Colorectal carcinoma (CRC) remains the third most commonly diagnosed cancer and the second leading cause of death from cancer for both man and women in the United States. Often it is diagnosed at an advanced stage, after the patient has developed symptoms, explaining its high mortality rate. Fortunately, most CRC are preventable because they arise from colorectal polyps over a 5 to 15 year period of malignant transformation and, therefore, screening programs to detect and remove the polyps (or precursor) during the transformation period have been advocated for cancer prevention. Unfortunately many people (at present time, more than 35% of the population, a very high rate for the high CRC incidence) do not follow the recommendation and, on the other hand, many of the screened people are either under- or over-diagnosed for several reasons related to the limitations of currently available screening methods. The health relatedness of this project is to advance a convenient, nearly risk-free screening method, called computed tomography (CT)-based virtual colonoscopy (VC) or CT colonography (CTC), to overcome the limitations. Optical colonoscopy (OC) is currently the gold standard for detection and removal of the polyps. Because OC is somehow too invasive, compliance to recommendation with OC screening would remain a concern. Furthermore, OC would demand a great resource to screen the large population as recommended with age over 50 and, therefore, would not be an optimal primary screening test. On the other hand, stool-based tests (e.g. fecal blood or DNA tests) are easy to perform but have a very low detectability. We have been the pioneers in developing CTC as a minimal-invasive cost-effective method to relieve the burden of OC for the screening purpose and have shown its comparable performance to OC on detection of polyps with size 8mm and larger. We understand very well on the two major concerns on the current CT radiation level and the current CTC inconsistency in detecting small polyps (<10mm), which were discussed by the expert panel if CTC is ready as massive screening for Medicare coverage. We have deep insight on CTC potential beyond the detection task for characterizing Hyperplastic (no-risk) vs. Adenoma (risk) polyps for personalized optimal polyp treatment. This proposal intends to relieve the concerns and bring the current CTC detection-only paradigm up to a new level of not only detecting polyps, but also characterizing the detected polyps at the screening stage via the following two specific aims: (1). To develop and evaluate adaptive image reconstruction methods to retain adequate image quality (particularly to enhance image textures) for polyp detection and characterization with as low as achievable CT radiation. (2). To explore and evaluate image texture features as imaging biomarkers to detect polyps and characterize polyp subtypes. We hypothesize that the above specific aims will advance CTC to be a cost-effective screening test and to supplement OC for a streamlined procedure to increase patient compliance and reduce CRC incident rate.
This proposal aims to relieve the current concerns on (1) CTC radiation risk and (2) in consistency in detection of small/flat polyps, and further to bring the current CTC detection-only capability up to a new paradigm of not only detecting polyps in a screening setting, but also characterizing the detected polyps for personalized polyp management. Because a significant portion of polyps in the screening population are hyperplastic, which are abnormal growths with no risk, removal of these growths would gain nothing but waste great resource to both the society and the family, e.g. resection intervention requires sedation, hospital facility, family member escort, a series of pathological analyses, etc. Differentiating hyperplastic (no-risk) from adenoma (risk) polyps is hoped to greatly reduce the cost of current colon cancer prevention practice, and furthermore differentiating adenocarcinoma (high-risk) from adenoma (low-risk) polyps is hoped to greatly benefit the decision-making on surgical intervention.
|Chen, Wensheng; You, Jie; Pan, Binbin et al. (2018) A sparse representation and dictionary learning based algorithm for image restoration in the presence of Rician noise. Neurocomputing 286:130-140|
|Jia, Xiao; Liao, Yuting; Zeng, Dong et al. (2018) Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image. Phys Med Biol 63:225020|
|Tan, Jiaxing; Huo, Yumei; Liang, Zhengrong et al. (2018) Expert knowledge-infused deep learning for automatic lung nodule detection. J Xray Sci Technol :|
|Chen, Bo; Bian, Zhaoying; Zhou, Xiaohui et al. (2018) A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction. Neurocomputing 285:74-81|
|Zhang, Hao; Ma, Jianhua; Wang, Jing et al. (2017) Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy. Med Phys 44:e264-e278|
|Xie, Qi; Zeng, Dong; Zhao, Qian et al. (2017) Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism. IEEE Trans Med Imaging 36:2487-2498|
|Zhang, Houjin; Zeng, Dong; Lin, Jiahui et al. (2017) Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization. Phys Med Biol 62:5556-5574|
|Zhang, Hao; Zeng, Dong; Zhang, Hua et al. (2017) Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review. Med Phys 44:1168-1185|
|Liu, Yongkai; Duan, Chaijie; Liang, Jerome et al. (2017) Haustral loop extraction for CT colonography using geodesics. Int J Comput Assist Radiol Surg 12:379-388|
|Zeng, Dong; Xie, Qi; Cao, Wenfei et al. (2017) Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization. IEEE Trans Med Imaging 36:2546-2556|
Showing the most recent 10 out of 11 publications