The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Cynthia H. Mccollough - One of the best experts on this subject based on the ideXlab platform.
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Low dose CT image and projection dataset.
Medical physics, 2020Co-Authors: Taylor R. Moen, Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Xinhui Duan, Cynthia H. MccolloughAbstract:PURPOSE To describe a large, publicly available dataset comprising computed tomography (CT) images and projection data from patient exams at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD Files, including a Dictionary File, data reader and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
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An open library of CT patient projection data
Proceedings of SPIE--the International Society for Optical Engineering, 2016Co-Authors: Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Cynthia H. MccolloughAbstract:Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text File. Several instructional documents are provided to help the users extract information from DICOM-CT-PD Files, including a Dictionary File for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.
Taylor R. Moen - One of the best experts on this subject based on the ideXlab platform.
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Low dose CT image and projection dataset.
Medical physics, 2020Co-Authors: Taylor R. Moen, Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Xinhui Duan, Cynthia H. MccolloughAbstract:PURPOSE To describe a large, publicly available dataset comprising computed tomography (CT) images and projection data from patient exams at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD Files, including a Dictionary File, data reader and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
Baiyu Chen - One of the best experts on this subject based on the ideXlab platform.
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Low dose CT image and projection dataset.
Medical physics, 2020Co-Authors: Taylor R. Moen, Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Xinhui Duan, Cynthia H. MccolloughAbstract:PURPOSE To describe a large, publicly available dataset comprising computed tomography (CT) images and projection data from patient exams at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD Files, including a Dictionary File, data reader and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
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An open library of CT patient projection data
Proceedings of SPIE--the International Society for Optical Engineering, 2016Co-Authors: Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Cynthia H. MccolloughAbstract:Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text File. Several instructional documents are provided to help the users extract information from DICOM-CT-PD Files, including a Dictionary File for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.
Shuai Leng - One of the best experts on this subject based on the ideXlab platform.
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Low dose CT image and projection dataset.
Medical physics, 2020Co-Authors: Taylor R. Moen, Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Xinhui Duan, Cynthia H. MccolloughAbstract:PURPOSE To describe a large, publicly available dataset comprising computed tomography (CT) images and projection data from patient exams at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD Files, including a Dictionary File, data reader and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
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An open library of CT patient projection data
Proceedings of SPIE--the International Society for Optical Engineering, 2016Co-Authors: Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Cynthia H. MccolloughAbstract:Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text File. Several instructional documents are provided to help the users extract information from DICOM-CT-PD Files, including a Dictionary File for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.
David R. Holmes - One of the best experts on this subject based on the ideXlab platform.
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Low dose CT image and projection dataset.
Medical physics, 2020Co-Authors: Taylor R. Moen, Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Xinhui Duan, Cynthia H. MccolloughAbstract:PURPOSE To describe a large, publicly available dataset comprising computed tomography (CT) images and projection data from patient exams at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD Files, including a Dictionary File, data reader and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
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An open library of CT patient projection data
Proceedings of SPIE--the International Society for Optical Engineering, 2016Co-Authors: Baiyu Chen, Shuai Leng, David R. Holmes, Joel G. Fletcher, Cynthia H. MccolloughAbstract:Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text File. Several instructional documents are provided to help the users extract information from DICOM-CT-PD Files, including a Dictionary File for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.