B-spline Transformation Research Articles

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

A 2D/3D Non-rigid Registration Method for Lung Images Based on a Non-linear Correlation Between Displacement Vectors and Similarity Measures

This paper presents a novel 2D/3D non-rigid registration method for lung lesions tracking in image-guided diagnoses and treatments. Preoperative 3D lung CT volumes were obtained at a series of respiratory phases and important anatomical points were extracted. A CT volume was selected as reference volume and others were considered floating volumes. Displacement vectors of anatomical points were calculated using coherent point drift (CPD) and diffeomorphic-demon methods. For each CT volume, 2D digitally reconstructed radiographs (DRR) were generated by ray projection to simulate intraoperative 2D X-ray, and grayscale-based similarity measures of DRR between reference and floating volumes were calculated. A pulmonary respiration model was constructed using cubic polynomial, which represents the non-linear correlation between displacement vectors and similarity measures. During operation, the pulmonary respiration model used X-ray to obtain displacement vectors of important anatomical points; deformation fields were calculated from the displacement vectors through B-spline transformations. Finally, simulated lung CT volumes corresponding to intraoperative X-ray were generated by applying deformation fields to reference volume. The proposed method was compared to a state-of-the-art 2D/3D non-rigid registration method named respiratory-phase matching. Experimental results on two datasets respectively from a lung cancer patient and an ex vivo pig lung, showed mean registration accuracy of the proposed method was 1.57 ± 0.92 mm and 2.65 ± 1.35 mm; an improvement of 23% and 9% over the matching method. Moreover, mean lung structure overlaps were 0.91 and 0.88, comparable to matching method. The proposed method has potential in aiding lung intervention diagnoses and real-time lesion tracking.

A 3D cine-MRI acquisition technique and image analysis framework to quantify bowel motion demonstrated in gynecological cancer patients.

PurposeMagnetic resonance imaging (MRI) is increasingly used in radiation oncology for target delineation and radiotherapy treatment planning, for example, in patients with gynecological cancers. As a consequence of pelvic radiotherapy, a part of the bowel is irradiated, yielding risk of bowel toxicity. Existing dose‐effect models predicting bowel toxicity are inconclusive and bowel motion might be an important confounding factor. The exact motion of the bowel and dosimetric effects of its motion are yet uncharted territories in radiotherapy. In diagnostic radiology methods on the acquisition of dynamic MRI sequences were developed for bowel motility visualization and quantification. Our study aim was to develop an imaging technique based on three‐dimensional (3D) cine‐MRI to visualize and quantify bowel motion and demonstrate it in a cohort of gynecological cancer patients.MethodsWe developed an MRI acquisition suitable for 3D bowel motion quantification, namely a balanced turbo field echo sequence (TE = 1.39 ms, TR = 2.8 ms), acquiring images in 3.7 s (dynamic) with a 1.25 × 1.25 × 2.5 mm3 resolution, yielding a field of view of 200 × 200 × 125 mm3. These MRI bowel motion sequences were acquired in 22 gynecological patients. During a 10‐min scan, 160 dynamics were acquired. Subsequent dynamics were deformably registered using a B‐spline transformation model, resulting in 159 3D deformation vector fields (DVFs) per MRI set. From the 159 DVFs, the average vector length was calculated per voxel to generate bowel motion maps. Quality assurance was performed on all 159 DVFs per MRI, using the Jacobian Determinant and the Harmonic Energy as deformable image registration error metrics. In order to quantify bowel motion, we introduced the concept of cumulative motion–volume histogram (MVH) of the bowel bag volume. Finally, interpatient variation of bowel motion was analyzed using the MVH parameters M10%, M50%, and M90%. The M10%/M50%/M90% represents the minimum bowel motion per frame of 10%/50%/90% of the bowel bag volume.ResultsThe motion maps resulted in a visualization of areas with small and large movements within the bowel bag. After applying quality assurance, the M10%, M50%, and M90% were 4.4 (range 2.2–7.6) mm, 2.2 (range 0.9–4.1) mm, and 0.5 (range 0.2–1.4) mm per frame, on average over all patients, respectively.ConclusionWe have developed a method to visualize and quantify 3D bowel motion with the use of bowel motion specific MRI sequences in 22 gynecological cancer patients. This 3D cine‐MRI‐based quantification tool and the concept of MVHs can be used in further studies to determine the effect of radiotherapy on bowel motion and to find the relation with dose effects to the small bowel. In addition, the developed technique can be a very interesting application for bowel motility assessment in diagnostic radiology.

Multi-Modality Non-rigid Image Registration Using Local Similarity Estimations

In this study, we focus on improving the efficiency and accuracy of nonrigid multi-modality registration of medical images. In this regard, we analyze the potentials of using the point similarity measurement approach as an alternative to global computation of mutual information (MI), which is still the most renown multi-modality similarity measure. The improvement capabilities are illustrated using the popular B-spline transformation model. The proposed solution is a combination of three related improvements of the most straightforward implementation, i.e., efficient computation of the voxel displacement field, local estimation of similarity and usage of a static image intensity dependence estimate. Five image registration prototypes were implemented to show contribution and dependence of the proposed improvements. When all the proposed improvements are applied, a significant reduction of computational cost and increased accuracy are obtained. The concept offers additional improvement opportunities by incorporating prior knowledge and machine learning techniques into the static intensity dependence estimation.

The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer.

We quantitatively investigate the influence of image registration, using open-source software (3DSlicer), on kinetic analysis (Tofts model) of dynamic contrast enhanced MRI of early-stage breast cancer patients. We also show that registration computation time can be reduced by reducing the percent sampling (PS) of voxels used for estimation of the cost function. DCE-MRI breast images were acquired on a 3T-PET/MRI system in 13 patients with early-stage breast cancer who were scanned in a prone radiotherapy position. Images were registered using a BSpline transformation with a 2cm isotropic grid at 100, 20, 5, 1, and 0.5PS (BRAINSFit in 3DSlicer). Signal enhancement curves were analyzed voxel-by-voxel using the Tofts kinetic model. Comparing unregistered with registered groups, we found a significant change in the 90th percentile of the voxel-wise distribution of Ktrans. We also found a significant reduction in the following: (1) in the standard error (uncertainty) of the parameter value estimation, (2) the number of voxel fits providing unphysical values for the extracellular-extravascular volume fraction (ve > 1), and (3) goodness of fit. We found no significant differences in the median of parameter value distributions (Ktrans, ve) between unregistered and registered images. Differences between parameters and uncertainties obtained using 100PS versus 20PS were small and statistically insignificant. As such, computation time can be reduced by a factor of 2, on average, by using 20PS while not affecting the kinetic fit. The methods outlined here are important for studies including a large number of post-contrast images or number of patient images.

U-shaped relationship between pre-operative plasma fibrinogen levels and severe peri-operative bleeding in cardiac surgery.

An inverse linear relationship has been reported between pre-operative fibrinogen levels and postoperative blood loss in cardiac surgery. However, recently high pre-operative fibrinogen levels have also been reported to be associated with increased blood transfusion and re-operation. We tested the hypothesis that the relationship between pre-operative fibrinogen levels and severe peri-operative bleeding is not linear. A large-scale (n = 3883) single-centre retrospective study. A tertiary care teaching hospital. We analysed data from our institutional database which includes all patients above 18 years who underwent on-pump cardiac surgery through a sternotomy between September 2010 and May 2014. Peri-operative severe bleeding adapted from the Universal Definition of Peri-operative Bleeding, class 3 or 4. The relationship between pre-operative fibrinogen levels and peri-operative severe bleeding was analysed by binary logistic regression. A cubic B-spline transformation was used to estimate the relationship between pre-operative fibrinogen level associated with excessive peri-operative bleeding. Severe peri-operative bleeding was observed in 957 (24.6%) patients. An L-shaped relationship was observed between pre-operative fibrinogen levels and 24-h postoperative blood loss. The relationship between pre-operative fibrinogen levels and severe peri-operative bleeding (i.e. Universal Definition of Peri-operative Bleeding class 3 or 4) was U-shaped: the risk of severe peri-operative bleeding bottomed at 3.3 g l when the upward sloping curve started at 5.8 g l with a steeper increase above 8.2 g l. We reported a U-shaped relationship between severe peri-operative bleeding and pre-operative fibrinogen levels. While a low-level of fibrinogen appears to be associated with a high risk of bleeding, a high level does not necessarily protect the patient against such a risk and could even be a risk factor for peri-operative bleeding.

Robust three-dimensional registration on optical coherence tomography angiography for speckle reduction and visualization.

In the clinical applications of optical coherence tomography angiography (OCTA), the repeated scanning and averaging method can provide better contrast with reduced speckle noises in the final results, which are useful for visualizing and quantifying vascular components with high accuracy, reproducibility, and reliability. However, the inevitable patient motion presents a challenge to this method. The objective of this study is to meet this challenge by introducing a 3D registration method to register optical coherence tomography (OCT)/OCTA scans for precise volume averaging of multiple scans to improve the signal-to-noise ratio (SNR) and increase quantification accuracy. The proposed method utilized both rigid affine transformation and non-rigid B-spline transformation in which their parameters were optimized and calculated by the average stochastic gradient descent on OCT structural images. In addition, we also introduced a multi-level resolution approach to further improve the robustness and computational speed of our proposed method. The imaging performance was tested on in vivo imaging of human skin and eye and assessed by SNR, peak signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC). Five subjects were enrolled in this study for obtaining in vivo images of skin and retina. The proposed registration and averaging method provided substantial improvements of the imaging performance in terms of vessel connectivity and signal to noise ratio. The increase of repeated volume numbers in the averaging improves all the metrics assessed, i.e., SNR, PSNR and NCC. An improvement of the SNR from 10 to 40 dB after 10 repeated volumetric averaging was achieved. The proposed 3D registration and averaging method is effective in reducing speckle noises and suppressing motion artifacts, thereby improving SNR, PSNR and NCC metrics for final averaged images. It is expected that the proposed algorithm would be practically useful in better visualization and more reliable quantification of in vivo OCT and OCTA data, which would be beneficial to OCT clinical applications.

An Efficient MRI Brain Image Registration and Wavelet Based Fusion

Over last few decades, 3D reconstruction of medical images becomes advance technique in medical image processing. Reconstruction of 2D images of such data sets into 3D volumes, via registration of 2D sections had become a most interesting topic. In current years, MRI has been used for many medical analysis applications. The proposed system considered MRI images are taken from the same view, different times or acquired by different imaging modalities to increase the information. T1, T2 and PD MRI are taken as an input; T2 image is registered with a reference of T1 image using affine transformation, the registered T2 image is fused with T1 using DWT. The Fused T1T2 image is taken as reference image to register PD image using B-Spline transformation. DT-CWT technique is used to fuse the T1T2 image with registered PD image. The performance of the system shows that the proposed system gives more information by fusing T1T2PD images.

Automated Deformation-Based Analysis of 3D Optical Coherence Tomography in Diabetic Retinopathy.

Diabetic retinopathy (DR) is a significant microvascular complication of diabetes mellitus and a leading cause of vision impairment in working age adults. Optical coherence tomography (OCT) is a routinely used clinical tool to observe retinal structural and thickness alterations in DR. Pathological changes that alter the normal anatomy of the retina, such as intraretinal edema, pose great challenges for conventional layer-based analysis of OCT images. We present an alternative approach for the automated analysis of OCT volumes in DR research based on nonlinear registration. In this paper, we first obtain an anatomically consistent volume of interest (VOI) in different OCT images via carefully designed masking and affine registration. After that, efficient B-spline transformations are computed using stochastic gradient descent optimization. Using the OCT volumes of normal controls, for which layer-based segmentation works well, we demonstrate the accuracy of our registration-based analysis in aligning layer boundaries. By nonlinearly registering the OCT volumes of DR subjects to an atlas constructed from normal controls and measuring the Jacobian determinant of the deformation, we can simultaneously visualize tissue contraction and expansion due to DR pathology. Tensor-based morphometry (TBM) can also be performed for quantitative analysis of local structural changes. In our experimental results, we apply our method to a dataset of 105 subjects and demonstrate that volumetric OCT registration and TBM analysis can successfully detect local retinal structural alterations due to DR.

PREPROCESSING AND REGISTRATION OF MINISCOPE-BASED CALCIUM IMAGING OF THE RODENT BRAIN

Abstract. Microscopic imaging is central to the brain and cognition studies in animals and often requires advanced image processing. In vivo recordings on awake behaving animals require stabilization of the images as the tissue in the images undergoes non-rigid deformations due to animal movement, pulse beat and breathing of the animal. Here we propose an approach to compensation for the tissue motion in calcium imaging data acquired with miniaturized wearable microscopes (miniscopes) from live rodent brains. Our approach includes preprocessing of the images in which we compensate for non-uniform illumination, remove calcium transients and instrument-specific noise. For image registration we use the multiscale mutual information based non-rigid algorithm with B-spline transformation model. We present the preliminary results of such motion compensation approach applied to the real miniscope image stacks.

Deformable Registration of Chest Radiographs Using B-spline Based Method with Modified Residual Complexity

Accurate registration of chest radiographs plays an increasingly important role in medical applications. However, most current intensity-based registration methods rely on the assumption of intensity conservation that is not suitable for alignment of chest radiographs. In this study, we propose a novel algorithm to match chest radiographs, for which the conventional residual complexity (RC) is modified as the similarity measure and the cubic B-spline transformation is adopted for displacement estimation. The modified similarity measure is allowed to incorporate the neighborhood influence into variation of intensity in a justified manner of the weight, while the transformation is implemented with a registration framework of pyramid structure. The results show that the proposed algorithm is more accurate in registration of chest radiographs, compared with some widely used methods such as the sum-of-squared-differences (SSD), correlation coefficient (CC) and mutual information (MI) algorithms, as well as the conventional RC approaches.

Accurate registration of in vivo time-lapse images.

In vivo imaging experiments often require automated detection and tracking of changes in the specimen. These tasks can be hindered by variations in the position and orientation of the specimen relative to the microscope, as well as by linear and nonlinear tissue deformations. We propose a feature-based registration method, coupled with optimal transformations, designed to address these problems in 3D time-lapse microscopy images. Features are detected as local regions of maximum intensity in source and target image stacks, and their bipartite intensity dissimilarity matrix is used as an input to the Hungarian algorithm to establish initial correspondences. A random sampling refinement method is employed to eliminate outliers, and the resulting set of corresponding features is used to determine an optimal translation, rigid, affine, or B-spline transformation for the registration of the source and target images. Accuracy of the proposed algorithm was tested on fluorescently labeled axons imaged over a 68-day period with a two-photon laser scanning microscope. To that end, multiple axons in individual stacks of images were traced semi-manually and optimized in 3D, and the distances between the corresponding traces were measured before and after the registration. The results show that there is a progressive improvement in the registration accuracy with increasing complexity of the transformations. In particular, sub-micrometer accuracy (2-3 voxels) was achieved with the regularized affine and B-spline transformations.

Accurate estimation of choriocapillaris flow deficits beyond normal intercapillary spacing with swept source OCT angiography.

To estimate choriocapillaris flow deficits beyond normal intercapillary distance with swept source optical coherence tomography angiography (SS-OCTA). Subjects were enrolled and repeated SS-OCTA scans were performed using the 3 mm × 3 mm scan pattern. Blood flow was identified using the complex optical microangiography (OMAGc) algorithm. The choriocapillaris (CC) was defined as a 20 µm slab of the flow volume beneath the outer boundary of Bruch's membrane (BM) and was compensated with the corresponding structural image for flow deficits measurement. Flow deficits were segmented based on one mean standard deviation from a normal database. A histogram based thresholding method was developed to remove small flow deficits that were determined by examining intercapillary spacing within normal CC networks. A registration method based on affine and B-spline transformation was utilized for the CC angiogram averaging. Four repeated scans were averaged, and results were compared with and without removal of small flow deficits after averaging a different number of scans (N=1, group 1; N=2, group 2; N=3, group 3 and N=4, group 4). Seven normal subjects were enrolled. Intercapillary distance was found to be 24 µm for the CC networks under OCTA, which was used as the threshold to exclude small flow deficits for CC quantification. After averaging, significant reduction in background noise and improvement in continuity of blood vessel networks were observed both on retinal and choriocapillaris angiograms. Flow deficit percentages of the choriocapillaris were significantly reduced with averaging (group 1 vs. group 2: P<0.0001; group 2 vs. group 3: P<0.001; group 3 vs. group 4: P<0.001). The flow deficit percentages were also significantly reduced after removing the small flow deficits (≤24 µm in diameter) in all groups (P<0.01). A statistically significant difference was found after removing small flow deficits (≤24 µm in diameter) between group 1 and group 2 (P<0.001), between group 2 and group 3 (P<0.05), and between group 3 and group 4 (P<0.05). However, the significance was decreased compared to that without small flow deficits removal. A method was developed to improve the robust estimation of choriocapillaris flow deficits by removing the small flow deficits corresponding to normal intercapillary spacing. After the removal of small flow deficits, fewer repeats were required for image averaging to achieve comparable accuracy of flow deficit measurements with SS-OCTA.

Automated three-dimensional tracking of the left ventricular myocardium in time-resolved and dose-modulated cardiac CT images using deformable image registration

BackgroundAssessment of myocardial deformation from time-resolved cardiac computed tomography (4D CT) would augment the already available functional information from such an examination without incurring any additional costs. A deformable image registration (DIR) based approach is proposed to allow fast and automatic myocardial tracking in clinical 4D CT images. MethodsLeft ventricular myocardial tissue displacement through a cardiac cycle was tracked using a B-spline transformation based DIR. Gradient of such displacements allowed Lagrangian strain estimation with respect to end-diastole in clinical 4D CT data from ten subjects with suspected coronary artery disease. Dice similarity coefficient (DSC), point-to-curve error (PTC), and tracking error were used to assess the tracking accuracy. Wilcoxon signed rank test provided significance of tracking errors. Topology preservation was verified using Jacobian of the deformation. Reliability of estimated strains and torsion (normalized twist angle) was tested in subjects with normal function by comparing them with normal strain in the literature. ResultsComparison with manual tracking showed high accuracy (DSC: 0.99±0.05; PTC: 0.56mm±0.47 mm) and resulted in determinant(Jacobian)>0 for all subjects, indicating preservation of topology. Average radial (0.13 mm), angular (0.64) and longitudinal (0.10 mm) tracking errors for the entire cohort were not significant (p > 0.9). For patients with normal function, average strain [circumferential, radial, longitudinal] and peak torsion estimates were: [-23.5%, 31.1%, −17.2%] and 7.22°, respectively. These estimates were in conformity with the reported normal ranges in the existing literature. ConclusionsAccurate wall deformation tracking and subsequent strain estimation are feasible with the proposed method using only routine time-resolved 3D cardiac CT.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

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.

A Hybrid Tactic Model Intended for Video Compression Using Global Affine Motion and Local Free-Form Transformation Parameters

Video compression marks its necessity when a huge sized video needs to be transmitted. The process starts with the identification of GoP (group of pictures), which depends on I- (intra), B- (bidirectional) and P- (predicted) frames determination. GoP is fixed, where consecutive frames are placed in an orderly manner based on the GoP size. Conventionally, B-frames lead to buffering of memory within the past and future frames consuming more computational time. Such issues are handled by an adaptive framework for determining frames based on matching criteria rather than fixed GoP. NSEW (North–South–East–West) affine translation (NAT) is proposed for replacing B with either I- or P-frame. The proposed framework involves video compression using affine motion-based free-form transformation and video decompression using warping methodologies for the purpose of compressing and decompressing the video sequence, based on the resulted I- and P-frames. B-spline transformation was also initiated at local level along with global affine transformation to improve the subjective quality of the decompressed video sequence. The methodology was investigated for the file size, computational time, peak-signal-to-noise ratio (PSNR) and Structural Similarity index (SSIM), which proved the superiority of the proposed technique. Further, the methodology was also investigated with optimizing the affine motion parameters (AMP) using nonlinear least squares, Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS which yet again proved to be far more superior to conventional techniques with an average PSNR of 38.98 dB with LBFGS. To further improve the subjective quality, affine B-spline-based motion estimation using LBFGS was implemented and observed the average PSNR gain to be 42.03 dB.

Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks.

Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning.

Nonrigid Registration of Pre-Procedural MRI and Intra-Procedural CT in CT-Guided Cryoablation of Lung Tumors to Improve Lung Tumor Conspicuity

To improve lung tumor conspicuity during CT-guided cryoablations, we used nonrigid image registrations to fuse pre-procedural MR images and intra-procedural CT images and determined which set of CT images taken at planning, targeting, and monitoring phases of the procedure provided the most accurate and fastest registrations. In 10 percutaneous CT-guided cryoablation procedures, MR images were registered with intra-procedural CT images using a nonrigid registration technique using an intensity-based approach with affine and B-Spline transformations. The time to complete the registration as well as the accuracy of the registration defined by Target Registration Error (TRE), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD) were measured to assess the performance of the registration. The least significant difference (LSD) method was used as a post-hoc analysis for comparing time and accuracy among planning, targeting, and monitoring phases. The mean TRE of the registration ranged from 6.26 (planning) to 10.31 (monitoring) mm. The mean DSC ranged from 83.86 (monitoring) to 89.22 (planning). The mean HD values ranged from 7.74 (targeting) to 12.20 (monitoring). Mean registration time ranged from 68.67 (monitoring) to 92.02 (planning) s. Using HD, registrations in either the planning or targeting phase were more accurate than in the monitoring phase. The registration was faster using monitoring images than using planning images. Nonrigid registration techniques can be used to fuse pre-procedural MR images with intra-procedural CT images with varying performance depending on the CT images taken at the different phases of the procedure. Therefore, caution should be taken in setting expectations on accuracies and speeds of registration depending on the phases of the CT-guided ablation procedures.

A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants

This paper proposes a new hybrid data-driven soft measurement modeling method for power plant sensor condition monitoring and fault diagnosis. The method integrates Generalized Regression Neural Network (GRNN), Mean Impact Value (MIV), Partial-Least Squares Regression (PLSR) and B-Spline transformation techniques. First, the relevant parameters are obtained from mechanism analysis and a GRNN model is built to assess the average contribution rate of each independent variable and filter out the main modeling parameters by method of MIV. Then, the main modeling parameters are modeled with a PLSR method based on the cubic B-Spline transformation, which is an effective approach to the nonlinear modeling and multicollinearity problems. The final reliable model is completed to monitor and diagnose the sensors. Taking the active power sensor of a combined cycle generator unit of Siemens V94.3A as an example, the computational result shows that this modeling approach to sensor measurement data fits well in both accuracy and generalization ability under different conditions. Through fault signs and fault diagnosis methods analysis, this model could accurately identify sensor fault types. Most importantly, only a few model parameters need to be saved, and the model has low computation cost and strong robustness. Therefore the model is more suitable in solving the online real-time monitoring and diagnosis problems.

A MR-TRUS Registration Method for Ultrasound-Guided Prostate Interventions.

In this paper, we report a MR-TRUS prostate registration method that uses a subject-specific prostate strain model to improve MR-targeted, US-guided prostate interventions (e.g., biopsy and radiotherapy). The proposed algorithm combines a subject-specific prostate biomechanical model with a B-spline transformation to register the prostate gland of the MRI to the TRUS images. The prostate biomechanical model was obtained through US elastography and a 3D strain map of the prostate was generated. The B-spline transformation was calculated by minimizing Euclidean distance between the normalized attribute vectors of landmarks on MR and TRUS prostate surfaces. This prostate tissue gradient map was used to constrain the B-spline-based transformation to predict and compensate for the internal prostate-gland deformation. This method was validated with a prostate-phantom experiment and a pilot study of 5 prostate-cancer patients. For the phantom study, the mean target registration error (TRE) was 1.3 mm. MR-TRUS registration was also successfully performed for 5 patients with a mean TRE less than 2 mm. The proposed registration method may provide an accurate and robust means of estimating internal prostate-gland deformation, and could be valuable for prostate-cancer diagnosis and treatment.