Developing Virtual Colonoscopy for Cancer Screening

项目摘要 结直肠癌是第三大最常见的诊断癌症，也是结直肠癌死亡的第二大原因。 癌症在美国通常它是在晚期被诊断出来的，在病人已经发展成 症状，解释其高死亡率。由于大多数结肠癌是在5到15年内从息肉中产生的， 在恶变期间，筛查计划，以发现小息肉已被提倡。 不幸的是，大多数人都没有遵循这个建议。该项目的健康相关性是 通过制定一项计划，大大增加愿意参加筛查计划的人数。 方便，几乎无风险的检测小息肉的方法。 计算机断层扫描结肠成像（CTC）或基于CT的虚拟结肠镜检查（VC）显示了相当的 与金标准光学结肠镜（OC）检测8 mm及以上息肉的性能相比， 入侵的方式。为了成为一种筛选工具，必须改进目前的CTC，使其为较大的 对较小息肉的检测能力较高的人群。这些需求由最近的 CTC被拒绝接受医疗保险覆盖，如减少CT辐射，检测较小的 息肉，减轻肠道准备（BP）压力，最大限度地减少阅片者的变化，提高效率等。 在技术进步方面取得了良好的进展，实现了该项目的广泛、长期目标，即， 将CTC发展为一种安全、准确、成本效益高、侵入性最小、压力最小的筛查方式。 为使CTC向长远目标迈进，本次项目更新的具体目标是：（1）发展 并评估自适应统计重建算法以获得当前腹部CT图像 质量在尽可能低的mAs水平，以尽量减少X射线暴露的风险。算法将考虑 不仅X射线量子统计和能谱，而且系统背景噪声，因为这 噪声在低mAs水平下起着显著的作用。(2)开发和评价部分容积（PV）统计 改进电子结肠清洗的具有粪便标记不均匀性校正的分割算法 （ECC）。这一改进将导致更小息肉的检测，减少一半的CT辐射， 通过接近无泻药CTC缓解患者对BP的压力。(3)开发和评估一个 ECC-自适应、基于纹理的特征提取算法，以改进计算机辅助息肉检测 （CADpolyp）以提高CTC的成本效益。 希望在较小息肉和更多息肉上具有更高检测能力的改进性能 一个大的人群可以接受的（通过更少的辐射和压力）将促进CTC向筛查 接受医疗保险的方式。低mAs的统计重建将有助于 其他CT应用具有不均匀性校正的PV图像分割将有利于 其他虚拟内窥镜应用，例如提取血管壁用于虚拟内窥镜中的斑块分析， 血管造影和膀胱壁的早期检测增长的虚拟膀胱造影。

Project Summary 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.