. Conclusions: This modified framework is effectively able to separate patient motion and anatomical change to directly highlight anatomical change in lung nodule surveillance.

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

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Abstract Intraoperative brain deformation, so-called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.

Tumour-Targeted Treatment Intensification for Prostate Cancer Using Magnetic Resonance Imaging Guidance

IntroductionLocal control in prostate cancer may be improved with targeted dose escalation to regions with high tumour burden. Multiparametric magnetic resonance imaging (mpMRI) combined with MRI-guided biopsies may aid in defining tumour-dense regions before radiation therapy. Deformable registration techniques may be used to map these tumour regions onto the radiotherapy planning MRI. Radiation therapy delivery techniques such as volumetric modulated arc therapy and high-dose-rate brachytherapy may allow for highly conformal dose escalation, and when coupled with image-guided radiation delivery (ie, cone beam computed technology and fiducial markers), may allow high-precision dose-escalated treatment. MethodsEligible prostate cancer patients were enrolled on a prospective trial of tumour dose escalation. Two strategies were investigated: (1) an integrated boost to external beam radiation for a total tumour dose of 95 Gy in 38 fractions or (2) a focal high-dose-rate boost of 10 Gy before 76 Gy in 38 fractions external beam radiation. Patients underwent MRI-guided biopsy with fiducial marker placement before therapy. mpMRI was acquired and used in conjunction with a non-endorectal coil T2 MRI and computed technology simulation images to define the gross tumour volume via a deformable registration approach for intraprostatic tumour dose escalation. ResultsA case example for each dose escalation strategy illustrates the tumour-targeted approach using MRI guidance. ConclusionsCombining mpMRI sequences with a deformable registration approach may aid in more accurate and reproducible definition of tumour-dense regions. This novel process coupled with daily image guidance may allow high-precision dose-escalated tumour-targeted radiotherapy for prostate cancer.

Thoracic CT-MRI coregistration for regional pulmonary structure-function measurements of obstructive lung disease.

Recent pulmonary imaging research has revealed that in patients with chronic obstructive pulmonary disease (COPD) and asthma, structural and functional abnormalities are spatially heterogeneous. This novel information may help optimize treatment in individual patients, monitor interventional efficacy, and develop new treatments. Moreover, by automating the measurement of regional biomarkers for the 19 different anatomical lung segments, there is an opportunity to embed imaging biomarkers into clinically acceptable clinical workflows and improve lung disease clinical care. Therefore, to exploit the regional structure-function information provided by thoracic imaging, and as a first step toward this goal, our objective was to develop a fully automated registration pipeline for thoracic x-ray computed tomography (CT) and inhaled gas functional magnetic resonance imaging (MRI) whole lung and segmental structure-function biomarkers. Thirty-five patients including 15 severe, poorly controlled asthmatics and 20 COPD patients [classified according to the global initiative for chronic obstructive lung disease (GOLD) criteria)] provided written informed consent to a study protocol approved by Health Canada and underwent pulmonary function tests, MRI, and CT during a single 2-hour visit. Using this diverse patient dataset, we developed and evaluated a joint deformable registration approach to simultaneously coregister CT with both 1 H and 3 He MRI by enforcing the similarity of the deformation fields from the two individual registrations. We derived a simpler model that was equivalent to the original challenging optimization problem through variational analysis and the simpler model gave rise to an efficient numerical solver that was parallelized on a graphics processing unit. The coregistered CT-3 He MRI and whole lung/segmental lung masks were used to generate whole lung and segmental 3 He MRI ventilation defect percent (VDP). To estimate fiducial localization reproducibility, a single observer manually identified 109 pairs of CT and 3 He MRI fiducials for 35 patient images on five separate occasions and determined the fiducial localization error (FLE). CT-3 He MRI registration accuracy was evaluated using the target registration error (TRE). Whole lung VDP generated using the algorithm was compared with VDP generated using a previously validated semiautomated approach and computational efficiency was evaluated using run time. In 35 patients including 15 with severe asthma and 20 with COPD, mean forced expiratory volume in 1s (FEV1 ) was 63±24%pred and FEV1 /forced vital capacity (FVC) was 54±17%. FLE was 0.16mm and 0.34mm for 3 He MRI and CT, respectively. TRE was 4.5±2.0mm, 4.0±1.7mm, 4.8±2.3mm for asthma, COPD GOLD II, and GOLD III groups, respectively, with a mean of 4.4±2.0mm for the entire dataset. TRE was significantly improved for joint CT-1 H/3 He MRI registration compared with CT-1 H MRI rigid registration (P<0.0001). Whole lung VDP generated using the pipeline was not significantly different (P=0.37) compared to a semiautomated method with which it was strongly correlated (r=0.93, P<0.0001). The fully automated pipeline required 11±0.4 min to generate whole lung and segmental VDP. For a diverse group of patients with COPD and asthma, whole lung and segmental VDP was measured using an automated lung image analysis pipeline which provides a way to incorporate lung functional biomarkers into clinical research and patient care.

Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT

We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L1 norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold σ. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.

Cardiorespiratory motion-compensated micro-CT image reconstruction using an artifact model-based motion estimation.

Cardiac in vivo micro-CT imaging of small animals typically requires double gating due to long scan times and high respiratory rates. The simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. In any case, for true 5D imaging, i.e., three spatial dimensions plus one respiratory-temporal dimension plus one cardiac temporal dimension, the amount of information corresponding to a given respiratory and cardiac phase is orders of magnitude lower than the total amount of information acquired. Achieving similar image quality for 5D than for usual 3D investigations would require increasing the amount of data and thus the applied dose to the animal. Therefore, the goal is phase-correlated imaging with high image quality but without increasing the dose level. To achieve this, the authors propose a new image reconstruction algorithm that makes use of all available projection data, also of that corresponding to other motion windows. In particular, the authors apply a motion-compensated image reconstruction approach that sequentially compensates for respiratory and cardiac motion to decrease the impact of sparsification. In that process, all projection data are used no matter which motion phase they were acquired in. Respiratory and cardiac motion are compensated for by using motion vector fields. These motion vector fields are estimated from initial phase-correlated reconstructions based on a deformable registration approach. To decrease the sensitivity of the registration to sparse-view artifacts, an artifact model-based approach is used including a cyclic consistent nonrigid registration algorithm. The preliminary results indicate that the authors' approach removes the sparse-view artifacts of conventional phase-correlated reconstructions while maintaining temporal resolution. In addition, it achieves noise levels and spatial resolution comparable to that of nongated reconstructions due to the improved dose usage. By using the proposed motion estimation, no sensitivity to streaking artifacts has been observed. Using sequential double gating combined with artifact model-based motion estimation allows to accurately estimate respiratory and cardiac motion from highly undersampled data. No sensitivity to streaking artifacts introduced by sparse angular sampling has been observed for the investigated dose levels. The motion-compensated image reconstruction was able to correct for both, respiratory and cardiac motion, by applying the estimated motion vector fields. The administered dose per animal can thus be reduced for 5D imaging allowing for longitudinal studies at the highest image quality.

Three-Dimensional Nonrigid MR-TRUS Registration Using Dual Optimization.

In this study, we proposed an efficient nonrigid magnetic resonance (MR) to transrectal ultrasound (TRUS) deformable registration method in order to improve the accuracy of targeting suspicious regions during a three dimensional (3-D) TRUS guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighborhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization-based algorithmic scheme was introduced to extract the deformations and align the two MIND descriptors. The registration accuracy was evaluated using 20 patient images by calculating the TRE using manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone. Additional performance metrics [Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD)] were also calculated by comparing the MR and TRUS manually segmented prostate surfaces in the registered images. Experimental results showed that the proposed method yielded an overall median TRE of 1.76 mm. The results obtained in terms of DSC showed an average of 80.8±7.8% for the apex of the prostate, 92.0±3.4% for the mid-gland, 81.7±6.4% for the base and 85.7±4.7% for the whole gland. The surface distance calculations showed an overall average of 1.84±0.52 mm for MAD and 6.90±2.07 mm for MAXD.

Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery.

Intraoperative cone-beam CT (CBCT) could offer an important advance to thoracic surgeons in directly localizing subpalpable nodules during surgery. An image-guidance system is under development using mobile C-arm CBCT to directly localize tumors in the OR, potentially reducing the cost and logistical burden of conventional preoperative localization and facilitating safer surgery by visualizing critical structures surrounding the surgical target (e.g., pulmonary artery, airways, etc.). To utilize the wealth of preoperative image/planning data and to guide targeting under conditions in which the tumor may not be directly visualized, a deformable registration approach has been developed that geometrically resolves images of the inflated (i.e., inhale or exhale) and deflated states of the lung. This novel technique employs a coarse model-driven approach using lung surface and bronchial airways for fast registration, followed by an image-driven registration using a variant of the Demons algorithm to improve target localization to within ∼1 mm. Two approaches to model-driven registration are presented and compared - the first involving point correspondences on the surface of the deflated and inflated lung and the second a mesh evolution approach. Intensity variations (i.e., higher image intensity in the deflated lung) due to expulsion of air from the lungs are accounted for using an a priori lung density modification, and its improvement on the performance of the intensity-driven Demons algorithm is demonstrated. Preliminary results of the combined model-driven and intensity-driven registration process demonstrate accuracy consistent with requirements in minimally invasive thoracic surgery in both target localization and critical structure avoidance.

SU‐E‐I‐103: Tissue Dependent Deformation Field Regularization through Collapsed Cone Convolution/Superposition

Purpose Previous Tissue‐dependent deformation field filters incur a relatively high computation cost. We present a collapsed‐cone based adaptive filtering method to reduce the computational overhead. Our proposed method is able to reduce the computational complexity of the superposition process from O(q3N3) to O(MN3) time, where q is the chosen neighborhood size, N is the volume dimension and M is the number of kernel axes. Method and Materials Our proposed filter performs diffusion along chosen kernel axes instead of within a size‐limited cubic neighborhood. During the ray tracing process along the kernel axis, the contribution of a given voxel to its neighbors is exponentially attenuated to simulate the physical characteristics of tissues. During the attenuation, local biomedical information can be naturally incorporated by scaling the ray tracing length according to the underlying tissue stiffness. This scaling makes a voxel within soft tissues more “malleable” with respect to its neighborsˈ movements. Results: We integrated different regularization methods into the same deformable registration approach (Demons algorithm). The kernel size of the Gaussian filter was set to be 5; the number of kernel directions for the CC filter were 4 in 2D and 16 in 3D. The experiments shows that our results produces are no‐worse result with a faster speed than these from other techniques. By investigating the finer details, the proposed method shows better capability of tracking changes. Conclusion Wpresented a collapsed‐cone (CC) based technique for the regularization of the deformation fields in image registration, which provides realistic deformation field and is useful for studying and modeling organs in motion. Since the CC filter can cover the entire 3D volume of interest, a better maintenance of the overall object shapes can be achieved. The incorporation of tissue information allows the CC filter to adjust its behavior to match the tissue biomedical characteristics.

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.

SU-GG-T-25: Improving Deformable Dose Registration of Brachytherapy by Incorporating CT Scan Masking

Purpose: Deformable dose registration is a burgeoning use for deformable registration algorithms. By applying the deformation matrix generated from an anatomic registration to the dose matrix, it may be possible to sum patient doses across multiple treatment fractions. In certain clinical situations, such as HDR brachytherapy, obtaining a good registration is difficult. Improving the performance in such cases may be possible using only the anatomy of interest. Using the physician drawn contours to mask off areas outside the organs of interest can provide better registration results. With an improved registration, the doses can be added across fractions more accurately. Methods: Using a research version of the program ABAS (CMS Elekta Software, Maryland Heights, MO), a deformable registration was performed on image sets of a multi‐fraction brachytherapy treatment. The quality of registration was examined by using the Dice Similarity Coefficient (DSC) values for bladder and rectum contours. A routine was written to automatically mask scans using bony anatomy and physician drawn contours. Registrations performed using the full patient CT scan were compared to registrations performed with a masked CT.Results: The focus of this study was the DSC values for bladder and rectum. Comparing these values for registrations performed with normal CT scans and masked CT scans demonstrates that masking can provide a much better registration. Patients with bladder DSC values of 0.38–0.74 improved to 0.94–0.99 with the masking technique. DSC values of 0.9 are commonly seen in cases of patients with well‐contrasted bladder and rectal anatomy.Conclusions: Using a masking approach for deformable registration can greatly improve the registration results for HDR brachytherapy patients. Masking can be done automatically in seconds using physician drawn contours. Focusing a registration on the anatomical areas that are the most relevant to treatment may provide a superior deformable dose registration.

SU‐FF‐I‐67: Conventional CT to Cone‐Beam CT Image Registration Using Combination of Intensity‐ and Landmark‐Based Deformable Image Registration Methods

Purpose: A hybrid deformable registration approach was proposed to combine the landmark‐based compact support radial basis functions (CSRBFs) spline registration with the automatic intensity‐based diffusion registration. The goal was to gain more local control of the region that cannot be handled properly with intensity‐based automatic image registration for cone beam CT (CBCT) images with local image artifacts. Method and Materials: The hybrid registration takes the advantage of both methods: the diffusion registration for automatic registration of the entire image and the CSRBFs spline method with limited landmark points to control the local deformation for special situations. For registration of CT image with the CBCT image of head and neck cancer patient, the air cavity and the image artifacts in CBCT image are the major concern of the local CSRBFs spline registration. The control points were usually selected on the boundary of those correspondence ambiguity regions in both images. Due to the limited range of the CSRBFs, the registration will not affect the area outside of the region where the landmark points were placed. The constraint of the topology preservation involved in this algorithm ensures that the transformations are reliable and accurate. Results: we register CT image to CBCT images of head and neck cancer patients, and map the manual contours to daily CBCT image using three different registration approaches. The performances of the registration algorithms were evaluated by visually assessing the agreement of the structures with the deformed contours in the CBCT image. We found that the hybrid registration gave a better result. Conclusion: The hybrid method can handle correspondence ambiguity problem in image registration cause by air cavity and image artifacts in CBCT images. It can also improve the registration accuracy without significantly increasing the registration time.Research is sponsored in part by Varian.