摘要
根据NewsRx编辑来自弗吉尼亚州亚历山大市的新闻报道，Robotics&Machine Learning Daily News-GE Precision Healthcare LC(Waukesha，Wisconsin，United States)的新闻记者兼工作人员新闻编辑已获得专利号12002204.专利的发明人是Imran、Abdullah-al-Zubaer(加利福尼亚州桑尼维尔)、Pal、D Ebashish(加利福尼亚州桑尼维尔)、Patel、Bhavik Natvar(亚利桑那州天堂谷)、Wa Ng、Adam S.(加利福尼亚州帕洛阿尔托)、Wang、Sen(加利福尼亚州门洛帕克)、Zucker、Evan(加利福尼亚州帕洛阿尔托)。该专利于2021年9月10日提交，并于2024年6月4日在线发布。从发明人提供的背景资料中，新闻记者引述如下：“计算机断层摄影术(CT)自从几十年前发明以来，已经成为最成功的成像方式之一，并促进了无数基于图像的医疗程序。CT占了大量的辐射暴露，特别是随着CT检查的迅速增长，因此，希望降低CT辐射剂量。但是，在这方面，辐射剂量和图像质量传统上一直是CT成像中的竞争目标。为了平衡这两者，现代CT系统使用自动曝光控制(AEC)，特别是基于SC输出图像的管电流调制(TCM)。传统的AEC算法的目标是在整个成像体积上提供单一形式的噪声水平，虽然这通常在避免过度辐射剂量和保持图像质量方面是成功的，但它主要是基于患者的一般形状和尺寸。传统的AEC算法使用的是反映管子输出而不是病人实际剂量的剂量度量。传统的AEC技术仍然不能反映影响器官剂量的病人特定解剖结构，不考虑基于任务的图像质量，并且容易出现病人中心等问题。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Ge Precision Healthcare LLC (Waukesha, Wisconsin, United States) has been issued patent number 12002204, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent's inventors are Imran, Abdullah-Al-Zubaer (Sunnyvale, CA, US), Pal, D ebashish (Sunnyvale, CA, US), Patel, Bhavik Natvar (Paradise Valley, AZ, US), Wa ng, Adam S. (Palo Alto, CA, US), Wang, Sen (Menlo Park, CA, US), Zucker, Evan (P alo Alto, CA, US). This patent was filed on September 10, 2021 and was published online on June 4, 2024. From the background information supplied by the inventors, news correspondents o btained the following quote: "Computed tomography (CT) has been one of the most successful imaging modalities and has facilitated countless image-based medical procedures since its invention decades ago. CT accounts for a large amount of io nizing radiation exposure, especially with the rapid growth in CT examinations. Therefore, it is desirable to reduce the CT radiation dose. However, the reducti on in dose also incurs additional noise and with the degraded image quality, dia gnostic performance can be compromised. "In this regard, radiation dose and image quality have traditionally been compet ing objectives in CT imaging. To balance the two, modern CT systems use automati c exposure control (AEC), particularly tube current modulation (TCM) based on sc out images. However, the goal of conventional AEC algorithms is to provide a uni form noise level across the entire imaged volume. While this is generally succes sful in avoiding excessive radiation dose and maintaining image quality, it is l argely based solely on the general shape and size of the patient. In addition, c onventional AEC algorithms use a metric for dose which reflects the tube output rather than actual dose to the patient. Conventional AEC techniques remain unawa re of patient-specific anatomy that impacts organ dose, do not account for image quality based on the task, and are prone to issues such as patient centering."
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