Intensity-based Rigid Registration Research Articles

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

Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy. We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three-dimensional (3D) point cloud (PC) matching network. Image intensity-based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network's motion prediction with biomechanical constraints, finite element (FE) modeling-generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field. The mean and standard deviation of our method were 0.93±0.01, 1.66±0.10mm, and 2.68±1.91mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity-based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality-independent neighborhood descriptor (MIND) registration, respectively. We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.

A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules within the mouse hind-limb. Sections of the tibialis anterior muscle from C57BL/J6 mice (both normal and subjected to femoral artery excision) were reconstructed using pairwise rigid and affine registrations of 5 µm-thick, paraffin-embedded serial sections digitized at 0.25 µm/pixel. Low-resolution intensity-based rigid registration was used to initialize the nucleus landmark-based registration, and conventional high-resolution intensity-based registration method. The affine nucleus landmark-based registration was developed in this work and was compared to the conventional affine high-resolution intensity-based registration method. Target registration errors were measured between adjacent tissue sections (pairwise error), as well as with respect to a 3D reference reconstruction (accumulated error, to capture propagation of error through the stack of sections). Accumulated error measures were lower (p<0.01) for the nucleus landmark technique and superior vasculature continuity was observed. These findings indicate that registration based on automatic extraction and correspondence of small, homologous landmarks may support accurate 3D histology reconstruction. This technique avoids the otherwise problematic “banana-into-cylinder” effect observed using conventional methods that optimize the pairwise alignment of salient structures, forcing them to be section-orthogonal. This approach will provide a valuable tool for high-accuracy 3D histology tissue reconstructions for analysis of diseased microvasculature.

Development of CBCT-based prostate setup correction strategies and impact of rectal distension

BackgroundCone-beam computed tomography (CBCT) image-guided radiotherapy (IGRT) systems are widely used tools to verify and correct the target position before each fraction, allowing to maximize treatment accuracy and precision. In this study, we evaluate automatic three-dimensional intensity-based rigid registration (RR) methods for prostate setup correction using CBCT scans and study the impact of rectal distension on registration quality.MethodsWe retrospectively analyzed 115 CBCT scans of 10 prostate patients. CT-to-CBCT registration was performed using (a) global RR, (b) bony RR, or (c) bony RR refined by a local prostate RR using the CT clinical target volume (CTV) expanded with 1-to-20-mm varying margins. After propagation of the manual CT contours, automatic CBCT contours were generated. For evaluation, a radiation oncologist manually delineated the CTV on the CBCT scans. The propagated and manual CBCT contours were compared using the Dice similarity and a measure based on the bidirectional local distance (BLD). We also conducted a blind visual assessment of the quality of the propagated segmentations. Moreover, we automatically quantified rectal distension between the CT and CBCT scans without using the manual CBCT contours and we investigated its correlation with the registration failures. To improve the registration quality, the air in the rectum was replaced with soft tissue using a filter. The results with and without filtering were compared.ResultsThe statistical analysis of the Dice coefficients and the BLD values resulted in highly significant differences (p<10−6) for the 5-mm and 8-mm local RRs vs the global, bony and 1-mm local RRs. The 8-mm local RR provided the best compromise between accuracy and robustness (Dice median of 0.814 and 97% of success with filtering the air in the rectum). We observed that all failures were due to high rectal distension. Moreover, the visual assessment confirmed the superiority of the 8-mm local RR over the bony RR.ConclusionThe most successful CT-to-CBCT RR method proved to be the 8-mm local RR. We have shown the correlation between its registration failures and rectal distension. Furthermore, we have provided a simple (easily applicable in routine) and automatic method to quantify rectal distension and to predict registration failure using only the manual CT contours.

A comparison of rigid registration methods for prostate localization on CBCT and the dependence on rectum distension

We evaluated automatic three-dimensional intensity-based rigid registration (RR) methods for prostate localization on CBCT scans and studied the impact of rectum distension on registration quality. 106 CBCT scans of 9 prostate patients were used. Each one was registered to the planning computed tomography (CT) scan using different methods: (a) global registration, (b) pelvis bony structure registration, (c) bony registration refined by a local prostate registration using the CT clinical target volume (CTV) expanded with 1, 3, 5, 8, 10, 12, 15 or 20-mm margin. Automatic CBCT contours were generated after propagation of the manual CT contours. To evaluate results, a radiation oncologist was asked to manually delineate the CTV on the CBCT scans (gold standard). The Dice similarity coefficients between propagated and manual CBCT contours were calculated.

Evaluation of intersession 3D‐TRUS to 3D‐TRUS image registration for repeat prostate biopsies

3D-TRUS-guided prostate biopsy permits a 3D record of biopsy cores, supporting the planning of targets to resample or avoid during repeat biopsy sessions. Image registration is required in order to map biopsy targets planned on a previous session's 3D-TRUS image into the context of the current session. The authors evaluated the performance of surface- and intensity-based rigid and nonrigid registration algorithms for this task using a clinically motivated success criterion of a maximum 2.5 mm target registration error (TRE). The authors collected two 3D-TRUS images for each of 13 patients, where each image was collected in a separate biopsy session, and the sessions were 1 week apart. The authors tested the iterative closest point and thin-plate spline surface-based registration methods, and the block matching and B-spline intensity-based methods. Manually marked intrinsic fiducials (calcifications) were used to calculate a TRE for each of the tested methods. In addition, error ellipsoids, anisotropy, and variability due to image segmentation were analyzed. All analysis was performed separately for the peripheral zone since this area harbors up to 80% of all prostate cancer. Only the intensity-based nonrigid registration method met the success criterion for both the whole gland and the peripheral zone. Segmentation was a substantial contributor to registration error variability for the surface-based methods, and the surface-based methods resulted in greater error volumes and anisotropy. Intensity-based rigid registration is clinically sufficient to register regions outside the peripheral zone, but nonrigid registration is required in order to register the peripheral zone with clinically needed accuracy. The clinical advantage of using nonrigid registration is questionable since the difference between the RMS TREs for rigid and nonrigid intensity-based registration could be considered to be small (0.3 mm) and is statistically significant. If the added clinical value in performing a nonrigid registration is insufficient given the additional time required for this computation, rigid registration alone may be suitable.

A study of the motion and deformation of the heart due to respiration.

This paper describes a quantitative assessment of respiratory motion of the heart and the construction of a model of respiratory motion. Three-dimensional magnetic resonance scans were acquired on eight normal volunteers and ten patients. The volunteers were imaged at multiple positions in the breathing cycle between full exhalation and full inhalation while holding their breath. The exhalation volume was segmented and used as a template to which the other volumes were registered using an intensity-based rigid registration algorithm followed by nonrigid registration. The patients were imaged at inhale and exhale only. The registration results were validated by visual assessment and consistency measurements indicating subvoxel registration accuracy. For all subjects, we assessed the nonrigid motion of the heart at the right coronary artery, right atrium, and left ventricle. We show that the rigid-body motion of the heart is primarily in the craniocaudal direction with smaller displacements in the right-left and anterior-posterior directions; this is in agreement with previous studies. Deformation was greatest for the free wall of the right atrium and the left ventricle; typical deformations were 3-4 mm with deformations of up to 7 mm observed in some subjects. Using the registration results, landmarks on the template surface were mapped to their correct positions through the breathing cycle. Principal component analysis produced a statistical model of the motion and deformation of the heart. We discuss how this model could be used to assist motion correction.