Quantitative diagnostic dual energy CT with atlas-based prior knowledge
基于图谱先验知识的定量诊断双能 CT
基本信息
- 批准号:286653188
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2016
- 资助国家:德国
- 起止时间:2015-12-31 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
During the last decade, dual energy CT (DECT) became widely available in clinical routine. Offered by all major CT manufacturers, with differing hard- and software concepts, the DECT applications are alike: the acquired DECT data are used to conduct two- or multi material decompositions, e.g. to separate iodine and bone from soft tissue or to quantify contrast agent and fat, to classify or characterize tissue, or to increase contrasts (CNR maximization), or to suppress contrasts (artifact reduction). The applications are designed to work with certain organs and the user needs to take care to invoke the correct application and to interpret its output only in the appropriate organ or anatomical region. E.g. interpreting the output of a kidney stone applications in organs other than the kidney will yield a wrong classification. To obtain quantitative results the applications require to set patient-specific parameters. In order to calibrate those the user is asked to place ROIs in predefined anatomical regions. Since this is time-consuming users are often tempted to use the default settings instead of optimizing them. Here, we want to develop a DECT atlas to utilize its anatomical (and functional) information for con-text-sensitive DECT imaging and material decomposition, and to be able to automatically calibrate the open parameters without the need of user interaction. To improve quantification the initial images shall not be reconstructed separately but rather undergo a rawdata-based decomposition before being converted into image domain. A dedicated user interface shall be developed to provide the new context-sensitive DECT information - such as automatically decomposing each organ into different but reasonable basis materials, for example - and to display it to the reader in a convenient way. Similarly, user-placed ROIs shall trigger a context-sensitive statistical evaluation of the ROI's contents and provide it to the user. This will help to quantify the iodine uptake in a tumor or a lesion, to separate it from fat or calcium components, to estimate its blood supply etc. Since the DECT data display the contrast uptake just for a given instance in time and since this contrast depends on patient-specific factors such as the cardiac output, we are planning to normalize the contrast uptake with the help of the dual energy information contained in the atlas. This will minimize inter and intra patient effects and increase the reproducibility. In addition, organ-specific material scores shall be developed that quantify a patient's material composition on an organ by organ basis. The new methods (DECT atlas, material decomposition, ...) shall be tested and evaluated using phantom and patient studies, and shall be optimized accordingly.
在过去十年中,双能CT(DECT)在临床常规中得到广泛应用。所有主要的CT制造商都提供不同的硬件和软件概念,DECT应用是相似的:采集的DECT数据用于进行两种或多种材料分解,例如从软组织中分离碘和骨,或量化造影剂和脂肪,对组织进行分类或表征,或增加对比度(CNR最大化),或抑制对比度(伪影减少)。应用程序设计用于某些器官,用户需要注意调用正确的应用程序,并仅在适当的器官或解剖区域中解释其输出。例如,将肾结石应用程序的输出解释为肾脏以外的器官将产生错误的分类。为了获得定量结果,应用程序需要设置患者特定参数。为了校准这些,要求用户将ROI放置在预定义的解剖区域中。由于这很耗时,用户通常倾向于使用默认设置,而不是优化它们。在这里,我们希望开发一个DECT图谱,利用其解剖(和功能)信息的上下文敏感的DECT成像和材料分解,并能够自动校准开放的参数,而无需用户交互。为了改善量化,初始图像不应单独重建,而是在转换到图像域之前进行基于原始数据的分解。应开发专用用户界面,以提供新的上下文敏感DECT信息-例如,自动将每个器官分解为不同但合理的基本材料-并以方便的方式将其显示给读者。同样,用户放置的ROI应触发ROI内容的上下文敏感统计评估并将其提供给用户。这将有助于量化肿瘤或病变中的碘摄取,将其与脂肪或钙成分分离,估计其血液供应等。由于DECT数据仅显示给定时刻的造影剂摄取,并且由于该造影剂取决于患者特定因素,例如心输出量,我们计划在图谱中包含的双能量信息的帮助下使造影剂摄取标准化。这将最大限度地减少患者间和患者内的影响,并增加再现性。此外,应制定器官特异性物质评分,以逐个器官的方式量化患者的物质成分。新方法(DECT图谱,材料分解,.)应使用体模和患者研究进行测试和评估,并应进行相应优化。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Manifold Learning-based Data Sampling for Model Training
- DOI:10.1007/978-3-662-56537-7_70
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Shuqing Chen;Sabrina Dorn;M. Lell;M. Kachelriess;A. Maier
- 通讯作者:Shuqing Chen;Sabrina Dorn;M. Lell;M. Kachelriess;A. Maier
A Feasibility Study of Automatic Multi-Organ Segmentation Using Probabilistic Atlas
使用概率图谱自动多器官分割的可行性研究
- DOI:10.1007/978-3-662-54345-0_50
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:S. Chen;J. Endres;S. Dorn;J. Maier;M. M: Lell;M. Kachelrieß;A. Maier
- 通讯作者:A. Maier
Organ-specific context-sensitive CT image reconstruction and display
- DOI:10.1117/12.2291897
- 发表时间:2018-03
- 期刊:
- 影响因子:0
- 作者:Sabrina Dorn;Shuqing Chen;S. Sawall;D. Simons;M. May;J. Maier;M. Knaup;H. Schlemmer;A. Maier;M. Lell;M. Kachelriess
- 通讯作者:Sabrina Dorn;Shuqing Chen;S. Sawall;D. Simons;M. May;J. Maier;M. Knaup;H. Schlemmer;A. Maier;M. Lell;M. Kachelriess
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Professor Dr. Marc Kachelrieß其他文献
Professor Dr. Marc Kachelrieß的其他文献
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{{ truncateString('Professor Dr. Marc Kachelrieß', 18)}}的其他基金
Automatic exposure control (AEC) for CT based on neural network-driven patient-specific real-time assessment of dose distributions and minimization of the effective dose
基于神经网络驱动的患者特异性剂量分布实时评估和有效剂量最小化的 CT 自动曝光控制 (AEC)
- 批准号:
428660931 - 财政年份:2019
- 资助金额:
-- - 项目类别:
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Metal Artifact Reduction (MAR) in Image-Guided Radiation Therapy (IGRT)
图像引导放射治疗 (IGRT) 中的金属伪影减少 (MAR)
- 批准号:
250538804 - 财政年份:2014
- 资助金额:
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New Acquisition and Reconstruction Techniques for Interventional Flat Detector-Based C-Arm CT
基于介入性平面探测器的 C 形臂 CT 的新采集和重建技术
- 批准号:
233427018 - 财政年份:2013
- 资助金额:
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Research Grants (Transfer Project)
Quantitative und artefaktfreie Mehrspektren-CT mit hoher Dosisnutzung und hoher Scangeschwindigkeit
定量、无伪影的多能谱 CT,剂量利用率高,扫描速度快
- 批准号:
187885109 - 财政年份:2011
- 资助金额:
-- - 项目类别:
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Schnelle Iterative Rekonstruktion zur Bildgebung des Herzens in der Spiral-Computertomographie
螺旋计算机断层扫描中心脏成像的快速迭代重建
- 批准号:
138525782 - 财政年份:2009
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Exakte Niedrigdosis-Bildrekonstruktion für Single- und Dual-Energy Mikro-CT
单能和双能显微 CT 的精确低剂量图像重建
- 批准号:
26682968 - 财政年份:2006
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