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Sotoudeh-Paima S, Segars WP, Ghosh D, Luo S, Samei E, Abadi E. A systematic assessment and optimization of photon-counting CT for lung density quantifications. Med Phys 2024; 51:2893-2904. [PMID: 38368605 PMCID: PMC11055522 DOI: 10.1002/mp.16987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. PURPOSE To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. METHODS This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. RESULTS Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied. CONCLUSION Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.
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Affiliation(s)
- Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
| | - W. Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
| | - Dhrubajyoti Ghosh
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
- Department of Physics, Duke University, Durham, USA
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
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Sharma S, Kapadia A, Ria F, Segars WP, Samei E. Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols. Med Phys 2022; 49:5439-5450. [PMID: 35690885 PMCID: PMC9536505 DOI: 10.1002/mp.15798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/16/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The gold-standard method for estimation of patient-specific organ doses in digital tomosynthesis (DT) requires protocol-specific Monte Carlo (MC) simulations of radiation transport in anatomically accurate computational phantoms. Although accurate, MC simulations are computationally expensive, leading to a turnaround time in the order of core hours for simulating a single exam. This limits their clinical utility. The purpose of this study is to overcome this limitation by utilizing patient- and protocol-specific MC simulations to develop a comprehensive database of air-kerma-normalized organ dose coefficients for a virtual population of adult and pediatric patient models over an expanded set of exam protocols in DT for retrospective and prospective estimation of radiation dose in clinical tomosynthesis. MATERIALS AND METHODS A clinically representative virtual population of 14 patient models was used, with pediatric models (M and F) at ages 1, 5, 10, and 15 and adult patient models (M and F) with body mass index (BMIs) at 10th, 50th, and 90th percentiles of the US population. A graphics processing unit (GPU)-based MC simulation framework was used to simulate organ doses in the patient models, incorporating the scanner-specific configuration of a clinical DT system (VolumeRad, GE Healthcare, Waukesha, WI, USA) and an expanded set of exam protocols, including 21 distinct acquisition techniques for imaging a variety of anatomical regions (head and neck, thorax, spine, abdomen, and knee). Organ dose coefficients (hn ) were estimated by normalizing organ dose estimates to air kerma at 70 cm (X70cm ) from the source in the scout view. The corresponding coefficients for projection radiography were approximated using organ doses estimated for the scout view. The organ dose coefficients were further used to compute air-kerma-normalized patient-specific effective dose coefficients (Kn ) for all combinations of patients and protocols, and a comparative analysis examining the variation of radiation burden across sex, age, and exam protocols in DT, and with projection radiography was performed. RESULTS The database of organ dose coefficients (hn ) containing 294 distinct combinations of patients and exam protocols was developed and made publicly available. The values of Kn were observed to produce estimates of effective dose in agreement with prior studies and consistent with magnitudes expected for pediatric and adult patients across the different exam protocols, with head and neck regions exhibiting relatively lower and thorax and C-spine (apsc, apcs) regions relatively higher magnitudes. The ratios (r = Kn /Kn ,rad ) quantifying the differences air-kerma-normalized patient-specific effective doses between DT and projection radiography were centered around 1.0 for all exam protocols, with the exception of protocols covering the knee region (pawk, patk). CONCLUSIONS This study developed a database of organ dose coefficients for a virtual population of 14 adult and pediatric XCAT patient models over a set of 21 exam protocols in DT. Using empirical measurements of air kerma in the clinic, these organ dose coefficients enable practical retrospective and prospective patient-specific radiation dosimetry. The computation of air-kerma-normalized patient-specific effective doses further enables the comparison of radiation burden to the patient populations between protocols and between imaging modalities (e.g., DT and projection radiography), as presented in this study.
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Affiliation(s)
- Shobhit Sharma
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Physics, Duke University, Durham, North Carolina, USA
| | - Anuj Kapadia
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Physics, Duke University, Durham, North Carolina, USA
| | - Francesco Ria
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Clinical Imaging Physics Group, Department of Radiology, Duke University, Durham, North Carolina, USA
| | - W Paul Segars
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Ehsan Samei
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Physics, Duke University, Durham, North Carolina, USA.,Clinical Imaging Physics Group, Department of Radiology, Duke University, Durham, North Carolina, USA.,Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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