Ehsan Dehghan

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.

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%.

Segmentation of iodine brachytherapy implants in fluoroscopy

PurposeIn 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.MethodsImplanted 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.ResultsGround 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.ConclusionsRobust and accurate iodine seed segmentation can be achieved through the proposed segmentation workflow.

Declustering n -connected components for segmentation of iodine implants in C-arm fluoroscopy images

Dynamic dosimetry is becoming the standard to evaluate the quality of radioactive implants during brachytherapy. It is essential to obtain a 3D visualization of the implanted seeds and their relative position to the prostate. For this, a robust and precise segmentation of the seeds in 2D X-ray is required. First, implanted seeds are segmented using a region-based implicit active contour approach. Then, n-seed clusters are resolved using an efficient template based approach. A collection of 55 C-arm images from 10 patients are used to validate the proposed algorithm. Compared to manual ground-truth segmentation of 6002 seeds, 98.7% of seeds were automatically detected and declustered showing a false-positive rate of only 1.7%. Results indicate the proposed method is able to perform the identification and annotation processes of seeds on par with a human expert, constituting a viable alternative to the traditional manual segmentation approach.

Surface-based registration of liver in ultrasound and CT

Ultrasound imaging is an attractive modality for real-time image-guided interventions. Fusion of US imaging with a diagnostic imaging modality such as CT shows great potential in minimally invasive applications such as liver biopsy and ablation. However, significantly different representation of liver in US and CT turns this image fusion into a challenging task, in particular if some of the CT scans may be obtained without contrast agents. The liver surface, including the diaphragm immediately adjacent to it, typically appears as a hyper-echoic region in the ultrasound image if the proper imaging window and depth setting are used. The liver surface is also well visualized in both contrast and non-contrast CT scans, thus making the diaphragm or liver surface one of the few attractive common features for registration of US and non-contrast CT. We propose a fusion method based on point-to-volume registration of liver surface segmented in CT to a processed electromagnetically (EM) tracked US volume. In this approach, first, the US image is pre-processed in order to enhance the liver surface features. In addition, non-imaging information from the EM-tracking system is used to initialize and constrain the registration process. We tested our algorithm in comparison with a manually corrected vessel-based registration method using 8 pairs of tracked US and contrast CT volumes. The registration method was able to achieve an average deviation of 12.8mm from the ground truth measured as the root mean square Euclidean distance for control points distributed throughout the US volume. Our results show that if the US image acquisition is optimized for imaging of the diaphragm, high registration success rates are achievable.

Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation.

In this paper, we present a learning based, registration free, atlas ranking technique for selecting outperforming atlases prior to image registration and multi-atlas segmentation (MAS). To this end, we introduce ensemble hashing, where each data (image volume) is represented with ensemble of hash codes and a learnt distance metric is used to obviate the need for pairwise registration between atlases and target image. We then pose the ranking process as an assignment problem and solve it through two different combinatorial optimization (CO) techniques. We use 43 unregistered cardiac CT Angiography (CTA) scans and perform thorough validations to show the effectiveness and superiority of the presented technique against existing atlas ranking and selection methods.

EM‐enhanced US‐based seed detection for prostate brachytherapy

PURPOSE Intraoperative dosimetry in low-dose-rate (LDR) permanent prostate brachytherapy requires accurate localization of the implanted seeds with respect to the prostate anatomy. Transrectal Ultrasound (TRUS) imaging, which is the main imaging modality used during the procedure, is not sufficiently robust for accurate seed localization. We present a method for integration of electromagnetic (EM) tracking into LDR prostate brachytherapy procedure by fusing it with TRUS imaging for seed localization. METHOD Experiments were conducted on five tissue mimicking phantoms in a controlled environment. The seeds were implanted into each phantom using an EM-tracked needle, which allowed recording of seed drop locations. After each needle, we reconstructed a 3D ultrasound (US) volume by compounding a series of 2D US images acquired during retraction of an EM-tracked TRUS probe. Then, a difference image was generated by nonrigid registration and subtraction of two consecutive US volumes. A US-only seed detection method was used to detect seed candidates in the difference volume, based on the signature of the seeds. Finally, the EM-based positions of the seeds were used to detect the false positives of the US-based seed detection method and also to estimate the positions of the missing seeds. After the conclusion of the seed implant process, we acquired a CT image. The ground truth for seed locations was obtained by localizing the seeds in the CT image and registering them to the US coordinate system. RESULTS Compared to the ground truth, the US-only detection algorithm achieved a localization error mean of 1.7 mm with a detection rate of 85%. By contrast, the EM-only seed localization method achieved a localization error mean of 3.7 mm with a detection rate of 100%. By fusing EM-tracking information with US imaging, we achieved a localization error mean of 1.8 mm while maintaining a 100% detection rate without any false positives. CONCLUSIONS Fusion of EM-tracking and US imaging for prostate brachytherapy can combine high localization accuracy of US-based seed detection with the robustness and high detection rate of EM-based seed localization. Our phantom experiments serve as a proof of concept to demonstrate the potential value of integrating EM-tracking into LDR prostate brachytherapy.

Automatic detection of aortic dissection in contrast-enhanced CT

Aortic dissection is a condition in which a tear in the inner wall of the aorta allows blood to flow between two layers of the aortic wall. Aortic dissection is associated with severe chest pain and can be deadly. Contrast-enhanced CT is the main modality for detection of aortic dissection. Aortic dissection is one of the target abnormalities during evaluation of a triple rule-out CT in emergency cases. In this paper, we present a method for automatic patient-level detection of aortic dissection. Our algorithm starts by an atlas-based segmentation of the aorta which is used to produce cross-sectional images of the organ. Segmentation refinement, flap detection and shape analysis are employed to detect aortic dissection in these cross-sectional slices. Then, the slice-level results are aggregated to render a patient-level detection result. We tested our algorithm on a data set of 37 contrast-enhanced CT volumes, with 13 cases of aortic dissection. We achieved an accuracy of 83.8%, a sensitivity of 84.6% and a specificity of 83.3%.

CT and MRI fusion for postimplant prostate brachytherapy evaluation

Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a need for manual initialization. Our registration method employs a point-to-volume registration scheme during which localized seeds in the CT images, produced by commercial treatment planning systems as part of the standard of care, are rigidly registered to preprocessed MRI images. We tested our algorithm on ten patient data sets and achieved an overall registration error of 1.6 ± 0.8 mm with a running time of less than 20s. With high registration accuracy and computational speed, and no need for manual intervention, our method has the potential to be employed in clinical applications.

Ultrasound-Fluoroscopy Registration for Intraoperative Dynamic Dosimetry in Prostate Brachytherapy

Low-dose-rate prostate brachytherapy is a treatment option for low- and mid-risk prostate cancer through introduction of radioactive seeds into the prostate. Seed placement deviations are common and associated with postoperative complications. Dynamic dosimetry is a method to accurately localize the true position of the seeds inside the tissue, calculate the delivered dose, and adapt the implant plan accordingly to compensate for seed placement deviations in the operating room. A practical method for dynamic dosimetry relies on localization of the implanted seeds in 3D space from several C-arm images and registering them to a 3D ultrasound volume of the prostate region. In this chapter we introduce a system and workflow for intraoperative dosimetry for prostate brachytherapy. In the suggested workflow, C-arm images are acquired from different angles and are used to reconstruct the seeds in 3D space. For this purpose, we rely on a method based on dimensionality reduced linear programming to match the projections of a seed in different images and localize the seed positions after automatic C-arm pose correction. In the next step of the workflow, the reconstructed seeds are registered to an ultrasound volume of the prostate in a point-to-volume registration scheme. We tested our method on data from 16 patients and compared our dosimetry results with results from Day-1 CT. In comparison, we achieved absolute error of 2.2 ± 1.8 % (mean ± STD) in estimating the percentage of the prostate volume that receives 100 % of the prescribed dose (V100) and absolute error of 10.5 ± 9.5 % in prediction of the minimum dose delivered to 90 % of the prostate (D90).