Browsing by Author "Ria, Francesco"

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Open Access
A comparison of COVID-19 and imaging radiation risk in clinical patient populations.
(J Radiol Prot, 2020-10-07) Ria, Francesco; Fu, Wanyi; Chalian, Hamid; Abadi, Ehsan; Segars, Paul W; Fricks, Rafael; Khoshpouri, Pegah; Samei, Ehsan
OBJECTIVE: The outbreak of coronavirus SARS-COV2 affected more than 180 countries necessitating fast and accurate diagnostic tools. Reverse transcriptase polymerase chain reaction (RT-PCR) has been identified as a gold standard test with Chest CT and Chest Radiography showing promising results as well. However, radiological solutions have not been used extensively for the diagnosis of COVID-19 disease, partly due to radiation risk. This study aimed to provide quantitative comparison of imaging radiation risk versus COVID risk. METHODS: The analysis was performed in terms of mortality rate per age group. COVID-19 mortality was extracted from epidemiological data across 299,004 patients published by ISS-Integrated surveillance of COVID-19 in Italy. For radiological risk, the study considered 659 Chest CT performed in adult patients. Organ doses were estimated using a Monte Carlo method and then used to calculate Risk Index that was converted into an upper bound for related mortality rate following NCI-SEER data. RESULTS: COVID-19 mortality showed a rapid rise for ages >30 years old (min:0.30%; max:30.20%), whereas only 1 death was reported in the analyzed patient cohort for ages <20 years old. The rates decreased for radiation risk across age groups. The median mortality rate across all ages for Chest-CT and Chest-Radiography were 0.007% (min:0.005%; max:0.011%) and 0.0003% (min:0.0002%; max:0.0004%), respectively. CONCLUSIONS: COVID-19, Chest Radiography, and Chest CT mortality rates showed different magnitudes and trends across age groups. In higher ages, the risk of COVID-19 far outweighs that of radiological exams. Based on risk comparison alone, Chest Radiography and CT for COVID-19 care is justified for patients older than 20 and 30 years old, respectively. Notwithstanding other aspects of diagnosis, the present results capture a component of risk consideration associated with the use of imaging for COVID. Once integrated with other diagnostic factors, they may help inform better management of the pandemic.
Open Access
A database of 40 patient-based computational models for benchmarking organ dose estimates in CT.
(Medical physics, 2020-07-06) Samei, Ehsan; Ria, Francesco; Tian, Xiaoyu; Segars, Paul W
PURPOSE:Patient radiation burden in CT can best be characterized through risk estimates derived from organ doses. Organ doses can be estimated by Monte Carlo simulations of the CT procedures on computational phantoms assumed to emulate the patients. However, the results are subject to uncertainties related to how accurately the patient and CT procedure are modeled. Different methods can lead to different results. This paper, based on decades of organ dosimetry research, offers a database of CT scans, scan specifics, and organ doses computed using a validated Monte Carlo simulation of each patient and acquisition. It is aimed that the database can serve as means to benchmark different organ dose estimation methods against a benchmark dataset. ACQUISITION AND VALIDATION METHODS:Organ doses were estimated for 40 adult patients (22 female, 18 female) who underwent Chest and Abdominopelvic CT examinations. Patient-based computational models were created for each patient including 26 organs for female and 25 organs for male cases. A Monte Carlo code, previously validated experimentally, was applied to calculate organ doses under constant and two modulated tube current conditions. DATA FORMAT AND USAGE NOTES:The generated database reports organ dose values for Chest and Abdominopelvic examinations per patient and imaging condition. Patient information and images and scan specifications (energy spectrum, bowtie filter specification, and tube current profiles) are provided. The database is available at publicly accessible digital repositories. POTENTIAL APPLICATIONS:Consistency in patient risk estimation, and associated justification and optimization requires accuracy and consistency in organ dose estimation. The database provided in this paper is a helpful tool to benchmark different organ dose estimation methodologies to facilitate comparisons, assess uncertainties, and improve assessment of risk of CT scans based on organ dose.
Open Access
A mathematical framework to quantitatively balance clinical and radiation risk in Computed Tomography
(2021-12-01) Ria, Francesco; Zhang, Anru; Lerebours, Reginald; Erkanli, Alaattin; Solomon, justin; Marin, Daniele; Samei, Ehsan
Purpose: Risk in medical imaging is a combination of radiation risk and clinical risk, which is largely driven by the effective diagnosis. While radiation risk has traditionally been the main focus of Computed Tomography (CT) optimization, such a goal cannot be achieved without considering clinical risk. The purpose of this study was to develop a comprehensive mathematical framework that considers both radiation and clinical risks based on the specific task, the investigated disease, and the interpretive performance (i.e., false positive and false negative rates), tested across a representative clinical CT population. Methods and Materials: The proposed mathematical framework defined the radiation risk to be a linear function of the radiation dose, the population prevalence of the disease, and the false positive rate. The clinical risk was defined to be a function of the population prevalence, the expected life-expectancy loss for an incorrect diagnosis, and the interpretative performance in terms of the AUC as a function of radiation dose. A Total Risk (TR) was defined as the sum of the radiation risk and the clinical risk. With IRB approval, the mathematical function was applied to a dataset of 80 adult CT studies investigating localized stage liver cancer (LLC) for a specific false positive rate of 5% reconstructed with both Filtered Back Projection (FBP) and Iterative Reconstruction (IR) algorithm. Linear mixed effects models were evaluated to determine the relationship between radiation dose and radiation risk and interpretative performance, respectively. Lastly, the analytical minimum of the TR curve was determined and reported. Results: TR is largely affected by clinical risk for low radiation dose whereas radiation risk is dominant at high radiation dose. Concerning the application to the LLC population, the median minimum risk in terms of mortality per 100 patients was 0.04 in FBP and 0.03 in IR images; the corresponding CTDIvol values were 38.5 mGy and 25.7 mGy, respectively. Conclusions: The proposed mathematical framework offers a complete quantitative description of risk in CT enabling a comprehensive risk-to-benefit assessment essential in the effective justification of radiological procedures and in the design of optimal clinical protocols. Clinical Relevance/Application: The quantification of both radiation and clinical risk using comparable units allows the calculation of the overall risk paving the road towards a comprehensive risk-to-benefit assessment in CT.
Open Access
A patient-informed approach to predict iodinated-contrast media enhancement in the liver
(European Journal of Radiology, 2022-10) Setiawan, Hananiel; Chen, Chaofan; Abadi, Ehsan; Fu, Wanyi; Marin, Daniele; Ria, Francesco; Samei, Ehsan
Open Access
Accuracy of Noise Magnitude Measurements from Patient CT Images
(https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.16525, 2023-07-23) Ria, Francesco; Setiawan, Hananiel; Abadi, Ehsan; Samei, Ehsan
Purpose Noise magnitude is a main CT image quality indicator. In vivo measurements emerged as a patient-specific methodology to assess and qualify CT noise, yet methods to do so vary. Current noise measurement methods in soft tissues and air surrounding the patient use distinct image segmentations, HU thresholds, and region-of-interests, resulting in noise estimation variations. In this study, we compared two noise magnitude calculation methods against the gold standard ensemble noise in two cohorts of virtually-generated patient images across 36 imaging conditions. Methods 1800 image datasets were generated using a virtual trial platform based on anthropomorphic phantoms (XCAT) and a validated, scanner-specific CT simulator (DukeSim). XCAT phantoms were repeatedly imaged 50 times using Chest and Abdominopelvic protocols, three dose levels, and three reconstruction kernels, using both FBP and IR algorithms. Noise magnitudes were calculated in the air surrounding the patient (An) and soft tissues (GNI) by applying HU<-900 and -300
Open Access
Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose.
(Scientific reports, 2024-03) Ria, Francesco; Rehani, Madan M; Samei, Ehsan
An updated extension of effective dose was recently introduced, namely relative effective dose ( Er ), incorporating age and sex factors. In this study we extended Er application to a population of about 9000 patients who underwent multiple CT imaging exams, and we compared it with other commonly used radiation protection metrics in terms of their correlation with radiation risk. Using Monte Carlo methods, Er , dose-length-product based effective dose ( EDLP ), organ-dose based effective dose ( EOD ), and organ-dose based risk index ( RI ) were calculated for each patient. Each metric's dependency to RI was assessed in terms of its sensitivity and specificity. Er showed the best sensitivity, specificity, and agreement with RI (R2 = 0.97); while EDLP yielded the lowest specificity and, along with EOD , the lowest sensitivity. Compared to other metrics, Er provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.
Open Access
ChatGPT versus Radiology Institutional Websites: Comparative Analysis of Radiation Protection Information Provided to Patients.
(Radiology, 2024-06) Jankowski, Sofyan; Rotzinger, David; Ria, Francesco; Pozzessere, Chiara
Open Access
Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images
(Medical Imaging 2021: Computer-Aided Diagnosis, 2021-02-15) Fricks, Rafael; Abadi, Ehsan; Ria, Francesco; Samei, Ehsan
As computer-aided diagnostics develop to address new challenges in medical imaging, including emerging diseases such as COVID-19, the initial development is hampered by availability of imaging data. Deep learning algorithms are particularly notorious for performance that tends to improve proportionally to the amount of available data. Simulated images, as available through advanced virtual trials, may present an alternative in data-constrained applications. We begin with our previously trained COVID-19 x-ray classification model (denoted as CVX) that leveraged additional training with existing pre-pandemic chest radiographs to improve classification performance in a set of COVID-19 chest radiographs. The CVX model achieves demonstrably better performance on clinical images compared to an equivalent model that applies standard transfer learning from ImageNet weights. The higher performing CVX model is then shown to generalize effectively to a set of simulated COVID-19 images, both quantitative comparisons of AUCs from clinical to simulated image sets, but also in a qualitative sense where saliency map patterns are consistent when compared between sets. We then stratify the classification results in simulated images to examine dependencies in imaging parameters when patient features are constant. Simulated images show promise in optimizing imaging parameters for accurate classification in data-constrained applications.
Open Access
Clinical and radiation risk across one million patients in Computed Tomography: influence of age, size, and race
(2023-11-26) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Abadi, Ehsan; SOLOMON, justin; Marin, Daniele; Samei, Ehsan
Purpose. We recently developed a mathematical model to balance radiation risk and clinical risk, namely the risk of misdiagnosis due to insufficient image quality. In this work, we applied this model to a population of one million CT imaging cases to evaluate the risk stratification with different ages, sexes, and races. Materials and Methods. The demographics were informed by literature and census information simulating a clinical liver cancer population. The Total Risk (TR) was calculated as the linear combination of radiation risk and clinical risk. The model included factors for the radiation burden for different age and sex; the prevalence of the disease; the false positive rate; the expected life-expectancy loss for an incorrect diagnosis for different ages, sex, and race; and a typical false positive rate of 5%. It was assumed that each case received an average radiologist interpretative performance of 0.75 AUC for a hypothetical lesion without any changes in radiation dose beyond routine practice. We further, for each patient, simulated 2,000 imaging conditions with CTDIvol varying from 0.1 and 200 mGy with 0.1 mGy increments. Per each CTDIvol value, the anticipated AUC was calculated by applying the established asymptotic relationships between CTDIvol and image quality. The AUC distribution was then used to calculate the theoretical minimum total risk (TRmin) per each patient. Results. For the routine practice, the median theoretical total risk was estimated to be 0.058 deaths per 100 patients (range: 0.002 – 0.154) comprising of the median radiation risk of 0.009 (range: 0.001 – 0.069), and of the median clinical risk of 0.049 (range: 7.0x10-5 – 0.094). Considering the varying scanner output conditions, the median TRmin was 0.054 deaths per 100 patients for White male patients, 0.054 for Blacks, 0.057 for Hispanics, and 0.065 for Asians. For female patients, the median TRmin values were 0.049, 0.056, 0.054, and 0.061 deaths per 100 patients, respectively. Conclusion. For each demography condition, the clinical risk was found to largely outweigh the radiation risk by at least 500%. Total risk showed different stratifications with patient age and race. Clinical Relevance Statement. To optimize CT conditions for specific patients and/or population, both radiation risk and clinical risks should be all accounted for together with demographic information. We demonstrated a methodology that allows a complete depiction of total risk in CT, considering radiation and clinical risks at comparable units, and patient demographic.
Open Access
Clinical Decision Making in CT: Risk Assessment Comparison Across 12 Risk Metrics in Patient Populations
(Journal of Medical Physics, 2020-06-30) Ria, Francesco; Fu, wanyi; Hoye, Jocelyn; Segars, William; Kapadia, Anuj; Samei, Ehsan
Purpose The Medical Physics 3.0 initiative aims to enhance direct physicist involvement in clinical decision making to improve patient care. In this involvement, it is crucial to achieve effective and patient-specific radiation risk assessment. CT risk characterization presents a variety of metrics, many of which used as radiation risk surrogates; some are related to the device output (CTDI), whereas others include patient organ risk-, age-, and gender-factors (Effective Dose, Risk Index). It is unclear how different metrics can accurately reflect the radiological risk. This study compared how twelve metrics characterize risk across CT patient populations to inform effective clinical decision making in radiology. Methods This IRB-approved study included 1394 adult CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogate metrics were calculated: CTDIvol, DLP, SSDE, DLP-based Effective Dose (EDk), organ-dose-based ED (EDOD), dose to defining organ (stomach- and lungs-ODD), organ-dose-based Risk Index (RI), and 20 y.o. patient Risk Index (RIr). Furthermore, ODD,0, ED0, and RI0 were calculated for a reference patient (ICRP 110). Lastly, an adjusted ED (ED') was computed as the product of RI/RIr and EDOD. A linear regression was applied to assess each metric’s dependency to RI, assumed to be the closest patient risk surrogate. The normalized-slope (nS) and a Minimum Risk Detectability Index (MRDI=RMSE/slope) were calculated for each fit. Results The analysis reported significant differences between the metrics. ED’ showed the best concordance with RI in terms of nS and MRDI. Across all metrics and protocols, nS ranged between 0.37(SSDE) to 1.29(RI0); MRDI ranged between 39.11(EDk) to 1.10(ED’) cancers per 105 patients per 0.1Gy. Conclusion Radiation risk characterization in CT populations is strongly affected by the index used to describe it. When involved in clinical decisions, medical physicists should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.
Open Access
Clinical Decision Making in CT: Risk Assessment Comparison Across 12 Risk Metrics in Patient Populations
(Journal of Medical Physics, 2020-06-30) Ria, Francesco; Fu, Wanyi; Hoye, Jocelyn; Segars, William; Kapadia, Anuj; Samei, Ehsan
Purpose The Medical Physics 3.0 initiative aims to enhance direct physicist involvement in clinical decision making to improve patient care. In this involvement, it is crucial to achieve effective and patient-specific radiation risk assessment. CT risk characterization presents a variety of metrics, many of which used as radiation risk surrogates; some are related to the device output (CTDI), whereas others include patient organ risk-, age-, and gender-factors (Effective Dose, Risk Index). It is unclear how different metrics can accurately reflect the radiological risk. This study compared how twelve metrics characterize risk across CT patient populations to inform effective clinical decision making in radiology. Methods This IRB-approved study included 1394 adult CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogate metrics were calculated: CTDIvol, DLP, SSDE, DLP-based Effective Dose (EDk), organ-dose-based ED (EDOD), dose to defining organ (stomach- and lungs-ODD), organ-dose-based Risk Index (RI), and 20 y.o. patient Risk Index (RIr). Furthermore, ODD,0, ED0, and RI0 were calculated for a reference patient (ICRP 110). Lastly, an adjusted ED (ED') was computed as the product of RI/RIr and EDOD. A linear regression was applied to assess each metric’s dependency to RI, assumed to be the closest patient risk surrogate. The normalized-slope (nS) and a Minimum Risk Detectability Index (MRDI=RMSE/slope) were calculated for each fit. Results The analysis reported significant differences between the metrics. ED’ showed the best concordance with RI in terms of nS and MRDI. Across all metrics and protocols, nS ranged between 0.37(SSDE) to 1.29(RI0); MRDI ranged between 39.11(EDk) to 1.10(ED’) cancers per 105 patients per 0.1Gy. Conclusion Radiation risk characterization in CT populations is strongly affected by the index used to describe it. When involved in clinical decisions, medical physicists should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.
Open Access
Comparative Risk Assessment of Clinical and Radiation Risk across a Cohort of Patient and Individualized Risk Optimization
(https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.16525, 2023-07-23) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Abadi, Ehsan; SOLOMON, justin; Marin, daniele; Samei, Ehsan
Purpose Informed by a recent mathematical framework, we formulated an imaging strategy to balance interpretative performance-based clinical risk (i.e., false positive and false negative rates) and radiation risk as a risk-versus-risk assessment. The model was applied to a population of one million cases simulating a clinical liver cancer scenario. Moreover, a model was developed to predict individualized risk-versus-risk optimization. Methods The proposed model defined a Total Risk (TR) as the linear combination of radiation risk and clinical risk defined as functions of the radiation burden, the disease prevalence disease, the false positive rate, the expected life-expectancy loss for an incorrect diagnosis, and the radiologist interpretative performance (i.e., AUC). The mathematical framework was applied to a simulated dataset of 1,000,000 CT studies investigating localized stage liver cancer assuming a typical false positive rate of 5% and optimal imaging conditions (AUC=0.75). Demographic information was simulated according with literature and census data including male and female for different patient races (white, black, Asian, and Hispanic). Following BEIR-VII report, organ-specific radiation doses were used to calculate the radiation Risk Index per each patient. The model was then extended to predict the optimal scanner output associated with the TR for specific patients. Results Across all races and sexes, median radiation risk ranged between 0.008 and 0.012 number of deaths per 100 patients; median clinical risk ranged between 0.042 and 0.076; and medial total risk ranged between 0.010 and 0.088 deaths per 100 patients. The mathematical model was then generalized to estimate individualized optimal imaging condition minimizing TR. Conclusion A mathematical framework to describe total risk in CT was robustly tested in a simulated dataset of 1,000,000 CT studies. The results highlighted the dominance of clinical risk at typical CT examination dose levels. The generalization of the mathematical model allowed the prediction of individualized risk optimization.
Open Access
Comparison of 12 surrogates to characterize CT radiation risk across a clinical population.
(European radiology, 2021-02-23) Ria, Francesco; Fu, Wanyi; Hoye, Jocelyn; Segars, W Paul; Kapadia, Anuj J; Samei, Ehsan
Objectives
Quantifying radiation burden is essential for justification, optimization, and personalization of CT procedures and can be characterized by a variety of risk surrogates inducing different radiological risk reflections. This study compared how twelve such metrics can characterize risk across patient populations.
Methods
This study included 1394 CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogates were considered: volume computed tomography dose index (CTDI_vol), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (ED_k ), dose to a defining organ (OD_D), effective dose and risk index based on organ doses (ED_OD, RI), and risk index for a 20-year-old patient (RI_rp). The last three metrics were also calculated for a reference ICRP-110 model (OD_D,0, ED₀, and RI₀). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as [Formula: see text]. A linear regression was applied to assess each metric's dependency on RI. The results were characterized in terms of risk sensitivity index (RSI) and risk differentiability index (RDI).
Results
The analysis reported significant differences between the metrics with ED_r showing the best concordance with RI in terms of RSI and RDI. Across all metrics and protocols, RSI ranged between 0.37 (SSDE) and 1.29 (RI₀); RDI ranged between 0.39 (ED_k) and 0.01 (ED_r) cancers × 10³patients × 100 mGy.
Conclusion
Different risk surrogates lead to different population risk characterizations. ED_r exhibited a close characterization of population risk, also showing the best differentiability. Care should be exercised in drawing risk predictions from unrepresentative risk metrics applied to a population.
Key points
• Radiation risk characterization in CT populations is strongly affected by the surrogate used to describe it. • Different risk surrogates can lead to different characterization of population risk. • Healthcare professionals should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.
Open Access
Comparison of photon-counting and energy-integrating detector CT systems for the characterization of cystic renal lesions on virtual noncontrast imaging
(2023-11-26) Lofino, Ludovica; Schwartz, Fides; Al Tarhuni, Mohammed; Abadia, Andres; Ria, Francesco; Samei, Ehsan; Marin, Daniele
Purpose: The purpose of this study is to compare the absolute CT attenuation errors of cystic renal lesions and abdominal organs on virtual noncontrast images (VNC) between photon-counting (PCCT) and energy-integrating (EID) detector CT systems. Methods and Materials: In this HIPAA compliant, IRB-approved retrospective study, multiphase CT scans from one commercially available PCCT (NAEOTOM Alpha, Siemens Healthineers) and two EID dual-source dual-energy CT systems (SOMATOM Definition Flash and SOMATOM Force, Siemens Healthineers) were retrieved. A total of 56 BMI-matched patients (26 women, 30 men; mean age 58.5 ± 15.3 years; range 19-81 years, mean BMI 29.0 ± 6.8 kg/m2, range 13-47 kg/m2) were included: 16 for PCCT and 20 each per EID systems. Attenuation measurements of abdominal organs (liver, pancreas, spleen, kidney, and aorta) were recorded on VNC and True Noncontrast (TNC) datasets. Furthermore, attenuation measurements of 16 cystic renal lesions (eight for PCCT and eight for EID) were compared on VNC and TNC datasets. Absolute CT attenuation errors |HUVNC-HUTNC| were calculated and compared between PCCT and EID systems for the entire population and a subset of 20 obese patients (BMI: >30 kg/m2), using paired t-tests. Absolute CT attenuation errors were also compared for all cystic renal lesions and for renal lesions <1 cm, separately. *Results: PCCT yielded significantly lower absolute CT attenuation errors than EID using VNC in comparison with TNC images for the liver (4.3 ± 5.4 vs 8.8 ± 10.4), spleen (2.6 ± 6.2 vs 8.0 ± 10.3) and pancreas (4.4 ± 1.8 vs 7.7 ± 9.7) for all patients (P<0.01) and for spleen and pancreas in the obese patient cohort (P<0.05). Furthermore, PCCT yielded significantly lower absolute CT attenuation errors compared to EID for all cystic renal lesions (2.0 ± 1.3 vs. 12.0 ± 8.9; P<0.01) and for renal lesions <1 cm (1.4 ± 0.9 vs. 19.1 ± 6.8; P<0.01). Conclusions: PCCT yields significantly lower absolute CT attenuation errors for abdominal organs and cystic renal lesions in VNC images, compared to two dual-source dual-energy EID systems. Our results were corroborated in a subset of obese patients and small (<1 cm) renal lesions. Clinical Relevance/Application: Reliable CT attenuation values of virtual non-contrast imaging are necessary to replace true non-contrast acquisitions. This can be achieved with photon-counting CT with important implications in radiation dose reduction.
Open Access
Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training
(Journal of Medical Imaging, 2021-12-01) Fricks, Rafael B; Ria, Francesco; Chalian, Hamid; Khoshpouri, Pegah; Abadi, Ehsan; Bianchi, Lorenzo; Segars, William P; Samei, Ehsan
Open Access
Development and Testing of a Clinical Tool to Predict and Optimize Liver Contrast-Enhanced CT Imaging
(https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.16525, 2023-07-23) Setiawan, Hananiel; Abadi, Ehsan; Marin, Daniele; Molvin, Lior; Ria, Francesco; Samei, Ehsan
Achieving consistent and sufficient hepatic parenchyma contrast enhancement (HPCE) level can improve diagnostic performance and reduce enhancement variability; this raises the baseline image quality and optimize injection practices, both carries economic and safety implications. Patient factors, Iodine injection and scanning parameters (e.g. tube potential, scanning delay) affect HPCE in CT imaging. In this study, we developed and prospectively tested a clinical graphical user interface (GUI) tool which predicts enhancement level and suggests alternative injection/scanning parameters based on patient attributes (height, weight, sex, age). Methods: This work was based on our retrospectively-validated neural-network prediction model. We built a GUI to combine our model with an optimization algorithm, which suggests alternative injection/scanning parameters for patients with predicted-insufficient enhancement. The system was clinically-deployed and prospectively-tested in 24 patients considering a 110HU+/-10HU target portal-venous HPCE. For each patient, HPCE was calculated as the average HU-value of three ROIs and compared against the target value. Additionally, we compared the outcome with the patient’s previous similarly-protocoled scan to assess improvement and consistency. Results: The system suggested adjustment for 15 patients with median 8.8% and 9.1% reductions to volume and injection rate, respectively. All scan delays were reduced by an average of 42.6%. Comparison with previous scans shows increased consistency (CV=0.21 v. 0.11,p=0.012) while median enhancement remained relatively unchanged (111.3HU v. 108.7HU). The number of under-enhanced patients was halved, and all previously over-enhanced patients received enhancement reductions. Conclusion: We developed and tested a patient-informed clinical framework which predicts optimal patient’s HPCE; and suggests empiric injection/scanning parameters when predicted enhancement is deemed insufficient. The system improved HPCE consistency and decreased the number of under-enhanced patients as compared to their previous scans. This study demonstrated that the patient-informed clinical framework can predict an optimal patient's HPCE and suggest empiric injection/scanning parameters to achieve consistent and sufficient HPCE levels.
Open Access
Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols.
(Medical physics, 2022-06-11) Sharma, Shobhit; Kapadia, Anuj; Ria, Francesco; Segars, W Paul; Samei, Ehsan
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 BMIs at 10^th , 50^th , and 90^th percentiles of the US population. A 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) 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 (h_n ) were estimated by normalizing organ dose estimates to air kerma at 70 cm (X_70cm ) 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 (K_n ) 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 (h_n ) containing 294 distinct combinations of patients and exam protocols was developed and made publicly available. The values of K_n 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 = K_n /K_n,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 enable 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. This article is protected by copyright. All rights reserved.
Open Access
Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard
(Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952P, 2021-02-15) Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, Ehsan
Image quality estimation is crucial in modern CT with noise magnitude playing a key role. Several methods have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain the accuracy of three different noise-magnitude estimation methods. We used ensemble noise as the ground truth. The most accurate approach to assess ensemble noise is to scan a patient repeatedly and assess the noise for each pixel across the ensemble of images. This process is ethically undoable on actual patients. In this study, we surmounted this impasse using Virtual Imaging Trials (VITs) that simulate clinical scenarios using computer-based simulations. XCAT phantoms were imaged 47 times using a scanner-specific simulator (DukeSim) and reconstructed with filtered back projection (FBP) and iterative (IR) algorithms. Noise magnitudes were calculated in lung (ROIn), soft tissues (GNI), and air surrounding the patient (AIRn), applying different HU thresholds and techniques. The results were compared with the ensemble noise magnitudes within soft tissue (En). For the FBP-reconstructed images, median En was 30.6 HU; median ROIn was 46.6 HU (+52%), median GNI was 40.1 HU (+31%), and median AIRn 25.1 HU (-18%). For the IR images, median En was 19.5 HU; median ROIn was 31.2 HU (+60%), median GNI was 25.1 HU (+29%), and median AIRn 18.8 HU (-4%). Compared to ensemble noise, GNI and ROIn overestimate the tissue noise, while AIRn underestimates it. Air noise was least representative of variations in tissue noise due to imaging condition. These differences may be applied as adjustment or calibration factors to better represent clinical results.
Open Access
Evaluation and extension of in vivo detectability index to deep-learning and photon counting CT techniques
(2022-12-01) Ria, Francesco; Jensen, Corey; Zarei, Mojtaba; Liu, Xinming; Schwartz, Fides; Abbey, Craig; Samei, Ehsan
Open Access
Expanding the Concept of Diagnostic Reference Levels to Noise and Dose Reference Levels in CT.
(AJR. American journal of roentgenology, 2019-06-10) Ria, Francesco; Davis, Joseph T; Solomon, Justin B; Wilson, Joshua M; Smith, Taylor B; Frush, Donald P; Samei, Ehsan
OBJECTIVE. Diagnostic reference levels were developed as guidance for radiation dose in medical imaging and, by inference, diagnostic quality. The objective of this work was to expand the concept of diagnostic reference levels to explicitly include noise of CT examinations to simultaneously target both dose and quality through corresponding reference values. MATERIALS AND METHODS. The study consisted of 2851 adult CT examinations performed with scanners from two manufacturers and two clinical protocols: abdominopelvic CT with IV contrast administration and chest CT without IV contrast administration. An institutional informatics system was used to automatically extract protocol type, patient diameter, volume CT dose index, and noise magnitude from images. The data were divided into five reference patient size ranges. Noise reference level, noise reference range, dose reference level, and dose reference range were defined for each size range. RESULTS. The data exhibited strong dependence between dose and patient size, weak dependence between noise and patient size, and different trends for different manufacturers with differing strategies for tube current modulation. The results suggest size-based reference intervals and levels for noise and dose (e.g., noise reference level and noise reference range of 11.5-12.9 HU and 11.0-14.0 HU for chest CT and 10.1-12.1 HU and 9.4-13.7 HU for abdominopelvic CT examinations) that can be targeted to improve clinical performance consistency. CONCLUSION. New reference levels and ranges, which simultaneously consider image noise and radiation dose information across wide patient populations, were defined and determined for two clinical protocols. The methods of new quantitative constraints may provide unique and useful information about the goal of managing the variability of image quality and dose in clinical CT examinations.

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