REDMAPS: reduced-dimensionality matching for prostate brachytherapy seed reconstruction

Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding

There are clinical needs for optical coherence tomography (OCT) of large areas within a short period of time, such as imaging resected breast tissue for the evaluation of cancer. We report on the use of denoising predictive coding (DN-PC), a novel compressed sensing (CS) algorithm for reconstruction of OCT volumes of human normal breast and breast cancer tissue. The DN-PC algorithm has been rewritten to allow for computational parallelization and efficient memory transfer, resulting in a net reduction of computation time by a factor of 20. We compress image volumes at decreasing A-line sampling rates to evaluate a relation between reconstruction behavior and image features of breast tissue.

The causal interaction in human basal ganglia

The experimental study of the human brain has important restrictions, particularly in the case of basal ganglia, subcortical centers whose activity can be recorded with fMRI methods but cannot be directly modified. Similar restrictions occur in other complex systems such as those studied by Earth system science. The present work studied the cause/effect relationships between human basal ganglia with recently introduced methods to study climate dynamics. Data showed an exhaustive (identifying basal ganglia interactions regardless of their linear, non-linear or complex nature) and selective (avoiding spurious relationships) view of basal ganglia activity, showing a fast functional reconfiguration of their main centers during the execution of voluntary motor tasks. The methodology used here offers a novel view of the human basal ganglia which expands the perspective provided by the classical basal ganglia model and may help to understand BG activity under normal and pathological conditions.

Data-specific mask-guided image reconstruction for diffuse optical tomography

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l₁-regularization based sparse recovery approach.

Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy

We propose a deformable registration algorithm for prostate-specific membrane antigen (PSMA) PET/CT and transrectal ultrasound (TRUS) fusion. Accurate registration of PSMA PET to intraoperative TRUS will allow physicians to customize dose planning based on the regions involved. The inputs to the registration algorithm are the PET/CT and TRUS volumes as well as the prostate segmentations. PET/CT and TRUS volumes are first rigidly registered by maximizing the overlap between the segmented prostate binary masks. Three-dimensional anatomical landmarks are then automatically extracted from the boundary as well as within the prostate. Then, a deformable registration is performed using a regularized thin plate spline where the landmark localization error is optimized between the extracted landmarks that are in correspondence. The proposed algorithm was evaluated on 25 prostate cancer patients treated with low-dose-rate brachytherapy. We registered the postimplant CT to TRUS using the proposed algorithm and computed target registration errors (TREs) by comparing implanted seed locations. Our approach outperforms state-of-the-art methods, with significantly lower ( mean ± standard deviation ) TRE of 1.96 ± 1.29    mm while being computationally efficient (mean computation time of 38 s). The proposed landmark-based PET/CT-TRUS deformable registration algorithm is simple, computationally efficient, and capable of producing quality registration of the prostate boundary as well as the internal gland.

Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy

PURPOSE

Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft-tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C-arm.

METHODS

Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray-based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2-weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mm 3 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT-based seed locations.

RESULTS

The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s.

CONCLUSION

The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry.

Improving DOT reconstruction with a Born iterative method and US-guided sparse regularization

Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising low-cost imaging technique for diagnosis and assessment of breast cancer. US-guided DOT is best implemented in reflection geometry, which can be co-registered with US pulse-echo imaging and also minimizes the tissue depth for adequate light penetration. However, due to intense light scattering, the DOT reconstruction problem is ill-posed. In this communication, we describe a new non-linear Born iterative reconstruction method with US-guided depth-dependent ℓ 1 sparse regularization for improving DOT reconstruction by incorporating a priori lesion depth and shape information from the co-registered US image. Our method iteratively solves the inverse problem by updating the photon-density wave using the finite difference method, computing the weight matrix based on Born approximation, and reconstructing the absorption map using the fast iterative shrinkage-thresholding optimization algorithm (FISTA). We validate our method using both phantom and patient data and compare the results with those using the first order linear Born method. Phantom experiments demonstrate that the non-linear Born method provides more accurate target absorption reconstruction and better resolution than the linear Born method. Clinical studies on 20 patients show that non-linear Born reconstructs more realistic tumor shapes than linear Born, and improves the malignant-to-benign lesion contrast ratio from 2.73 to 3.07 , which is a 12.5 % improvement. For lesions approximately more than 2.0 cm in diameter, the average malignant-to-benign lesion contrast ratio is increased from 2.68 to 3.31 , which is a 23.5 % improvement.

Discordant attributes of structural and functional brain connectivity in a two-layer multiplex network

Several studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine node strength assortativity within and between the SC and FC layer. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may provide clues to understand laterality of some neurological dysfunctions and recovery.

A deformable multimodal image registration using PET/CT and TRUS for intraoperative focal prostate brachytherapy

In this paper, a deformable registration method is proposed that enables automatic alignment of preoperative PET/CT to intraoperative ultrasound in order to achieve PET-determined focal prostate brachytherapy. Novel PET imaging agents such as prostate specific membrane antigen (PSMA) enables highly accurate identification of intra/extra-prostatic tumors. Incorporation of PSMA PET into the standard transrectal ultrasound (TRUS)-guided prostate brachytherapy will enable focal therapy, thus minimizing radiation toxicities. Our registration method requires PET/CT and TRUS volume as well as prostate segmentations. These input volumes are first rigidly registered by maximizing spatial overlap between the segmented prostate volumes, followed by the deformable registration. To achieve anatomically accurate deformable registration, we extract anatomical landmarks from both prostate boundary and inside the gland. Landmarks are extracted along the base-apex axes using two approaches: equiangular and equidistance. Three-dimensional thin-plate spline (TPS)-based deformable registration is then performed using the extracted landmarks as control points. Finally, the PET/CT images are deformed to the TRUS space by using the computed TPS transformation. The proposed method was validated on 10 prostate cancer patient datasets in which we registered post-implant CT to end-of-implantation TRUS. We computed target registration errors (TREs) by comparing the implanted seed positions (transformed CT seeds vs. intraoperatively identified TRUS seeds). The average TREs of the proposed method are 1.98±1.22 mm (mean±standard deviation) and 1.97±1.24 mm for equiangular and equidistance landmark extraction methods, respectively, which is better than or comparable to existing state-of-the-art methods while being computationally more efficient with an average computation time less than 40 seconds.

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging.

Beyond Born-Rytov limit for super-resolution optical diffraction tomography

Optical diffraction tomography (ODT) using Born or Rytov approximation suffers from severe distortions in reconstructed refractive index (RI) tomograms when multiple scattering occurs or the scattering signals are strong. These effects are usually seen as a significant impediment to the application of ODT because multiple scattering is directly linked to an unknown object itself rather than a surrounding medium, and a strong scatter invalidates the underlying assumptions of the Born and Rytov approximations. The focus of this article is to demonstrate for the first time that multiple scattering and high material contrast, if handled aptly, can significantly improve the image quality of the ODT thanks to multiple scattering inside a sample. Experimental verification using various phantom and biological cells substantiates that we not only revealed the structures that were not observable using the conventional approaches but also resolved the long-standing problem of missing cones in the ODT.

Intraoperative Registered Ultrasound and Fluoroscopy (iRUF) for dose calculation during prostate brachytherapy: Improved accuracy compared to standard ultrasound-based dosimetry

BACKGROUND AND PURPOSE

Intraoperative transrectal ultrasound dosimetry during low-dose-rate prostate brachytherapy is imprecise due to sonographic distortion caused by seed echoes and needle tracks that obscure seed positions or create false signals as well as traumatic edema. Here we report the results of a pilot study comparing a combined ultrasound and fluoroscopy-based seed localization method (iRUF) to standard ultrasound-based dosimetry (USD).

MATERIAL AND METHODS

Eighty patients undergoing permanent Pd-103 seed implantation for prostate cancer were prospectively enrolled. Seed implantation was performed using standard USD for intraoperative dose tracking. Upon implant completion, six X-ray images were intraoperatively acquired using a mobile C-arm and transverse ultrasound images of the implanted prostate were also acquired. Three-dimensional seed locations were reconstructed from X-ray images and registered to the ultrasound for iRUF dosimetry. Day 1 CT/MRI scans were performed for post-implant dosimetry. Prostate and urethral dosimetric parameters were separately calculated for analysis on iRUF, USD, and CT/MRI data sets. Differences and similarities between dosimetric values measured by iRUF, USD, and CT/MRI were assessed based on root mean squared differences, intraclass correlation coefficients (ICC), and Wilcoxon signed rank test.

RESULTS

Data from 66 eligible patients were analyzed. Compared to CT/MRI, iRUF dosimetry showed higher correlation with overall ICC of 0.42 (0.01 for USD) and significantly smaller root mean squared differences (overall 16.5 vs 21.5 for iRUF and USD) than USD for all prostate and urethral dosimetric parameters examined. USD demonstrated a tendency to overestimate dose to the prostate when compared to iRUF.

CONCLUSIONS

iRUF approximated post-implant CT/MRI prostate and urethral dosimetry to a greater degree than USD. A phase II trial utilizing iRUF for intraoperative dynamic plan modification is underway, with the goal to confirm capability to minimize and correct for prostate underdosage not otherwise detected.

Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification

Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l₁-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network. Besides, the conventional sparse regularization often overlooks group structure in the brain network, i.e., a set of links (or connections) sharing similar attribute. To address these issues, we propose to construct BFCN by integrating both link strength and group structure information. Specifically, a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity, (2) link strength, and (3) group structure, in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics, as demonstrated by superior MCI classification accuracy of 81.8%. Moreover, our method is promising for its capability in modeling more biologically meaningful sparse brain networks, which will benefit both basic and clinical neuroscience studies.

Dimensionality Reduction Based Optimization Algorithm for Sparse 3-D Image Reconstruction in Diffuse Optical Tomography

Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.

Deformable registration of X-ray to MRI for post-implant dosimetry in prostate brachytherapy

Post-implant dosimetric assessment in prostate brachytherapy is typically performed using CT as the standard imaging modality. However, poor soft tissue contrast in CT causes significant variability in target contouring, resulting in incorrect dose calculations for organs of interest. CT-MR fusion-based approach has been advocated taking advantage of the complementary capabilities of CT (seed identification) and MRI (soft tissue visibility), and has proved to provide more accurate dosimetry calculations. However, seed segmentation in CT requires manual review, and the accuracy is limited by the reconstructed voxel resolution. In addition, CT deposits considerable amount of radiation to the patient. In this paper, we propose an X-ray and MRI based post-implant dosimetry approach. Implanted seeds are localized using three X-ray images by solving a combinatorial optimization problem, and the identified seeds are registered to MR images by an intensity-based points-to-volume registration. We pre-process the MR images using geometric and Gaussian filtering. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine transformation and local deformable registration. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. We tested our algorithm on six patient data sets, achieving registration error of (1.2±0.8) mm in < 30 sec. Our proposed approach has the potential to be a fast and cost-effective solution for post-implant dosimetry with equivalent accuracy as the CT-MR fusion-based approach.

Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography

Conventional reconstruction of diffuse optical tomography (DOT) is based on the Tikhonov regularization and the white Gaussian noise assumption. Consequently, the reconstructed DOT images usually have a low spatial resolution. In this work, we have derived a novel quantification method for noise variance based on the linear Rytov approximation of the photon diffusion equation. Specifically, we have implemented this quantification of noise variance to normalize the measurement signals from all source-detector channels along with sparsity regularization to provide high-quality DOT images. Multiple experiments from computer simulations and laboratory phantoms were performed to validate and support the newly developed algorithm. The reconstructed images demonstrate that quantification and normalization of noise variance with sparsity regularization (QNNVSR) is an effective reconstruction approach to greatly enhance the spatial resolution and the shape fidelity for DOT images. Since noise variance can be estimated by our derived expression with relatively limited resources available, this approach is practically useful for many DOT applications.

An image-guidance system for dynamic dose calculation in prostate brachytherapy using ultrasound and fluoroscopy

PURPOSE

Brachytherapy is a standard option of care for prostate cancer patients but may be improved by dynamic dose calculation based on localized seed positions. The American Brachytherapy Society states that the major current limitation of intraoperative treatment planning is the inability to localize the seeds in relation to the prostate. An image-guidance system was therefore developed to localize seeds for dynamic dose calculation.

METHODS

The proposed system is based on transrectal ultrasound (TRUS) and mobile C-arm fluoroscopy, while using a simple fiducial with seed-like markers to compute pose from the nonencoded C-arm. Three or more fluoroscopic images and an ultrasound volume are acquired and processed by a pipeline of algorithms: (1) seed segmentation, (2) fiducial detection with pose estimation, (3) seed matching with reconstruction, and (4) fluoroscopy-to-TRUS registration.

RESULTS

The system was evaluated on ten phantom cases, resulting in an overall mean error of 1.3 mm. The system was also tested on 37 patients and each algorithm was evaluated. Seed segmentation resulted in a 1% false negative rate and 2% false positive rate. Fiducial detection with pose estimation resulted in a 98% detection rate. Seed matching with reconstruction had a mean error of 0.4 mm. Fluoroscopy-to-TRUS registration had a mean error of 1.3 mm. Moreover, a comparison of dose calculations between the authors' intraoperative method and an independent postoperative method shows a small difference of 7% and 2% forD90 and V100, respectively. Finally, the system demonstrated the ability to detect cold spots and required a total processing time of approximately 1 min.

CONCLUSIONS

The proposed image-guidance system is the first practical approach to dynamic dose calculation, outperforming earlier solutions in terms of robustness, ease of use, and functional completeness.

Automatic seed localization of iodine brachytherapy implants from CT images

Radioactive seed implantation has emerged as an effective treatment modality where small radioactive seeds are implanted into the target organ to eradicate the cancer by emitting radiation. Precise seeds localization can indicate whether those seeds deliver sufficient doses of radiation. However, it is challenging and laborious to identify all seeds manually in a short time. Therefore, our purpose in this study was to develop an automatic technique for identifying implanted seeds on any parts of body. The algorithm relies on a 3D adaptive median filter to remove bone structure; white top-hat transform to extract seeds-like objects and further seed classification analysis based on size, shape and their connection etc. Preliminary results on ten patients and seven simulated data show that this approach to be effective and accurate. It resulted in a 96.9 % detection rate with a corresponding 4.7 % false-positive rate for clinical data; a 98.5 % detection rate with a corresponding 4.1 % false-positive rate for the simulated data; and sub-millimeter accuracy for both data sets. This method can achieve robust and accurate seed segmentation through the proposed workflow.

C-arm angle measurement with accelerometer for brachytherapy: an accuracy study

PURPOSE

X-ray fluoroscopy guidance is frequently used in medical interventions. Image-guided interventional procedures that employ localization for registration require accurate information about the C-arm's rotation angle that provides the data externally in real time. Optical, electromagnetic, and image-based pose tracking systems have limited convenience and accuracy. An alternative method to recover C-arm orientation was developed using an accelerometer as tilt sensor.

METHODS

The fluoroscopic C-arm's orientation was estimated using a tri-axial acceleration sensor mounted on the X-ray detector as a tilt sensor. When the C-arm is stationary, the measured acceleration direction corresponds to the gravitational force direction. The accelerometer was calibrated with respect to the C-arm's rotation along its two axes, using a high-accuracy optical tracker as a reference. The scaling and offset error of the sensor was compensated using polynomial fitting. The system was evaluated on a GE OEC 9800 C-arm. Results obtained by accelerometer, built-in sensor, and image-based tracking were compared, using optical tracking as ground truth data.

RESULTS

The accelerometer-based orientation measurement error for primary angle rotation was -0.1 ± 0.0° and for secondary angle rotation it was 0.1 ± 0.0°. The built-in sensor orientation measurement error for primary angle rotation was -0.1 ± 0.2°, and for secondary angle rotation it was 0.1 ± 0.2°. The image-based orientation measurement error for primary angle rotation was -0.1 ± 1.3°, and for secondary angle rotation it was -1.3 ± 0.3°.

CONCLUSION

The accelerometer provided better results than the built-in sensor and image-based tracking. The accelerometer sensor is small, inexpensive, covers the full rotation range of the C-arm, does not require line of sight, and can be easily installed to any mobile X-ray machine. Therefore, accelerometer tilt sensing is a very promising applicant for orientation angle tracking of C-arm fluoroscopes.

Ultrasound-fluoroscopy registration for prostate brachytherapy dosimetry

Prostate brachytherapy is a treatment for prostate cancer using radioactive seeds that are permanently implanted in the prostate. The treatment success depends on adequate coverage of the target gland with a therapeutic dose, while sparing the surrounding tissue. Since seed implantation is performed under transrectal ultrasound (TRUS) imaging, intraoperative localization of the seeds in ultrasound can provide physicians with dynamic dose assessment and plan modification. However, since all the seeds cannot be seen in the ultrasound images, registration between ultrasound and fluoroscopy is a practical solution for intraoperative dosimetry. In this manuscript, we introduce a new image-based nonrigid registration method that obviates the need for manual seed segmentation in TRUS images and compensates for the prostate displacement and deformation due to TRUS probe pressure. First, we filter the ultrasound images for subsequent registration using thresholding and Gaussian blurring. Second, a computationally efficient point-to-volume similarity metric, an affine transformation and an evolutionary optimizer are used in the registration loop. A phantom study showed final registration errors of 0.84 ± 0.45 mm compared to ground truth. In a study on data from 10 patients, the registration algorithm showed overall seed-to-seed errors of 1.7 ± 1.0 mm and 1.5 ± 0.9 mm for rigid and nonrigid registration methods, respectively, performed in approximately 30s per patient.

Segmentation of iodine brachytherapy implants in fluoroscopy

PURPOSE

In prostate brachytherapy, intraoperative dosimetry would allow for evaluation of the implant quality while the patient is still in treatment position. Such a mechanism, however, requires 3-D visualization of the deposited seeds relative to the prostate. It follows that accurate and robust seed segmentation is of critical importance in achieving intraoperative dosimetry.

METHODS

Implanted iodine brachytherapy seeds are segmented via a region-based implicit active contour model. Overlapping seed groups are then resolved using a template-based declustering technique.

RESULTS

Ground truth seed coordinates were obtained through manual segmentation. A total of 57 clinical C-arm images from 10 patients were used to validate the proposed algorithm. This resulted in two failed images and a 96.0% automatic detection rate with a corresponding 2.2% false-positive rate in the remaining 55 images. The mean centroid error between the manual and automatic segmentations was 1.2 pixels.

CONCLUSIONS

Robust and accurate iodine seed segmentation can be achieved through the proposed segmentation workflow.

Prostate implant reconstruction from C-arm images with motion-compensated tomosynthesis

PURPOSE

Accurate localization of prostate implants from several C-arm images is necessary for ultrasound-fluoroscopy fusion and intraoperative dosimetry. The authors propose a computational motion compensation method for tomosynthesis-based reconstruction that enables 3D localization of prostate implants from C-arm images despite C-arm oscillation and sagging.

METHODS

Five C-arm images are captured by rotating the C-arm around its primary axis, while measuring its rotation angle using a protractor or the C-arm joint encoder. The C-arm images are processed to obtain binary seed-only images from which a volume of interest is reconstructed. The motion compensation algorithm, iteratively, compensates for 2D translational motion of the C-arm by maximizing the number of voxels that project on a seed projection in all of the images. This obviates the need for C-arm full pose tracking traditionally implemented using radio-opaque fiducials or external trackers. The proposed reconstruction method is tested in simulations, in a phantom study and on ten patient data sets.

RESULTS

In a phantom implanted with 136 dummy seeds, the seed detection rate was 100% with a localization error of 0.86 ± 0.44 mm (Mean ± STD) compared to CT. For patient data sets, a detection rate of 99.5% was achieved in approximately 1 min per patient. The reconstruction results for patient data sets were compared against an available matching-based reconstruction method and showed relative localization difference of 0.5 ± 0.4 mm.

CONCLUSIONS

The motion compensation method can successfully compensate for large C-arm motion without using radio-opaque fiducial or external trackers. Considering the efficacy of the algorithm, its successful reconstruction rate and low computational burden, the algorithm is feasible for clinical use.

Point-to-volume registration of prostate implants to ultrasound

Ultrasound-Fluoroscopy fusion is a key step toward intraoperative dosimetry for prostate brachytherapy. We propose a method for intensity-based registration of fluoroscopy to ultrasound that obviates the need for seed segmentation required for seed-based registration. We employ image thresholding and morphological and Gaussian filtering to enhance the image intensity distribution of ultrasound volume. Finally, we find the registration parameters by maximizing a point-to-volume similarity metric. We conducted an experiment on a ground truth phantom and achieved registration error of 0.7 +/- 0.2 mm. Our clinical results on 5 patient data sets show excellent visual agreement between the registered seeds and the ultrasound volume with a seed-to-seed registration error of 1.8 +/- 0.9mm. With low registration error, high computational speed and no need for manual seed segmentation, our method is promising for clinical application.

Intraoperative 3D reconstruction of prostate brachytherapy implants with automatic pose correction

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland, and the capability of intraoperatively localizing implanted seeds provides potential for dose evaluation and optimization during therapy. REDMAPS is a recently reported algorithm that carries out seed localization by detecting, matching and reconstructing seeds in only a few seconds from three acquired x-ray images (Lee et al 2011 IEEE Trans. Med. Imaging 29 38-51). In this paper, we present an automatic pose correction (APC) process that is combined with REDMAPS to allow for both more accurate seed reconstruction and the use of images with relatively large pose errors. APC uses a set of reconstructed seeds as a fiducial and corrects the image pose by minimizing the overall projection error. The seed matching and APC are iteratively computed until a stopping condition is met. Simulations and clinical studies show that APC significantly improves the reconstructions with an overall average matching rate of ⩾99.4%, reconstruction error of ⩽0.5 mm, and the matching solution optimality of ⩾99.8%.

Automatic segmentation of radiographic fiducial and seeds from X-ray images in prostate brachytherapy

Prostate brachytherapy guided by transrectal ultrasound is a common treatment option for early stage prostate cancer. Prostate cancer accounts for 28% of cancer cases and 11% of cancer deaths in men with 217,730 estimated new cases and 32,050 estimated deaths in 2010 in the United States alone. The major current limitation is the inability to reliably localize implanted radiation seeds spatially in relation to the prostate. Multimodality approaches that incorporate X-ray for seed localization have been proposed, but they require both accurate tracking of the imaging device and segmentation of the seeds. Some use image-based radiographic fiducials to track the X-ray device, but manual intervention is needed to select proper regions of interest for segmenting both the tracking fiducial and the seeds, to evaluate the segmentation results, and to correct the segmentations in the case of segmentation failure, thus requiring a significant amount of extra time in the operating room. In this paper, we present an automatic segmentation algorithm that simultaneously segments the tracking fiducial and brachytherapy seeds, thereby minimizing the need for manual intervention. In addition, through the innovative use of image processing techniques such as mathematical morphology, Hough transforms, and RANSAC, our method can detect and separate overlapping seeds that are common in brachytherapy implant images. Our algorithm was validated on 55 phantom and 206 patient images, successfully segmenting both the fiducial and seeds with a mean seed segmentation rate of 96% and sub-millimeter accuracy.

Brachytherapy seed reconstruction with joint-encoded C-arm single-axis rotation and motion compensation

C-arm fluoroscopy images are frequently used for qualitative assessment of prostate brachytherapy. Three-dimensional seed reconstruction from C-arm images is necessary for intraoperative dosimetry and quantitative assessment. Seed reconstruction requires accurately known C-arm poses. We propose to measure the C-arm rotation angles and computationally compensate for inevitable C-arm motion to compute the pose. We compensate the translational motions of a C-arm, such as oscillation, sagging and wheel motion using a three-level optimization algorithm and obviate the need for full pose tracking using external trackers or fiducials. We validated our approach on simulated and 100 clinical data sets from 10 patients and gained on average, a seed matching rate of 98.5%, projection error of 0.33 mm (STD=0.21 mm) and computation time of 19.8s per patient, which must be considered as clinically excellent results. We also show that without motion compensation the reconstruction is likely to fail.