Modeling observer performance in low-dose CT assessments
Modeling observer performance in low-dose CT assessments
批准号:
10115725
负责人:
Craig Kendall Abbey
金额:
$40.15万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-04-01 至 2024-01-31
关键词:
AffectAlgorithmsAreaClassificationClinicalCognitionCollaborationsCollectionCommunitiesComputersConsumptionDataDetectionDevelopmentDevicesDiagnosticDiagnostic ImagingDiscriminationDoseGaussian modelHumanImageImage AnalysisImaging TechniquesIntegrated Health Care SystemsInvestigationLinear ModelsMalignant NeoplasmsManufacturer NameMeasuresMedical ImagingMedicineMethodologyMethodsModelingModernizationNoisePerceptionPerformanceProcessPropertyProtocols documentationPsychophysicsROC CurveReaderReceiver Operating CharacteristicsResearchRiskRoentgen RaysScanningScientistSeriesShapesSignal TransductionStimulusStructureSystemTask PerformancesTechniquesTextureTimeTrainingUnited StatesUnited States Food and Drug AdministrationUniversitiesValidationVisual system structureWeightWorkX-Ray Computed Tomographybasecancer imagingclinical imagingexperimental studyimage reconstructionimaging approachimaging propertiesimaging scientistinsightinterestlow dose computed tomographymodel developmentprospectivereconstructionresponsesimulationstatisticssuccesstomographyvalidation studiesvision science
中文摘要
项目总结/文摘
英文摘要
Project Summary/Abstract
X-ray computed tomography (CT) has become a mainstay of diagnostic imaging in many areas of
medicine because of its ability to render internal structures of the body with high accuracy. This has
resulted in a substantial increase in the use of CT imaging in the United States. As a result, there has been
sustained interest in dose reduction in CT imaging. However, demonstrating effective dose reduction is
challenging. By definition, such techniques seek to retain diagnostic quality with little or no measureable
effect on diagnostic performance. Clinical reader studies using receiver operating characteristic (ROC)
methodology are the accepted standard for evaluating diagnostic performance effects. However, these
studies are expensive and time consuming, and identifying small effects requires prohibitively large sets of
readers and cases. This has led to the development of “model-observers” for dose reduction claims at the US
Food and Drug Administration (FDA). At this time, at least three dose reduction claims at FDA have used
model observer studies to substantiate their claim of CT dose reduction using iterative reconstruction
algorithms.
The basis for this project is our recognition that such models have had relatively little validation,
given the complexity of both the human visual system and the images being evaluated. We propose an in-depth characterization of human observer responses in tasks related to dose reduction in CT. The purpose
of this research is to develop and validate a model (or models) of observer performance for use in
assessments of image reconstruction for CT dose reduction. For a model observer to be of use in this area, it
must be accepted as a reasonable predictor of human-observer performance for some range of relevant
tasks. This motivates our general approach, and many specifics of our research plan.
Our plan is to collect an initial set of psychophysical data, use this data to develop our model, and
then predict performance in CT reconstructions from simulations at a variety of doses. We then collect the
psychophysical data on these images to quantify predictive accuracy and to compare it to the accuracy of
other models. Specific Aim 1 involves the collection of psychophysical data in tasks with noise statistics
similar to CT dose assessments. Specific Aim 2 seeks to develop models of task performance by fitting
model parameter for several candidate models to the data from Aim 1. Specific Aim 3 proposes a
prospective prediction of observer performance in a new set of psychophysical data from images that have
been reconstructed using modern iterative methods. At the conclusion of the project period, we expect to
have a better understanding of how observers perform difficult localization and discrimination tasks in
noisy CT images, and how this process in influenced by the dose associated with images.
期刊论文(5)
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科研奖励(0)
会议论文
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DOI:
10.1117/1.jmi.8.4.041206
发表时间:
2021-07
期刊:
Journal of medical imaging (Bellingham, Wash.)
影响因子:
--
作者:
[Abbey CK, Lago MA, Eckstein MP]
通讯作者:
Eckstein MP
DOI:
10.1016/j.acra.2021.08.014
发表时间:
2022-06
期刊:
ACADEMIC RADIOLOGY
影响因子:
4.8
作者:
[Yang, Kai, Abbey, Craig K., Chou, Shinn-Huey Shirley, Dontchos, Brian N., Li, Xinhua, Lehman, Constance D., Liu, Bob]
通讯作者:
Liu, Bob
Performance Assessment of Texture Reproduction in High-Resolution CT.
高分辨率 CT 中纹理再现的性能评估。
DOI:
10.1117/12.2550579
发表时间:
2020
期刊:
Proceedings of SPIE--the International Society for Optical Engineering
影响因子:
--
作者:
[Shi,Hui, Gang,GraceJ, Li,Junyuan, Liapi,Eleni, Abbey,Craig, Stayman,JWebster]
通讯作者:
Stayman,JWebster
DOI:
10.1117/12.2612622
发表时间:
2022
期刊:
Proceedings of SPIE--the International Society for Optical Engineering
影响因子:
--
作者:
[Abbey,CraigK, Li,Junyuan, Gang,GraceJ, Stayman,JWebster]
通讯作者:
Stayman,JWebster
Sequential Reading Effects in Digital Breast Tomosynthesis
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批准号:10629384
-
项目类别:
-
资助金额:$45.02万
-
财政年份:2020
-
负责人:Craig Kendall Abbey
-
依托单位:
Sequential Reading Effects in Digital Breast Tomosynthesis
-
批准号:10238778
-
项目类别:
-
资助金额:$34.02万
-
财政年份:2020
-
负责人:Craig Kendall Abbey
-
依托单位:
Sequential Reading Effects in Digital Breast Tomosynthesis
-
批准号:10410475
-
项目类别:
-
资助金额:$44.98万
-
财政年份:2020
-
负责人:Craig Kendall Abbey
-
依托单位:
Utility-Based Assessment of Diagnostic Imaging Performance
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批准号:8824406
-
项目类别:
-
资助金额:$19.18万
-
财政年份:2014
-
负责人:Craig Kendall Abbey
-
依托单位:
Utility-Based Assessment of Diagnostic Imaging Performance
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批准号:8935780
-
项目类别:
-
资助金额:$19.18万
-
财政年份:2014
-
负责人:Craig Kendall Abbey
-
依托单位:
Quantitative Assessment of Murine Tumors with MicroPET.
-
批准号:6932211
-
项目类别:
-
资助金额:$18.07万
-
财政年份:2003
-
负责人:Craig Kendall Abbey
-
依托单位:
Quantitative Assessment of Murine Tumors with MicroPET.
-
批准号:6677249
-
项目类别:
-
资助金额:$18.07万
-
财政年份:2003
-
负责人:Craig Kendall Abbey
-
依托单位:
海外基金