Colorectal carcinoma is the third most commonly diagnosed cancer and the second leading cause of death from cancer in the United States. Often it is diagnosed at an advanced stage, after the patient has developed symptoms, explaining its high mortality rate. Since most colon cancers arise from polyps over a 5 to 15 year period of malignant transformation, screening programs to detect small polyps have been advocated. Unfortunately most people do not follow this recommendation. The health relatedness of this project is to dramatically increase the number of people willing to participate in screening programs by developing a convenient, nearly risk-free method of detecting small polyps. Computed tomography colonography (CTC) or CT-based virtual colonoscopy (VC) has shown a comparable performance to the gold-standard optical colonoscopy (OC) for detecting polyps of 8mm and larger by a less invasive manner. To be a screening tool, current CTC has to be advanced for acceptance by a larger population with higher detection capability on smaller polyps. These needs are documented by the recent refusal of CTC being accepted by Medicare coverage, such as reducing CT radiation, detecting smaller polyps, relieving bowel preparation (BP) stress, minimizing reader variation, improving efficiency, etc. We have made good progress in technical advancement toward the broad, long-term objective of this project, i.e., developing CTC as a safe, accurate, cost-effective, minimal-invasive, least-stressful screening modality. To advance CTC toward the long-term objective, the specific aims of this project renewal are: (1) To develop and evaluate an adaptive statistical reconstruction algorithm to obtain the current abdominal CT image quality at as low mAs level as achievable to minimize the risk of X-ray exposure. The algorithm will consider not only the X-ray quanta statistics and energy spectrum, but also the system background noise because this noise plays a noticeable role at low mAs level. (2) To develop and evaluate a partial-volume (PV) statistical segmentation algorithm with correction of fecal tagging inhomogeneity to improve electronic colon cleansing (ECC). The improvement will lead to the detection of smaller polyps, reduction of CT radiation by a half, and relief of patient stress on BP by approaching toward cathartic-free CTC. (3) To develop and evaluate an ECC-adaptive, texture-based, feature-extraction algorithm to improve computer-aided polyp detection (CADpolyp) toward enhancement of CTC cost-effectiveness. It is hoped that an improved performance with higher detection capability on smaller polyps and more acceptable by a large population (via less radiation and stress) would advance CTC toward a screening modality with acceptance by Medicare coverage. The low-mAs statistical reconstruction would be helpful to other CT applications. The PV image segmentation with correction of inhomogeneity would be beneficial to other virtual endoscopy applications, such as extracting blood vessel wall for plaque analysis in virtual angiography and bladder wall for early detection of growth in virtual cystography.

Public Health Relevance

This proposal aims to develop computed tomography colonography (CTC) or CT-based virtual colonoscopy (VC) toward a screening modality by using (1) as low as achievable low-dose CT scan, (2) less-stressful bowel preparation (BP), e.g., minimal laxative and/or cathartic-free BP, and (3) integration of computer-aided detection (CAD) and three-dimensional (3D) endoscopic views for polyp detection. PHS 398 (Rev. 04/06) Page __1___ Continuation Format Page

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA082402-10
Application #
8253736
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Tandon, Pushpa
Project Start
2001-04-01
Project End
2014-05-31
Budget Start
2012-07-19
Budget End
2013-05-31
Support Year
10
Fiscal Year
2012
Total Cost
$304,444
Indirect Cost
$107,873
Name
State University New York Stony Brook
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
804878247
City
Stony Brook
State
NY
Country
United States
Zip Code
11794
Zhang, Hao; Han, Hao; Liang, Zhengrong et al. (2016) Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images. IEEE Trans Med Imaging 35:860-70
Han, Hao; Lin, Qin; Li, Lihong et al. (2016) α-Information-Based Registration of Dynamic Scans for Magnetic Resonance Cystography. IEEE J Biomed Health Inform 20:1160-70
Niu, Shanzhou; Zhang, Shanli; Huang, Jing et al. (2016) Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations. Neurocomputing 197:143-160
Hu, Yifan; Liang, Zhengrong; Song, Bowen et al. (2016) Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography. IEEE Trans Med Imaging 35:1522-31
Zeng, Dong; Gong, Changfei; Bian, Zhaoying et al. (2016) Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study. Phys Med Biol 61:8135-8156
Li, Jiaojiao; Niu, Shanzhou; Huang, Jing et al. (2015) An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction. PLoS One 10:e0140579
Han, Fangfang; Wang, Huafeng; Zhang, Guopeng et al. (2015) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28:99-115
Han, Hao; Li, Lihong; Han, Fangfang et al. (2015) Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J Biomed Health Inform 19:648-59
Zeng, Dong; Huang, Jing; Bian, Zhaoying et al. (2015) A Simple Low-dose X-ray CT Simulation from High-dose Scan. IEEE Trans Nucl Sci 62:2226-2233
Zhang, Hao; Ma, Jianhua; Wang, Jing et al. (2015) Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach. Comput Med Imaging Graph 43:26-35

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