Hybrid cone-beam tomographic reconstruction: incorporation of prior anatomical models to compensate for missing data

Accurate and robust registration method for computer-assisted high tibial osteotomy surgery

PURPOSE

Computer-assisted high tibial osteotomy (HTO) is a frequently used treatment technique for lower extremity orthopedics, and its small incision and low exposure area are major limitations in tibial registration. This work combines skin surface features and gives a suitable registration algorithm based on Iterative Closest Points (ICP) algorithm to improve registration results. Furthermore, the precision, stability and efficiency of the described method is evaluated.

METHODS

After the initialization stage, the bone surface and skin surface data are combined to construct registration features. Then, a steepest perturbation search method is performed after the ICP algorithm (SPS-ICP) to obtain the optimal transformation through several iterations. Finally, the registration result is evaluated by establishing ground-truth through manual landmarks.

RESULTS

Phantom experiments including simulated human tissue show that the proximal fiducial registration error (FRE) of our method can reach 0.80 ± 0.30 mm (mean ± SD) with an overall rotational error < 1° and translational error < 1.5 mm. Furthermore, it remains stable when the point set is sparse. The average registration time is less than 40 s to ensure the high efficiency of surgical operation.

CONCLUSIONS

The approach fully describes a well-defined framework without additional imaging acquisition equipment for Computer-assisted HTO. By the experiment on the basis of a phantom with simulated soft tissue, the proposed method enables the accurate and robust registration of the tibia, and its computation time meets the demands of surgery.

Estimation of thyroid volume from scintigraphy through 2D/3D registration of a statistical shape model

Accurate measurement of thyroid volume is important for thyroid disease diagnosis and therapy. In nuclear medicine, the thyroid volume is usually estimated from scintigraphy images using empirical equations. However, due to the lack of volumetric information from the scintigraphy image, the accuracy of equation-based estimation is imperfect. To solve this problem, this paper proposes a method which registers a 3D thyroid statistical shape model (SSM) to a single-view scintigraphy image to achieve more accurate volume estimation. The SSM was constructed based on a training set of segmented 3D CT images, and the thyroid shape variations between the training subjects were modelled using the point distribution model. For thyroid volume estimation, the SSM was projected into the scintigraphy image of the target patient, and then the projected model shape was nonrigidly registered with the patient's scintigraphy image. The resultant 2D deformation file was back-projected to 3D space to guide the deformation of the 3D SSM. This process was repeated iteratively until convergence, and the volume of the finally deformed SSM was considered as the estimation of the patient's thyroid volume. For validation, this method was evaluated based on a test set of 20 scintigraphy images, achieving an estimation error of  -2.10%  ±  5.20% which was much less than the error of the conventional equation-based method (35.76%  ±  15.20%) based on the same test set. The robustness of this method was further tested using a challenging case, i.e. a scintigraphy image with a large thyroid tumor. For this case, the volume estimation error was only 6.08%. Our method has significantly improved the accuracy of thyroid volume estimation from scintigraphy images, and it will enhance the value of scintigraphy imaging for thyroid disease diagnosis and radioiodine therapy.

Deformable registration of CT and cone-beam CT with local intensity matching

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

Probabilistic atlas prior for CT image reconstruction

BACKGROUND AND OBJECTIVES

In computed tomography (CT), statistical iterative reconstruction (SIR) approaches can produce images of higher quality compared to the conventional analytical methods such as filtered backprojection (FBP) algorithm. Effective noise modeling and possibilities to incorporate priors in the image reconstruction problem are the main advantages that lead to continuous development of SIR methods. Oriented by low-dose CT requirements, several methods are recently developed to obtain a high-quality image reconstruction from down-sampled or noisy projection data. In this paper, a new prior information obtained from probabilistic atlas is proposed for low-dose CT image reconstruction.

METHODS

The proposed approach consists of two main phases. In learning phase, a dataset of images obtained from different patients is used to construct a 3D atlas with Laplacian mixture model. The expectation maximization (EM) algorithm is used to estimate the mixture parameters. In reconstruction phase, prior information obtained from the probabilistic atlas is used to construct the cost function for image reconstruction.

RESULTS

We investigate the low-dose imaging by considering the reduction of X-ray beam intensity and by acquiring the projection data through a small number of views or limited view angles. Experimental studies using simulated data and chest screening CT data demonstrate that the probabilistic atlas prior is a practically promising approach for the low-dose CT imaging.

CONCLUSIONS

The prior information obtained from probabilistic atlas constructed from earlier scans of different patients is useful in low-dose CT imaging.

Three-dimensional reconstruction of the peripancreatic vascular system based on computed tomographic angiography images and its clinical application in the surgical management of pancreatic tumors

OBJECTIVE

This study aimed to investigate the clinical significance of 3-dimensional (3D) reconstruction of peripancreatic vessels for patients with suspected pancreatic cancer (PC).

METHODS

A total of 89 patients with PC were included; 60 patients randomly underwent computed tomographic angiography. Based on the findings of 3D reconstruction of peripancreatic vessels, the appropriate method for individualized tumor resection was determined. These patients were compared with 29 conventionally treated patients with PC.

RESULTS

The rate of visualization was 100% for great vessels around the pancreas. The detection rates for anterior superior pancreaticoduodenal artery, posterior superior pancreaticoduodenal artery, anterior inferior pancreaticoduodenal artery, posterior inferior pancreaticoduodenal artery, dorsal pancreatic artery, superior marginal arterial branch of the pancreatic head, anterior superior pancreaticoduodenal vein, posterior superior pancreaticoduodenal vein, anterior inferior pancreaticoduodenal vein, and posterior inferior pancreaticoduodenal vein were 86.6%, 85.0%, 76.6%, 71.6%, 91.6%, 53.3%, 61.6%, 55.0%, 43.3%, and 51.6%, respectively. Forty-three patients who had undergone 3D reconstruction underwent surgery. Of the 29 conventionally treated patients, 19 underwent surgery. The operative time, blood loss, length of hospital stay, and complication incidence of the 43 patients were superior to that of the 19 patients.

CONCLUSIONS

A peripancreatic vascular reconstruction can reveal the vascular anatomy, variations of peripancreatic vascular, and tumor-induced vascular changes; the application of the simulation surgery platform could reduce surgical trauma and decrease operative time.

3D-2D registration for surgical guidance: effect of projection view angles on registration accuracy

An algorithm for intensity-based 3D-2D registration of CT and x-ray projections is evaluated, specifically using single- or dual-projection views to provide 3D localization. The registration framework employs the gradient information similarity metric and covariance matrix adaptation evolution strategy to solve for the patient pose in six degrees of freedom. Registration performance was evaluated in an anthropomorphic phantom and cadaver, using C-arm projection views acquired at angular separation, Δθ, ranging from ∼0°-180° at variable C-arm magnification. Registration accuracy was assessed in terms of 2D projection distance error and 3D target registration error (TRE) and compared to that of an electromagnetic (EM) tracker. The results indicate that angular separation as small as Δθ ∼10°-20° achieved TRE <2 mm with 95% confidence, comparable or superior to that of the EM tracker. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers and manual registration.

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior-posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 ± 0.16 for the spleen to 0.88 ± 0.03 for the whole body, and the average surface distance varies from 0.54 ± 0.06 mm for the lungs to 0.85 ± 0.10mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen.

Probabilistic evaluation of three-dimensional reconstructions from X-ray images spanning a limited angle

An important part of computed tomography is the calculation of a three-dimensional reconstruction of an object from series of X-ray images. Unfortunately, some applications do not provide sufficient X-ray images. Then, the reconstructed objects no longer truly represent the original. Inside of the volumes, the accuracy seems to vary unpredictably. In this paper, we introduce a novel method to evaluate any reconstruction, voxel by voxel. The evaluation is based on a sophisticated probabilistic handling of the measured X-rays, as well as the inclusion of a priori knowledge about the materials that the object receiving the X-ray examination consists of. For each voxel, the proposed method outputs a numerical value that represents the probability of existence of a predefined material at the position of the voxel while doing X-ray. Such a probabilistic quality measure was lacking so far. In our experiment, false reconstructed areas get detected by their low probability. In exact reconstructed areas, a high probability predominates. Receiver Operating Characteristics not only confirm the reliability of our quality measure but also demonstrate that existing methods are less suitable for evaluating a reconstruction.

Volume-of-change cone-beam CT for image-guided surgery

C-arm cone-beam CT (CBCT) can provide intraoperative 3D imaging capability for surgical guidance, but workflow and radiation dose are the significant barriers to broad utilization. One main reason is that each 3D image acquisition requires a complete scan with a full radiation dose to present a completely new 3D image every time. In this paper, we propose to utilize patient-specific CT or CBCT as prior knowledge to accurately reconstruct the aspects of the region that have changed by the surgical procedure from only a sparse set of x-rays. The proposed methods consist of a 3D-2D registration between the prior volume and a sparse set of intraoperative x-rays, creating digitally reconstructed radiographs (DRRs) from the registered prior volume, computing difference images by subtracting DRRs from the intraoperative x-rays, a penalized likelihood reconstruction of the volume of change (VOC) from the difference images, and finally a fusion of VOC reconstruction with the prior volume to visualize the entire surgical field. When the surgical changes are local and relatively small, the VOC reconstruction involves only a small volume size and a small number of projections, allowing less computation and lower radiation dose than is needed to reconstruct the entire surgical field. We applied this approach to sacroplasty phantom data obtained from a CBCT test bench and vertebroplasty data with a fresh cadaver acquired from a C-arm CBCT system with a flat-panel detector. The VOCs were reconstructed from a varying number of images (10-66 images) and compared to the CBCT ground truth using four different metrics (mean squared error, correlation coefficient, structural similarity index and perceptual difference model). The results show promising reconstruction quality with structural similarity to the ground truth close to 1 even when only 15-20 images were used, allowing dose reduction by the factor of 10-20.

Intraoperative image-based multiview 2D/3D registration for image-guided orthopaedic surgery: incorporation of fiducial-based C-arm tracking and GPU-acceleration

Intraoperative patient registration may significantly affect the outcome of image-guided surgery (IGS). Image-based registration approaches have several advantages over the currently dominant point-based direct contact methods and are used in some industry solutions in image-guided radiation therapy with fixed X-ray gantries. However, technical challenges including geometric calibration and computational cost have precluded their use with mobile C-arms for IGS. We propose a 2D/3D registration framework for intraoperative patient registration using a conventional mobile X-ray imager combining fiducial-based C-arm tracking and graphics processing unit (GPU)-acceleration. The two-stage framework 1) acquires X-ray images and estimates relative pose between the images using a custom-made in-image fiducial, and 2) estimates the patient pose using intensity-based 2D/3D registration. Experimental validations using a publicly available gold standard dataset, a plastic bone phantom and cadaveric specimens have been conducted. The mean target registration error (mTRE) was 0.34 ± 0.04 mm (success rate: 100%, registration time: 14.2 s) for the phantom with two images 90° apart, and 0.99 ± 0.41 mm (81%, 16.3 s) for the cadaveric specimen with images 58.5° apart. The experimental results showed the feasibility of the proposed registration framework as a practical alternative for IGS routines.

An active contour method for bone cement reconstruction from C-arm x-ray images

A novel algorithm is presented to segment and reconstruct injected bone cement from a sparse set of X-ray images acquired at arbitrary poses. The sparse X-ray multi-view active contour (SxMAC-pronounced "smack") can 1) reconstruct objects for which the background partially occludes the object in X-ray images, 2) use X-ray images acquired on a noncircular trajectory, and 3) incorporate prior computed tomography (CT) information. The algorithm's inputs are preprocessed X-ray images, their associated pose information, and prior CT, if available. The algorithm initiates automated reconstruction using visual hull computation from a sparse number of X-ray images. It then improves the accuracy of the reconstruction by optimizing a geodesic active contour. Experiments with mathematical phantoms demonstrate improvements over a conventional silhouette based approach, and a cadaver experiment demonstrates SxMAC's ability to reconstruct high contrast bone cement that has been injected into a femur and achieve sub-millimeter accuracy with four images.

Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol.

Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry

The accurate diagnosis of osteoporosis has gained increasing importance due to the aging of our society. Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is an established criterion in the diagnosis of osteoporosis. This measure, however, is limited by its two-dimensionality. This work presents a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image used in clinical routine. A statistical model of the combined shape and BMD distribution is presented, together with a method for its construction from a set of quantitative computed tomography (QCT) scans. A reconstruction is acquired in an intensity based 3D-2D registration process whereby an instance of the model is found that maximizes the similarity between its projection and the DXA image. Reconstruction experiments were performed on the DXA images of 30 subjects, with a model constructed from a database of QCT scans of 85 subjects. The accuracy was evaluated by comparing the reconstructions with the same subject QCT scans. The method presented here can potentially improve the diagnosis of osteoporosis and fracture risk assessment from the low radiation dose and low cost DXA devices currently used in clinical routine.

Personalized X-ray reconstruction of the proximal femur via intensity-based non-rigid 2D-3D registration

This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.