Atilla Peter Kiraly

Three-dimensional path planning for virtual bronchoscopy

Multidetector computed-tomography (MDCT) scanners provide large high-resolution three-dimensional (3-D) images of the chest. MDCT scanning, when used in tandem with bronchoscopy, provides a state-of-the-art approach for lung-cancer assessment. We have been building and validating a lung-cancer assessment system, which enables virtual-bronchoscopic 3-D MDCT image analysis and follow-on image-guided bronchoscopy. A suitable path planning method is needed, however, for using this system. We describe a rapid, robust method for computing a set of 3-D airway-tree paths from MDCT images. The method first defines the skeleton of a given segmented 3-D chest image and then performs a multistage refinement of the skeleton to arrive at a final tree structure. The tree consists of a series of paths and branch structural data, suitable for quantitative airway analysis and smooth virtual navigation. A comparison of the method to a previously devised path-planning approach, using a set of human MDCT images, illustrates the efficacy of the method. Results are also presented for human lung-cancer assessment and the guidance of bronchoscopy.

Extraction of the hepatic vasculature in rats using 3-D micro-CT images

High-resolution micro-computed tomography (CT) scanners now exist for imaging small animals. In particular, such a scanner can generate very large three-dimensional (3-D) digital images of the rats hepatic vasculature. These images provide data on the overall structure and function of such complex vascular trees. Unfortunately, human operators have extreme difficulty in extracting the extensive vasculature contained in the images. Also, no suitable tree representation exists that permits straightforward structural analysis and information retrieval. This work proposes an automatic procedure for extracting and representing such a vascular tree. The procedure is both computation and memory efficient and runs on current PCs. As the results demonstrate, the procedure faithfully follows human-defined measurements and provides far more information than can be defined interactively.

Hepatocellular Carcinoma: Response to TACE Assessed with Semiautomated Volumetric and Functional Analysis of Diffusion-weighted and Contrast-enhanced MR Imaging Data

PURPOSE To determine the association of early changes in posttreatment apparent diffusion coefficient (ADC) and venous enhancement (VE) with tumor size change after transarterial chemoembolization (TACE) by using an investigational semiautomated software. MATERIALS AND METHODS This retrospective HIPAA-compliant study was approved by the institutional review board, with waiver of informed consent. Patients underwent magnetic resonance (MR) imaging at 1.5 T before TACE, as well as 1 and 6 months after TACE. Volumetric analysis of change in ADC and VE 1 month after TACE compared with pretreatment values was performed in 48 patients with 71 hepatocellular carcinoma (HCC) lesions. Diagnostic accuracy was evaluated with receiver operating characteristic (ROC) analysis, using tumor response at 6 months according to Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST as end points. RESULTS According to RECIST criteria, 6 months after TACE, 30 HCC lesions showed partial response (PR), 35 showed stable disease (SD), and six showed progressive disease (PD). Increase in ADC and decrease in VE 1 month after TACE were significantly different between PR, SD, and PD. At area under the ROC curve (AUC) analysis of the ADC increase, there was an AUC of 0.78 for distinguishing PR from SD and PD and an AUC of 0.89 for distinguishing PR and SD from PD. The AUC for decrease in VE was 0.73 for discrimination of PR from SD and PD and 0.90 for discrimination of PR and SD from PD. CONCLUSION Volumetric analysis of increase in ADC and decrease in VE 1 month after TACE can provide an early assessment of response to treatment. Volumetric analysis of multiparametric MR imaging data may have potential as a prognostic biomarker for patients undergoing local-regional treatment of liver cancer.

Computer-based system for the virtual-endoscopic guidance of bronchoscopy

The standard procedure for diagnosing lung cancer involves two stages: three-dimensional (3D) computed-tomography (CT) image assessment, followed by interventional bronchoscopy. In general, the physician has no link between the 3D CT image assessment results and the follow-on bronchoscopy. Thus, the physician essentially performs bronchoscopic biopsy of suspect cancer sites blindly. We have devised a computer-based system that greatly augments the physicians vision during bronchoscopy. The system uses techniques from computer graphics and computer vision to enable detailed 3D CT procedure planning and follow-on image-guided bronchoscopy. The procedure plan is directly linked to the bronchoscope procedure, through a live registration and fusion of the 3D CT data and bronchoscopic video. During a procedure, the system provides many visual tools, fused CT-video data, and quantitative distance measures; this gives the physician considerable visual feedback on how to maneuver the bronchoscope and where to insert the biopsy needle. Central to the system is a CT-video registration technique, based on normalized mutual information. Several sets of results verify the efficacy of the registration technique. In addition, we present a series of test results for the complete system for phantoms, animals, and human lung-cancer patients. The results indicate that not only is the variation in skill level between different physicians greatly reduced by the system over the standard procedure, but that biopsy effectiveness increases.

Reproducibility of Dynamic Contrast-enhanced MR Imaging. Part II. Comparison of Intra- and Interobserver Variability with Manual Region of Interest Placement versus Semiautomatic Lesion Segmentation and Histogram Analysis

PURPOSE To compare the inter- and intraobserver variability with manual region of interest (ROI) placement versus that with software-assisted semiautomatic lesion segmentation and histogram analysis with respect to quantitative dynamic contrast material-enhanced (DCE) MR imaging determinations of the volume transfer constant (K(trans)). MATERIALS AND METHODS The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Fifteen DCE MR imaging studies of the female pelvis defined the study group. Uterine fibroids were used as a perfusion model. Three varying types of lesion measurements were performed by five readers on each study by using DCE MR imaging perfusion analysis software with manual ROI placement and a semiautomatic lesion segmentation and histogram analysis solution. Intra- and interreader variability of measurements of K(trans) with the different measurement types was calculated. RESULTS The overall interobserver variability of K(trans) with manual ROI placement (mean, 28.5% ± 9.3) was reduced by 42.5% when the semiautomatic, software-assisted lesion measurement method was used (16.4% ± 6.2). Whole-lesion measurement showed the lowest interobserver variability with both measurement methods (20.1% ± 4.3 with the manual method vs 10.8% ± 2.6 with the semiautomatic method). The overall intrareader variability with the manual ROI method (7.6% ± 10.6) was not significantly different from that with the semiautomatic method (7.3% ± 10.8), but the intraclass correlation coefficient for intrareader reproducibility improved from 0.86 overall with the manual method to 0.99 with the semiautomatic method. CONCLUSION A semiautomatic lesion segmentation and histogram analysis approach can provide a significant reduction in interobserver variability for DCE MR imaging measurements of K(trans) when compared with manual ROI methods, whereas intraobserver reproducibility is improved to some extent.

Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer.

PURPOSE To compare histogram analysis of voxel-based whole-lesion (WL) enhancement to qualitative assessment and region-of-interest (ROI)-based enhancement analysis in discriminating the renal cell cancer (RCC) subtype clear cell RCC (ccRCC) from papillary RCC (pRCC). MATERIALS AND METHODS In this institutional review board-approved, HIPAA-compliant retrospective study, 73 patients underwent magnetic resonance (MR) imaging prior to surgery for RCC between January 2007 and January 2010. Three-dimensional fat-suppressed T1-weighted gradient-echo corticomedullary phase acquisitions, obtained before and after contrast agent administration, were transferred to a workstation at which automated registration followed by semiautomated segmentation of the RCC was performed. Percent enhancement was computed on a per-voxel basis: (SI(post) - SI(pre))/SI(pre) .100, where SI(pre) and SI(post) indicate signal intensity before and after contrast enhancement, respectively. The WL quantitative parameters of mean, median, and third quartile enhancement and histogram distribution parameters kurtosis and skewness were computed for each lesion. WL enhancement parameters were compared with ROI-based analysis and qualitative assessment with regards to diagnostic accuracy and interreader agreement in differentiating ccRCC from pRCC. RESULTS There were 19 pRCCs and 55 ccRCCs at pathologic examination. ccRCC had significantly higher WL mean, median, and third quartile enhancement compared with pRCC and hade significantly lower kurtosis and skewness (all P < .001). Third quartile enhancement had the highest accuracy (94.6%; area under the curve, 0.980) in discriminating ccRCC from pRCC, which was significantly higher than the accuracy of qualitative assessment (86.0%; P = .04) but not significantly higher than that of ROI enhancement (89.2%; P = .52). WL enhancement parameters had higher interreader agreement (κ = 0.91-1.0) compared with ROI enhancement or qualitative assessment (κ = 0.83 and 0.7, respectively) in discriminating ccRCC from pRCC. CONCLUSION WL enhancement histogram analysis is feasible and can potentially be used to differentiate ccRCC from pRCC with high accuracy. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12111281/-/DC1.

Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions

Virtual bronchoscopy (VB) has emerged as a paradigm for more effective 3D CT image evaluation. Systematic evaluation of a 3D CT chest image using VB techniques, however, requires precomputed guidance data. This guidance data takes the form of central axes, or centerlines, through the major airways. We propose an axes-generation algorithm for VB assessment of 3D CT chest images. For a typical high-resolution 3D CT chest image, the algorithm produces a series of airway-tree axes, corresponding airway cross-sectional area measurements, and a segmented airway tree in a few minutes on a standard PC. Results for phantom and human airway-obstruction cases demonstrate the efficacy of the algorithm. Also, the algorithm is demonstrated in the context of VB-based 3D CT assessment.

Fuzzy pulmonary vessel segmentation in contrast enhanced CT data

Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

A novel multipurpose tree and path matching algorithm with application to airway trees

Tree matching methods have numerous applications in medical imaging, including registration, anatomical labeling, segmentation, and navigation of structures such as vessels and airway trees. Typical methods for tree matching rely on conventional graph matching techniques and therefore suffer potential limitations such as sensitivity to the accuracy of the extracted tree structures, as well as dependence on the initial alignment. We present a novel path-based tree matching framework independent of graph matching. It is based on a point-by-point feature comparison of complete paths rather than branch points, and consequently is relatively unaffected by spurious airways and/or missing branches. A matching matrix is used to enforce one-to-one matching. Moreover our method can reliably match irregular tree structures, resulting from imperfect segmentation and centerline extraction. Also reflecting the nature of these features, our method does not require a precise alignment or registration of tree structures. To test our method we used two thoracic CT scans from each of ten patients, with a median inter-scan interval of 3 months (range 0.5 to 10 months). The bronchial tree structure was automatically extracted from each scan and a ground truth of matching paths was established between each pair of tree structures. Overall 87% of 702 airway paths (average 35.1 per patient matched both ways) were correctly matched using this technique. Based on this success we also present preliminary results of airway-to-artery matching using our proposed methodology.

A novel method for pulmonary emboli visualization from high-resolution CT images

©2004 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: DOI Link: http://dx.doi.org/10.1117/12.532892