METHODS: This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center-RSNA International COVID-19 Open Radiology Database data set (chest CT of adult patients positive for COVID-19), which included expert annotations including "infectious opacities," were analyzed. First, the full z-scan length of the CT image data set was evaluated. Next, the z-scan length was reduced from the left hemidiaphragm to the top of the aortic arch. Generic dose reduction based on dose length product (DLP) and patient-specific organ dose reductions were calculated. The percentage of infectious opacities excluded from the reduced z-scan length was used to quantify the effect on diagnostic utility. RESULTS: Generic dose reduction, based on DLP, was 69%. The organ dose reduction ranged from approximately equal to 18% (breasts) to approximately equal to 64% (bone surface and bone marrow). On average, 12.4% of the infectious opacities were not included in the reduced z-coverage, per patient, of which 5.1% were above the top of the arch and 7.5% below the left hemidiaphragm. CONCLUSIONS: Limiting z-scan length of chest CTs reduced radiation dose without significantly compromising diagnostic utility in COVID-19 patients. The PREDICT demonstrated that patient-specific organ dose reductions varied from generic dose reduction based on DLP.
METHODS: This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center-RSNA International COVID-19 Open Radiology Database data set (chest CT of adult patients positive for COVID-19), which included expert annotations including "infectious opacities," were analyzed. First, the full z-scan length of the CT image data set was evaluated. Next, the z-scan length was reduced from the left hemidiaphragm to the top of the aortic arch. Generic dose reduction based on dose length product (DLP) and patient-specific organ dose reductions were calculated. The percentage of infectious opacities excluded from the reduced z-scan length was used to quantify the effect on diagnostic utility. RESULTS: Generic dose reduction, based on DLP, was 69%. The organ dose reduction ranged from approximately equal to 18% (breasts) to approximately equal to 64% (bone surface and bone marrow). On average, 12.4% of the infectious opacities were not included in the reduced z-coverage, per patient, of which 5.1% were above the top of the arch and 7.5% below the left hemidiaphragm. CONCLUSIONS: Limiting z-scan length of chest CTs reduced radiation dose without significantly compromising diagnostic utility in COVID-19 patients. The PREDICT demonstrated that patient-specific organ dose reductions varied from generic dose reduction based on DLP.
Authors: Sara Principi; Yonggang Lu; Yu Liu; Adam Wang; Alex Maslowski; Todd Wareing; John Van Heteren; Taly Gilat Schmidt Journal: Med Phys Date: 2021-10-29 Impact factor: 4.071
Authors: Emily B Tsai; Scott Simpson; Matthew P Lungren; Michelle Hershman; Leonid Roshkovan; Errol Colak; Bradley J Erickson; George Shih; Anouk Stein; Jayashree Kalpathy-Cramer; Jody Shen; Mona Hafez; Susan John; Prabhakar Rajiah; Brian P Pogatchnik; John Mongan; Emre Altinmakas; Erik R Ranschaert; Felipe C Kitamura; Laurens Topff; Linda Moy; Jeffrey P Kanne; Carol C Wu Journal: Radiology Date: 2021-01-05 Impact factor: 11.105
Authors: Geoffrey D Rubin; Christopher J Ryerson; Linda B Haramati; Nicola Sverzellati; Jeffrey P Kanne; Suhail Raoof; Neil W Schluger; Annalisa Volpi; Jae-Joon Yim; Ian B K Martin; Deverick J Anderson; Christina Kong; Talissa Altes; Andrew Bush; Sujal R Desai; Jonathan Goldin; Jin Mo Goo; Marc Humbert; Yoshikazu Inoue; Hans-Ulrich Kauczor; Fengming Luo; Peter J Mazzone; Mathias Prokop; Martine Remy-Jardin; Luca Richeldi; Cornelia M Schaefer-Prokop; Noriyuki Tomiyama; Athol U Wells; Ann N Leung Journal: Chest Date: 2020-04-07 Impact factor: 9.410