David S. Paik

Noninvasive detection of therapeutic cytolytic T cells with 18F-FHBG PET in a patient with glioma.

Background A 57-year-old man had been diagnosed with grade IV glioblastoma multiforme and was enrolled in a trial of adoptive cellular immunotherapy. The trial involved infusion of ex vivo expanded autologous cytolytic CD8+ T cells (CTLs), genetically engineered to express the interleukin 13 zetakine gene (which encodes a receptor protein that targets these T cells to tumor cells) and the herpes simplex virus 1 thymidine kinase (HSV1 tk) suicide gene, and PET imaging reporter gene.Investigations MRI, whole-body and brain PET scan with 18F-radiolabelled 9-[4-fluoro-3-(hydroxymethyl)butyl]guanine (18F–FHBG) to detect CTLs that express HSV1 tk, and safety monitoring after injection of 18F–FHBG.Diagnosis MRI detected grade III–IV glioblastoma multiforme plus two tumors recurrences that developed after resection of the initial tumor.Management Surgical resection of primary glioblastoma tumor, enrollment in CTL therapy trial, reresection of glioma recurrences, infusion of approximately 1 × 109 CTLs into the site of tumor reresection, and 18F–FHBG PET scan to detect infused CTLs.

Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography

Adenomatous polyps in the colon are believed to be the precursor to colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided detection of polyps in computed tomography (CT) colonography (virtual colonoscopy), a technique in which polyps are imaged along the wall of the air-inflated, cleansed colon with X-ray CT. Initial work with computer aided detection has shown high sensitivity, but at a cost of too many false positives. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and uses this information for the classification of the new cases. One of the main contributions of the paper is the new three-dimensional pattern processing approach, called random orthogonal shape sections method, which combines the information from many random images to generate reliable signatures of shape. The input to the proposed system is a collection of volume data from candidate polyps obtained by a high-sensitivity, low-specificity system that we developed previously. The results of our tenfold cross-validation experiments show that, on the average, the system increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity level of 1.0 (0.95).

Automated flight path planning for virtual endoscopy

In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images.

Adaptive Border Marching Algorithm: Automatic Lung Segmentation on Chest CT Images

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Visualization modes for CT colonography using cylindrical and planar map projections.

PURPOSE The purpose of this study was to demonstrate the limitations to the effectiveness of CT colonography, colloquially called virtual colonoscopy (VC), for detecting polyps in the colon and to describe a new technique, map projection CT colonography using Mercator projection and stereographic projection, that overcomes these limitations. METHOD In one experiment, data sets from nine patients undergoing CT colonography were analyzed to determine the percentage of the mucosal surface visible in various visualization modes as a function of field of view (FOV). In another experiment, 40 digitally synthesized polyps of various sizes (10, 7, 5, and 3.5 mm) were randomly inserted into four copies of one patient data set. Both Mercator and stereographic projections were used to visualize the surface of the colon of each data set. The sensitivity and positive predictive value (PPV) were calculated and compared with the results of an earlier study of visualization modes using the same CT colonography data. RESULTS The percentage of mucosal surface visualized by VC increases with greater FOV but only approaches that of map projection VC (98.8%) at a distorting, very high FOV. For both readers and polyp sizes of > or =7 mm, sensitivity for Mercator projection (87.5%) and stereographic projection (82.5%) was significantly greater (p < 0.05) than for viewing axial slices (62.5%), and Mercator projection was significantly more sensitive than VC (67.5%). Mercator and stereographic projection had PPVs of 75.4 and 78.9%, respectively. CONCLUSION The sensitivity of conventional CT colonography is limited by the percentage of the mucosal surface seen. Map projection CT colonography overcomes this problem and provides a more sensitive method with a high PPV for detecting polyps than other methods currently being investigated.

Edge displacement field-based classification for improved detection of polyps in CT colonography

Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).

Medical diagnostic ultrasound system and method for flow analysis

Medical diagnostic ultrasound methods and systems for automated flow analysis are provided. Multiple cross-sectional areas along a vessel are determined automatically. A processor locates an abnormality as a function of the multiple cross-sectional areas, such as identifying a cross-sectional area that is a threshold amount less than an average cross-sectional area. The abnormal area is highlighted on the display to assist with medical diagnosis. For the carotid artery, the interior and exterior branches are labeled to assist medical diagnosis. The two branches are automatically identified. The branch associated with additional small branches is identified as the exterior carotid.

Computed Tomography Colonography: Feasibility of Computer-Aided Polyp Detection in a "First Reader" Paradigm

Objective: To determine the feasibility of a computer-aided detection (CAD) algorithm as the “first reader” in computed tomography colonography (CTC). Methods: In phase 1 of a 2-part blind trial, we measured the performance of 3 radiologists reading 41 CTC studies without CAD. In phase 2, readers interpreted the same cases using a CAD list of 30 potential polyps. Results: Unassisted readers detected, on average, 63% of polyps ≥10 mm in diameter. Using CAD, the sensitivity was 74% (not statistically different). Per-patient analysis showed a trend toward increased sensitivity for polyps ≥10 mm in diameter, from 73% to 90% with CAD (not significant) without decreasing specificity. Computer-aided detection significantly decreased interobserver variability (P = 0.017). Average time to detection of the first polyp decreased significantly with CAD, whereas total reading case reading time was unchanged. Conclusion: Computer-aided detection as a first reader in CTC was associated with similar per-polyp and per-patient detection sensitivity to unassisted reading. Computer-aided detection decreased interobserver variability and reduced the time required to detect the first polyp.

Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: Pilot study

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.