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Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation

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Abstract

To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.

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Funding

This work was funded by the intramural program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics.

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Authors and Affiliations

  1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Choonsik Lee, Gleb A. Kuzmin & Elizabeth Mosher

  2. Radiation Epidemiology Branch/DCEG/NCI/NIH, 9609 Medical Center Drive, Rockville, MD, 20850, USA

    Choonsik Lee

  3. Kansas City University of Medicine and Bioscience, Kansas City, KS, USA

    Jinyong Bae

  4. Radiology and Imaging Sciences Clinical Center, National Institutes of Health, Bethesda, MD, USA

    Jianhua Yao & Les R. Folio

Authors
  1. Choonsik Lee
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  2. Gleb A. Kuzmin
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  4. Jianhua Yao
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  6. Les R. Folio
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Correspondence to Choonsik Lee.

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Lee, C., Kuzmin, G.A., Bae, J. et al. Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation. J Digit Imaging 32, 175–182 (2019). https://doi.org/10.1007/s10278-018-0119-2

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  • DOI: https://doi.org/10.1007/s10278-018-0119-2

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