Showing papers presented at "Computer Assisted Radiology and Surgery in 2012"

A PACS archive architecture supported on cloud services.

Abstract: Purpose Diagnostic imaging procedures have continuously increased over the last decade and this trend may continue in coming years, creating a great impact on storage and retrieval capabilities of current PACS. Moreover, many smaller centers do not have financial resources or requirements that justify the acquisition of a traditional infrastructure. Alternative solutions, such as cloud computing, may help address this emerging need.

Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach

Abstract: Purpose The optimal electrode trajectory is needed to assist surgeons in planning Deep Brain Stimulation (DBS). A method for image-based trajectory planning was developed and tested.

Microsurgical robotic system for vitreoretinal surgery.

Abstract: Purpose Robotics may improve vitreoretinal surgery by steadying hand motion, thereby reducing negative outcomes This study aimed to develop a microsurgical robot for vitreoretinal surgery and to perform clinical procedures using robot-assisted interventions

A multi-modal approach to computer-assisted deep brain stimulation trajectory planning

Abstract: Purpose Both frame-based and frameless approaches to deep brain stimulation (DBS) require planning of insertion trajectories that mitigate hemorrhagic risk and loss of neurological function. Currently, this is done by manual inspection of multiple potential electrode trajectories on MR-imaging data. We propose and validate a method for computer-assisted DBS trajectory planning.

Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique.

Abstract: Purpose An automatic, accurate and fast segmentation of hemorrhage in brain Computed Tomography (CT) images is necessary for quantification and treatment planning when assessing a large number of data sets. Though manual segmentation is accurate, it is time consuming and tedious. Semi-automatic methods need user interactions and might introduce variability in results. Our study proposes a modified distance regularized level set evolution (MDRLSE) algorithm for hemorrhage segmentation.

Evaluation of a portable image overlay projector for the visualisation of surgical navigation data: phantom studies.

Abstract: Introduction Presenting visual feedback for image-guided surgery on a monitor requires the surgeon to perform time-consuming comparisons and diversion of sight and attention away from the patient. Deficiencies in previously developed augmented reality systems for image-guided surgery have, however, prevented the general acceptance of any one technique as a viable alternative to monitor displays. This work presents an evaluation of the feasibility and versatility of a novel augmented reality approach for the visualisation of surgical planning and navigation data. The approach, which utilises a portable image overlay device, was evaluated during integration into existing surgical navigation systems and during application within simulated navigated surgery scenarios.

Robotic system for MRI-guided prostate biopsy: feasibility of teleoperated needle insertion and ex vivo phantom study

Abstract: Purpose Magnetic Resonance Imaging (MRI) combined with robotic assistance has the potential to improve on clinical outcomes of biopsy and local treatment of prostate cancer.

Combination of computer-aided detection algorithms for automatic lung nodule identification

Abstract: Purpose The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

Abstract: Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

Correlation between external and internal respiratory motion: a validation study

Abstract: Purpose In motion-compensated image-guided radiotherapy, accurate tracking of the target region is required. This tracking process includes building a correlation model between external surrogate motion and the motion of the target region. A novel correlation method is presented and compared with the commonly used polynomial model.

Preclinical evaluation of an MRI-compatible pneumatic robot for angulated needle placement in transperineal prostate interventions.

Abstract: Purpose To evaluate the targeting accuracy of a small profile MRI-compatible pneumatic robot for needle placement that can angulate a needle insertion path into a large accessible target volume.

Building an ensemble system for diagnosing masses in mammograms

Abstract: Purpose Classification of a suspicious mass (region of interest, ROI) in a mammogram as malignant or benign may be achieved using mass shape features. An ensemble system was built for this purpose and tested.

Navigated laparoscopic ultrasound in abdominal soft tissue surgery: technological overview and perspectives

Abstract: Purpose Two-dimensinal laparoscopic ultrasound (LUS) is commonly used for many laparoscopic procedures, but 3D LUS and navigation technology are not conventional tools in the clinic. Navigated LUS can help the user understand and interpret the ultrasound images in relation to the laparoscopic view and preoperative images. When combined with information from MRI or CT, navigated LUS has the potential to provide information about anatomic shifts during the procedure. In this paper, we present an overview of the ongoing technological research and development related to LUS combined with navigation technology, The purpose of this overview is threefold: (1) an introduction for those new to the field of navigated LUS; (2) an overview for those working in the field and; and (3) as a reference for those searching for literature on technological developments related to navigation in ultrasound-guided laparoscopic surgery.

Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images

Abstract: Purpose Pulmonary nodules may indicate the early stage of lung cancer, and the progress of lung cancer causes associated changes in the shape and number of pulmonary blood vessels The automatic segmentation of pulmonary nodules and blood vessels is desirable for chest computer-aided diagnosis (CAD) systems Since pulmonary nodules and blood vessels are often attached to each other, conventional nodule detection methods usually produce many false positives (FPs) in the blood vessel regions, and blood vessel segmentation methods may incorrectly segment the nodules that are attached to the blood vessels A method to simultaneously and separately segment the pulmonary nodules and blood vessels was developed and tested

Design and analysis of a head-mounted parallel kinematic device for skull surgery.

Abstract: Purpose Precision skull surgery requires specialized instrumentation to satisfy demanding requirements in cochlear array implantation, deep brain stimulation electrode placement, and related applications. A miniaturized reconfigurable parallel kinematic mechanism which can be directly mounted on a patient’s skull was designed, built, and tested for precision skull surgery.

Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery

Abstract: Purpose We present a new technique for registering magnetic resonance (MR) and ultrasound images in the context of neurosurgery. It involves generating a pseudo-ultrasound (pseudo-US) from a segmented MR image and uses cross-correlation as the cost function to register the pseudo-US to the real ultrasound data. The algorithm’s performance is compared with that of a state-of-the-art technique that uses a median-filtered MR image to register to a Gaussian-blurred ultrasound using a normalized mutual information (NMI) objective function.

Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom.

Abstract: Purpose Minimally invasive treatment of solid cancers, especially in the breast and liver, remains clinically challenging, despite a variety of treatment modalities, including radiofrequency ablation (RFA), microwave ablation or high-intensity focused ultrasound. Each treatment modality has advantages and disadvantages, but all are limited by placement of a probe or US beam in the target tissue for tumor ablation and monitoring. The placement is difficult when the tumor is surrounded by large blood vessels or organs. Patient-specific image-based 3D modeling for thermal ablation simulation was developed to optimize treatment protocols that improve treatment efficacy.

2D/3D image fusion of X-ray mammograms with breast MRI: visualizing dynamic contrast enhancement in mammograms.

Abstract: Purpose Breast cancer is the most common cancer among women. The established screening method to detect breast cancer is X-ray mammography. Additionally, MRI is used for diagnosis in clinical routine. Due to complementary diagnostic information, both modalities are often read in combination. Yet, the correlation is challenging due to different dimensionality of images and different patient positioning. In this paper, we describe a method to fuse X-ray mammograms with DCE-MRI. The present study was conducted to evaluate the feasibility of the approach.

Automatic scoring of virtual mastoidectomies using expert examples.

Abstract: Purpose Automatic scoring of resident performance on a virtual mastoidectomy simulation system is needed to achieve consistent and efficient evaluations. By not requiring immediate expert intervention, the system provides a completely objective assessment of performance as well as a self-driven user assessment mechanism.

Evaluation of 2D and 3D navigation for iliosacral screw fixation

Abstract: Image guidance is essential in some orthopedic surgical procedures, especially iliosacral screw fixation. Currently, there is no consensus regarding the best image guidance technique. An ex-vivo study was performed to compare conventional, 2-dimensional (2D), and 3D imaging techniques and determine the optimal image guidance technique for pelvic surgery. Plastic (n = 9) and donated cadaver pelvises (n = 8) were evaluated in the laboratory. The pelvises were positioned on radiolucent operation tables in a prone position. Transiliosacral screws were inserted without or with 2D- and 3D-navigational support. A digital mobile X-ray unit with flat-panel fluoroscopy and navigation software was used to measure precision, radiation exposure, and time requirements. 2D-navigation resulted in 40% incorrect screw positioning for the cadavers, 6% for the plastic phantoms, and 21% overall. The highest accuracy was accomplished with 3D-navigation (plastic: 100%; cadavers: 83%; p < 0.05). The dose-area product showed that both 2D- and 3D-navigation required increased exposure compared to the conventional technique (p < 0.01). For both plastic and cadaver specimens, navigated techniques required significantly longer times for screw insertion than the conventional technique (p < 0.01). 3D image guidance for transiliosacral screw fixation enabled more accurate screw placement in S1 and S2 vertebrae. However, radiation exposure in 3D-navigation was excessive; thus, we recommend avoiding 3D-navigation in young patients. A primary advantage of 3D-navigation was that the operating team could leave the room during the scan; thus, it reduced their radiation exposure. Moreover, the time required for screw insertion with 3D-navigation was similar to that required in the conventional technique; thus, 3D-navigation is recommended for older patients.

Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans

Abstract: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation. Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features. The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%. Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

An on-board surgical tracking and video augmentation system for C-arm image guidance

Abstract: Conventional tracker configurations for surgical navigation carry a variety of limitations, including limited geometric accuracy, line-of-sight obstruction, and mismatch of the view angle with the surgeon’s-eye view. This paper presents the development and characterization of a novel tracker configuration (referred to as “Tracker-on-C”) intended to address such limitations by incorporating the tracker directly on the gantry of a mobile C-arm for fluoroscopy and cone-beam CT (CBCT). A video-based tracker (MicronTracker, Claron Technology Inc., Toronto, ON, Canada) was mounted on the gantry of a prototype mobile isocentric C-arm next to the flat-panel detector. To maintain registration within a dynamically moving reference frame (due to rotation of the C-arm), a reference marker consisting of 6 faces (referred to as a “hex-face marker”) was developed to give visibility across the full range of C-arm rotation. Three primary functionalities were investigated: surgical tracking, generation of digitally reconstructed radiographs (DRRs) from the perspective of a tracked tool or the current C-arm angle, and augmentation of the tracker video scene with image, DRR, and planning data. Target registration error (TRE) was measured in comparison with the same tracker implemented in a conventional in-room configuration. Graphics processing unit (GPU)-accelerated DRRs were generated in real time as an assistant to C-arm positioning (i.e., positioning the C-arm such that target anatomy is in the field-of-view (FOV)), radiographic search (i.e., a virtual X-ray projection preview of target anatomy without X-ray exposure), and localization (i.e., visualizing the location of the surgical target or planning data). Video augmentation included superimposing tracker data, the X-ray FOV, DRRs, planning data, preoperative images, and/or intraoperative CBCT onto the video scene. Geometric accuracy was quantitatively evaluated in each case, and qualitative assessment of clinical feasibility was analyzed by an experienced and fellowship-trained orthopedic spine surgeon within a clinically realistic surgical setup of the Tracker-on-C. The Tracker-on-C configuration demonstrated improved TRE (0.87 ± 0.25) mm in comparison with a conventional in-room tracker setup (1.92 ± 0.71) mm (p < 0.0001) attributed primarily to improved depth resolution of the stereoscopic camera placed closer to the surgical field. The hex-face reference marker maintained registration across the 180° C-arm orbit (TRE = 0.70 ± 0.32 mm). DRRs generated from the perspective of the C-arm X-ray detector demonstrated sub- mm accuracy (0.37 ± 0.20 mm) in correspondence with the real X-ray image. Planning data and DRRs overlaid on the video scene exhibited accuracy of (0.59 ± 0.38) pixels and (0.66 ± 0.36) pixels, respectively. Preclinical assessment suggested potential utility of the Tracker-on-C in a spectrum of interventions, including improved line of sight, an assistant to C-arm positioning, and faster target localization, while reducing X-ray exposure time. The proposed tracker configuration demonstrated sub- mm TRE from the dynamic reference frame of a rotational C-arm through the use of the multi-face reference marker. Real-time DRRs and video augmentation from a natural perspective over the operating table assisted C-arm setup, simplified radiographic search and localization, and reduced fluoroscopy time. Incorporation of the proposed tracker configuration with C-arm CBCT guidance has the potential to simplify intraoperative registration, improve geometric accuracy, enhance visualization, and reduce radiation exposure.

TREK: an integrated system architecture for intraoperative cone-beam CT-guided surgery

Abstract: Purpose A system architecture has been developed for integration of intraoperative 3D imaging [viz., mobile C-arm cone-beam CT (CBCT)] with surgical navigation (e.g., trackers, endoscopy, and preoperative image and planning data). The goal of this paper is to describe the architecture and its handling of a broad variety of data sources in modular tool development for streamlined use of CBCT guidance in application-specific surgical scenarios.

A Hessian-based filter for vascular segmentation of noisy hepatic CT scans

Abstract: Purpose Extraction and enhancement of tubular structures are important in image processing applications, especially in the analysis of liver CT scans where delineation of vascular structures is needed for surgical planning. Portal vein cross-sections have circular or elliptical shapes, so an algorithm must accommodate both. A vessel segmentation method based on medial-axis points was developed and tested on portal veins in CT images.

Computer-aided simple triage (CAST) for coronary CT angiography (CCTA)

Abstract: Following a recent introduction of computer-aided simple triage (CAST) as a new subclass of computer-aided detection/diagnosis (CAD), we present a CAST software system for a fully automatic initial interpretation of coronary CT angiography (CCTA). We show how the system design and diagnostic performance make it CAST-compliant and suitable for chest pain patient triage in emergency room (ER). The processing performed by the system consists of three major steps: segmentation of coronary artery tree, labeling of major coronary arteries, and detection of significant stenotic lesions (causing > 50% stenosis). In addition, the system performs an automatic image quality assessment to discards low-quality studies. For multiphase studies, the system automatically chooses the best phase for each coronary artery. Clinical evaluation results were collected in 14 independent trials that included more than 2000 CCTA studies. Automatic diagnosis results were compared with human interpretation of the CCTA and to cath lab results. The presented system performs a fully automatic initial interpretation of CCTA without any human interaction and detects studies with significant coronary artery disease. The system demonstrated higher than 90% per patient sensitivity and 40–70% per patient specificity. For the chest pain, ER population, the specificity was 60–70%, yielding higher than 98% NPV. The diagnostic performance of the presented CCTA CAD system meets the CAST requirements, thus enabling efficient, 24/7 utilization of CCTA for chest pain patient triage in ER. This is the first fully operational, clinically validated, CAST-compliant CAD system for a fully automatic analysis of CCTA and detection of significant stenosis.

Online recognition of surgical instruments by information fusion.

Abstract: Automatic online recognition of surgical instruments is required to monitor instrument use for surgical process modeling. A system was developed and tested using available technologies. A recognition system was developed using RFID technology to identify surgical activities. Information fusion for online recognition of surgical process models was conceived as a layer model to abstract information from specific sensor technologies. Redundant, complementary, and cooperative sensor signal fusion was used in the layer model to increase the surgical instrument recognition rate. Several different information fusion strategies were evaluated for situation recognition abilities in a mock-up environment based on simulations of surgical processes. This information fusion system was able to reliably detect, identify, and localize surgical instruments in an interventional suite. A combination of information fusion strategies was able to achieve a correct classification rate of 97% and was as effective as observer-based acquisition methods. Different information fusion strategies for the recognition of surgical instruments were evaluated, showing that redundant, complementary, and cooperative information fusion is feasible for recognition of surgical work steps. A combination of sensor- and observer-based modeling strategies provides the most robust solution for surgical process models.

Electromagnetic tracking for US-guided interventions: standardized assessment of a new compact field generator

Abstract: One of the main challenges related to electromagnetic tracking in the clinical setting is a placement of the field generator (FG) that optimizes the reliability and accuracy of sensor localization. Recently, a new mobile FG for the NDI Aurora® tracking system has been presented. This Compact FG is the first FG that can be attached directly to an ultrasound (US) probe. The purpose of this study was to assess the precision and accuracy of the Compact FG in the presence of nearby mounted US probes. Six different US probes were mounted onto the Compact FG by means of a custom-designed mounting adapter. To assess precision and accuracy of the Compact FG, we employed a standardized assessment protocol. Utilizing a specifically manufactured plate, we measured positional data on three levels of distances from the FG as well as rotational data. While some probes had negligible influence on tracking accuracy two probes increased the mean distance error up to 1.5 mm compared with a reference measurement of 0.5 mm. The jitter error consistently stayed below 0.2 mm in all cases. The mean relative error in orientation was found to be smaller than 3°. Attachment of an US probe to the Compact FG does not have a critical influence on tracking accuracy in most cases. Clinical benefit of this promising mobile FG must be shown in future studies.

Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements.

Abstract: Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation. Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices. The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization. An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface segmentation results were shown to closely approximate manual segmentations.

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Abstract: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user’s side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Mandible reconstruction with patient-specific pre-bent reconstruction plates: comparison of a transfer key method to the standard method—results of an in vitro study

Abstract: Purpose Patient-specific mandible reconstruction plates may be pre-bent to facilitate the surgical procedure. A method using transfer keys (TK) for repositioning pre-bent plates was compared with the standard method on mandibular models to evaluate its feasibility.