Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose.

An updated extension of effective dose was recently introduced, namely relative effective dose ( E r ), incorporating age and sex factors. In this study we extended E r 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, E r , dose-length-product based effective dose ( E DLP ), organ-dose based effective dose ( E OD ), 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. E r showed the best sensitivity, specificity, and agreement with RI (R2 = 0.97); while E DLP yielded the lowest specificity and, along with E OD , the lowest sensitivity. Compared to other metrics, E r provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.

A systematic assessment and optimization of photon-counting CT for lung density quantifications.

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.

A systematic task-based image quality assessment of photon-counting and energy integrating CT as a function of reconstruction kernel and phantom size.

BACKGROUND: Image quality of photon-counting and energy integrating CT scanners changes with object size, dose to the object, and kernel selection. PURPOSE: To comprehensively compare task-generic image quality of photon-counting CT (PCCT) and energy integrating CT (EICT) systems as a function of phantom size, dose, and reconstruction kernel. METHODS: A size-variant phantom (Mercury Phantom 3.0) was used to characterize the image quality of PCCT and EICT systems as a function of object size. The phantom contained five cylinders attached by slanted tapered sections. Each cylinder contained two sections: one uniform for noise, and the other with inserts for spatial resolution and contrast measurements. The phantom was scanned on Siemens' SOMATOM Force and NAEOTOM Alpha at 1.18 and 7.51 mGy without tube current modulation. CTDIvol was matched across two systems by setting the required tube currents, else, all other acquisition settings were fixed. CT sinograms were reconstructed using FBP and iterative (ADMIRE2 - Force; QIR2 - Alpha) algorithms with Body regular (Br) kernels. Noise Power Spectrum (NPS), Task Transfer Function (TTF), contrast-to-noise ratio (CNR), and detectability index (d') for a task of identifying 2-mm disk were evaluated based on AAPM TG-233 metrology using imQuest, an open-source software package. Averaged noise frequency (fav ) and 50% cut-off frequency for TTF (f50 ) were used as scalar metrics to quantify noise texture and spatial resolution, respectively. The difference between image quality metrics' measurements was calculated as IQPCCT - IQEICT . RESULTS: From Br40 (soft) to Br64 (sharp), f50 for air insert increased from 0.35 mm-1  ± 0.04 (standard deviation) to 0.76 mm-1  ± 0.17, 0.34 mm-1  ± 0.04 to 0.77 mm-1  ± 0.17, 0.37 mm-1  ± 0.02 to 0.95 mm-1  ± 0.17 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively, when averaged over all sizes and dose levels. Similarly, from Br40 to Br64, noise magnitude increased from 10.86 HU ± 7.12 to 38.61 HU ± 18.84, 10.94 HU ± 7.08 to 38.82 HU ± 18.70, 13.74 HU ± 11.02 to 52.11 HU ± 26.22 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. The difference in fav was consistent across all sizes and dose levels. PCCT-70keV-VMI showed better consistency in contrast measurements for iodine and bone inserts than PCCT-T3D and EICT; however, PCCT-T3D had higher contrast for both inserts. From Br40 to Br64, considering all sizes and dose levels, CNR for iodine insert decreased from 52.30 ± 46.44 to 12.18 ± 10.07, 40.42 ± 33.42 to 9.48 ± 7.16, 39.94 ± 37.60 to 7.84 ± 6.67 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. CONCLUSIONS: Both PCCT image types, T3D and 70-keV-VMI exhibited similar or better noise, contrast, CNR than EICT when comparing kernels with similar names. For 512 × 512 matrix, PCCT's sharp kernels had lower resolution than EICT's sharp kernels. For all image quality metrics, except extreme low, every dose condition had a similar set of IQ-matching kernels. It suggests that considering patient size and dose level to determine IQ-matching kernel pairs across PCCT and EICT systems may not be imperative while translating protocols, except when the signal to the detector is extremely low.

A systematic assessment of photon-counting CT for bone mineral density and microarchitecture quantifications.

Photon-counting CT (PCCT) is an emerging imaging technology with potential improvements in quantification and rendition of micro-structures due to its smaller detector sizes. The aim of this study was to assess the performance of a new PCCT scanner (NAEOTOM Alpha, Siemens) in quantifying clinically relevant bone imaging biomarkers for characterization of common bone diseases. We evaluated the ability of PCCT in quantifying microarchitecture in bones compared to conventional energy-integrating CT. The quantifications were done through virtual imaging trials, using a 50 percentile BMI male virtual patient, with a detailed model of trabecular bone with varied bone densities in the lumbar spine. The virtual patient was imaged using a validated CT simulator (DukeSim) at CTDIvol of 20 and 40 mGy for three scan modes: ultra-high-resolution PCCT (UHR-PCCT), high-resolution PCCT (HR-PCCT), and a conventional energy-integrating CT (EICT) (FORCE, Siemens). Further, each scan mode was reconstructed with varying parameters to evaluate their effect on quantification. Bone mineral density (BMD), trabecular volume to total bone volume (BV/TV), and radiomics texture features were calculated in each vertebra. The most accurate BMD measurements relative to the ground truth were UHR-PCCT images (error: 3.3% ± 1.5%), compared to HR-PCCT (error: 5.3% ± 2.0%) and EICT (error: 7.1% ± 2.0%). UHR-PCCT images outperformed EICT and HR-PCCT. In BV/TV quantifications, UHR-PCCT (errors of 29.7% ± 11.8%) outperformed HR-PCCT (error: 80.6% ± 31.4%) and EICT (error: 67.3% ± 64.3). UHR-PCCT and HR-PCCT texture features were sensitive to anatomical changes using the sharpest kernel. Conversely, the texture radiomics showed no clear trend to reflect the progression of the disease in EICT. This study demonstrated the potential utility of PCCT technology in improved performance of bone quantifications leading to more accurate characterization of bone diseases.

Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols.

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.

Medical physics 3.0: A renewed model for practicing medical physics in clinical imaging.

Inspired by the principles of Medical Physics 3.0, this paper frames a new model of clinical physics practice, anchored to clinical realities, clinical priorities, and advanced physics. The model is based on the conviction that the physicist is vested with the expertise and the responsibility to ensure each patient gets the optimum imaging exam towards the best clinical outcome. Key expectations and activities are encapsulated into 12 areas: scientific perspective, quality and safety assurance, regulatory compliance, technology assessment, use optimization, performance monitoring, technology acquisition, technology commissioning, vendor cooperation, translational practice, research consultancy, and technology education. The paper further highlights key challenges to effective clinical physics practice of increased scope of competency, balancing rigor and relevance, managing metrological surrogates of quality and safety, and integrating principle- and data-informed approaches. Mindful to practically mitigate these challenges, clinical imaging physics can play an essential role to enable evidence-based imaging care.

A GPU-accelerated framework for individualized estimation of organ doses in digital tomosynthesis.

PURPOSE: Estimation of organ doses in digital tomosynthesis (DT) is challenging due to the lack of existing tools that accurately and flexibly model protocol- and view-specific collimations and motion trajectories of the source and detector for a variety of exam protocols, and the computational inefficiencies of conducting MC simulations. The purpose of this study was to overcome these limitations by developing and benchmarking a GPU-accelerated MC simulation framework compatible with patient-specific computational phantoms for individualized estimation of organ doses in DT. MATERIALS AND METHODS: The framework for individualized estimation of dose in DT was developed as a two-step workflow: (1) a custom MATLAB code that accepts a patient-specific computational phantom and exam description (organ markers for defining the extremities of the anatomical region of interest, tube voltage, source-to-image distance, angular sweep range, number of projection views, and the pivot point to image distance - PPID) to compute the field of views (FOVs) for a clinical DT system, and (2) a MC tool (developed using MC-GPU) modeling the configuration of a clinical DT system to estimate organ doses based on the computed FOVs. Using this framework, we estimated organ doses for 28 radiosensitive organs in an adult reference patient model (M; 30 years) imaged using a commercial DT system (VolumeRad, GE Healthcare, Waukesha, WI). The estimates were benchmarked against values from a comparable organ dose estimation framework (reference dataset developed by the Advanced Laboratory for Radiation Dosimetry Studies at University of Florida) for a posterior-anterior chest exam. The resulting differences were quantified as percent relative errors and analyzed to identify any potential sources of bias and uncertainties. The timing performance (run duration in seconds) of the framework was also quantified for the same simulation to gauge the feasibility of the workflow for time-constrained clinical applications. RESULTS: The organ dose estimates from the developed framework showed a close agreement with the reference dataset, with percent relative errors ranging from -6.9% to 5.0% and a mean absolute percent difference of 1.7% over all radiosensitive organs, with the exception of testes and eye lens, for which the percent relative errors were higher at -18.9% and -27.6%, respectively, due to their relative positioning outside the primary irradiation field, leading to fewer photons depositing energy and consequently higher errors in estimated organ doses. The run duration for the same simulation was 916.3 s, representing a substantial improvement in performance over existing nonparallelized MC tools. CONCLUSIONS: This study successfully developed and benchmarked a GPU-accelerated framework compatible with patient-specific anthropomorphic computational phantoms for accurate individualized estimation of organ doses in DT. By enabling patient-specific estimation of organ doses, this framework can aid clinicians and researchers by providing them with tools essential for tracking the radiation burden to patients for dose monitoring purposes and identifying the trends and relationships in organ doses for a patient population to optimize existing and develop new exam protocols.

Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT.

PURPOSE: To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model. MATERIALS AND METHODS: An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study. RESULTS: Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85). CONCLUSION: Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.

A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials.

Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.

Assessment of pleural invasion and adhesion of lung tumors with dynamic chest radiography: A virtual clinical imaging study.

PURPOSE: Accurate preoperative assessment of tumor invasion/adhesion is crucial for planning appropriate operative procedures. Recent advances in digital radiography allow a motion analysis of lung tumors with dynamic chest radiography (DCR) with total exposure dose comparable to that of conventional chest radiography. The aim of this study was to investigate the feasibility of preoperative evaluation of pleural invasion/adhesion of lung tumors with DCR through a virtual clinical imaging study, using a four-dimensional (4D) extended cardiac-torso (XCAT) computational phantom. METHODS: An XCAT phantom of an adult man (50th percentile in height and weight) with simulated respiratory and cardiac motions was generated to use as a virtual patient. To simulate lung tumors with and without pleural invasion, a 30-mm diameter tumor sphere was inserted into each lobe of the phantom. The virtual patient during respiration was virtually projected using an x-ray simulator in posteroanterior (PA) and oblique directions, and sequential bone suppression (BS) images were created. The measurement points (tumor, rib, and diaphragm) were automatically tracked on simulated images by a template matching technique. We calculated five quantitative metrics related to the movement distance and directions of the targeted tumor and evaluated whether DCR could distinguish between tumors with and without pleural invasion/adhesion. RESULTS: Precise tracking of the targeted tumor was achieved on the simulated BS images without undue influence of rib shadows. There was a significant difference in all five quantitative metrics between the lung tumors with and without pleural invasion both on the oblique and PA projection views (P < 0.05). Quantitative metrics related to the movement distance were effective for tumors in the middle and lower lobes, while, those related to the movement directions were effective for tumors close to the frontal chest wall on the oblique projection view. The oblique views were useful for the evaluation of the space between the chest wall and a moving tumor. CONCLUSION: DCR could help distinguish between tumors with and without pleural invasion/adhesion based on the two-dimensional movement distance and direction using oblique and PA projection views. With anticipated improved image: processing to evaluate the respiratory displacement of lung tumors in the upper lobe or behind the heart, DCR holds promise for clinical assessment of tumor invasion/adhesion in the parietal pleura.

Comparison of 12 surrogates to characterize CT radiation risk across a clinical population.

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 (CTDIvol), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (EDk ), dose to a defining organ (ODD), effective dose and risk index based on organ doses (EDOD, RI), and risk index for a 20-year-old patient (RIrp). The last three metrics were also calculated for a reference ICRP-110 model (ODD,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 EDr 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 (EDk) and 0.01 (EDr) cancers × 103patients × 100 mGy. CONCLUSION: Different risk surrogates lead to different population risk characterizations. EDr 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.

Patient-Informed Organ Dose Estimation in Clinical CT: Implementation and Effective Dose Assessment in 1048 Clinical Patients.

OBJECTIVE. The purpose of this study is to comprehensively implement a patient-informed organ dose monitoring framework for clinical CT and compare the effective dose (ED) according to the patient-informed organ dose with ED according to the dose-length product (DLP) in 1048 patients. MATERIALS AND METHODS. Organ doses for a given examination are computed by matching the topogram to a computational phantom from a library of anthropomorphic phantoms and scaling the fixed tube current dose coefficients by the examination volume CT dose index (CTDIvol) and the tube-current modulation using a previously validated convolution-based technique. In this study, the library was expanded to 58 adult, 56 pediatric, five pregnant, and 12 International Commission on Radiological Protection (ICRP) reference models, and the technique was extended to include multiple protocols, a bias correction, and uncertainty estimates. The method was implemented in a clinical monitoring system to estimate organ dose and organ dose-based ED for 647 abdomen-pelvis and 401 chest examinations, which were compared with DLP-based ED using a t test. RESULTS. For the majority of the organs, the maximum errors in organ dose estimation were 18% and 8%, averaged across all protocols, without and with bias correction, respectively. For the patient examinations, DLP-based ED was significantly different from organ dose-based ED by as much as 190.9% and 234.7% for chest and abdomen-pelvis scans, respectively (mean, 9.0% and 24.3%). The differences were statistically significant (p < .001) and exhibited overestimation for larger-sized patients and underestimation for smaller-sized patients. CONCLUSION. A patient-informed organ dose estimation framework was comprehensively implemented applicable to clinical imaging of adult, pediatric, and pregnant patients. Compared with organ dose-based ED, DLP-based ED may overestimate effective dose for larger-sized patients and underestimate it for smaller-sized patients.

Patient-based Performance Assessment for Pediatric Abdominal CT: An Automated Monitoring System Based on Lesion Detectability and Radiation Dose.

RATIONALE AND OBJECTIVE: To deploy an automated tool for evaluating pediatric body computed tomography (CT) performance utilizing metrics of radiation dose and image quality for the task of liver lesion detection. MATERIALS AND METHODS: This IRB approved retrospective investigation used 507 IV-contrast-enhanced abdominopelvic CT scans of pediatric patients (<18 years) between June 2014 and November 2017 acquired on three scanner models from two manufacturers. The scans were evaluated in terms of radiation metrics (CTDIvol, DLP, and SSDE) as well as task-based performance based on the clinical task of detecting a 5 mm liver lesion with a 10 HU attenuation difference from background liver. An informatics algorithm extracted a previously-validated quantitative detectability index (d') from each case reflective of the likelihood of detecting a liver lesion. The results were analyzed in terms of the relationship between d' and radiation dose metrics. RESULTS: There was minimal SSDE variability by age. Median SSDE at 100 kV on one scanner model was 5.2 mGy (5.0-5.4 mGy interquartile range). However, when assessing image quality by applying d', the age groups separated such that the younger patients had higher d' values than older patients. Similar trends were seen in all scanners. CONCLUSIONS: An automated method to assess clinical image quality for pediatric CT provided a metric of image quality that varied as expected across ages (i.e., higher quality for younger patients). This tool affords the establishment of a quality reference level that, in addition to dose estimations currently available, would allow for enhanced assessment (e.g., facilitated audit) of CT imaging performance.

A database of 40 patient-based computational models for benchmarking organ dose estimates in CT.

PURPOSE: Patient radiation burden in computed tomography (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 male, 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 risk assessment of CT scans based on organ dose.

Correlation of Algorithmic and Visual Assessment of Lesion Detection in Clinical Images.

RATIONALE AND OBJECTIVES: Clinically-relevant quantitative measures of task-based image quality play key roles in effective optimization of medical imaging systems. Conventional phantom-based measures do not adequately reflect the real-world image quality of clinical Computed Tomography (CT) series which is most relevant for diagnostic decision-making. The assessment of detectability index which incorporates measurements of essential image quality metrics on patient CT images can overcome this limitation. Our current investigation extends and validates the technique on standard-of-care clinical cases. MATERIALS AND METHODS: We obtained a clinical CT image dataset from an Institutional Review Board-approved prospective study on colorectal adenocarcinoma patients for detecting hepatic metastasis. For this study, both perceptual image quality and lesion detection performance of same-patient CT image series with standard and low dose acquisitions in the same breath hold and four processing algorithms applied to each acquisition were assessed and ranked by expert radiologists. The clinical CT image dataset was processed using the previously validated method to estimate a detectability index for each known lesion size in the size distribution of hepatic lesions relevant for the imaging task and for each slice of a CT series. We then combined these lesion-size-specific and slice-specific detectability indexes with the size distribution of hepatic lesions relevant for the imaging task to compute an effective detectability index for a clinical CT imaging condition of a patient. The assessed effective detectability indexes were used to rank task-based image quality of different imaging conditions on the same patient for all patients. We compared the assessments to those by expert radiologists in the prospective study in terms of rank order agreement between the rankings of algorithmic and visual assessment of lesion detection and perceptual quality. RESULTS: Our investigation indicated that algorithmic assessment of lesion detection and perceptual quality can predict observer assessment for detecting hepatic metastasis. The algorithmic and visual assessment of lesion detection and perceptual quality are strongly correlated using both the Kendall's Tau and Spearman's Rho methods (perfect agreement has value 1): for assessment of lesion detection, 95% of the patients have rank correlation coefficients values exceeding 0.87 and 0.94, respectively, and for assessment of perceptual quality, 0.85 and 0.94, respectively. CONCLUSION: This study used algorithmic detectability index to assess task-based image equality for detecting hepatic lesions and validated it against observer rankings on standard-of-care clinical CT cases. Our study indicates that detectability index provides a robust reflection of overall image quality for detecting hepatic lesions under clinical CT imaging conditions. This demonstrates the concept of utilizing the measure to quantitatively assess the quality of the information content that different imaging conditions can provide for the same clinical imaging task, which enables targeted optimization of clinical CT systems to minimize clinical and patient risks.

Why physics in medicine?

Despite its crucial role in the development of new medical imaging technologies, in clinical practice, physics has primarily been involved in technical evaluation of technologies. However, this narrow role is no longer adequate. New trajectories in medicine call for a stronger role for physics in the clinic. The movement towards evidence-based, quantitative, and value-based medicine requires physicists to play a more integral role in delivering innovative precision care through the intentional clinical application of physical sciences. There are three aspects of this clinical role: technology assessment based on metrics as they relate to expected clinical performance, optimized use of technologies for patient-centered clinical outcomes, and retrospective analysis of imaging operations to ensure attainment of expectations in terms of quality and variability. These tasks fuel the drive towards high-quality, consistent practice of medical imaging that is patient-centered, evidence-based, and safe. While this particular article focuses on imaging, this trajectory and paradigm is equally applicable to the multitudes of the applications of physics in medicine.

A real-time Monte Carlo tool for individualized dose estimations in clinical CT.

The increasing awareness of the adverse effects associated with radiation exposure in computed tomography (CT) has necessesitated the quantification of dose delivered to patients for better risk assessment in the clinic. The current methods for dose quantification used in the clinic are approximations, lacking realistic models for the irradiation conditions utilized in the scan and the anatomy of the patient being imaged, which limits their relevance for a particular patient. The established gold-standard technique for individualized dose quantification uses Monte Carlo (MC) simulations to obtain patient-specific estimates of organ dose in anatomically realistic computational phantoms to provide patient-specific estimates of organ dose. Although accurate, MC simulations are computationally expensive, which limits their utility for time-constrained applications in the clinic. To overcome these shortcomings, a real-time GPU-based MC tool based on FDA's MC-GPU framework was developed for patient and scanner-specific dosimetry in the clinic. The tool was validated against (1) AAPM's TG-195 reference datasets and (2) physical measurements of dose acquired using TLD chips in adult and pediatric anthropomorphic phantoms. To demonstrate its utility towards providing individualized dose estimates, it was integrated with an automatic segmentation method for generating patient-specific models, which were then used to estimate patient- and scanner-specific organ doses for a select population of 50 adult patients using a clinically relevant CT protocol. The organ dose estimates were compared to corresponding dose estimates from a previously validated MC method based on Penelope. The dose estimates from our MC tool agreed within 5% for all organs (except thyroid) tabulated by TG-195 and within 10% for all TLD locations in the adult and pediactric phantoms, across all validation cases. Compared against Penelope, the organ dose estimates agreed within 3% on average for all organs in the patient population study. The average run duration for each patient was estimated at 23.79 s, representing a significant speedup (~700×) over existing non-parallelized MC methods. The accuracy of dose estimates combined with a significant improvement in execution times suggests a feasible solution utilizing the proposed MC tool for real-time individualized dosimetry in the clinic.

Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique.

PURPOSE: The purpose of this study was to simulate and validate organ doses from different computed tomography (CT) localizer radiograph geometries using Monte Carlo methods for a population of patients. METHODS: A Monte Carlo method was developed to estimate organ doses from CT localizer radiographs using PENELOPE. The method was validated by comparing dosimetry estimates with measurements using an anthropomorphic phantom imbedded with thermoluminescent dosimeters (TLDs) scanned on a commercial CT system (Siemens SOMATOM Flash). The Monte Carlo simulation platform was then applied to conduct a population study with 57 adult computational phantoms (XCAT). In the population study, clinically relevant chest localizer protocols were simulated with the x-ray tube in anterior-posterior (AP), right lateral, and PA positions. Mean organ doses and associated standard deviations (in mGy) were then estimated for all simulations. The obtained organ doses were studied as a function of patient chest diameter. Organ doses for breast and lung were compared across different views and represented as a percentage of organ doses from rotational CT scans. RESULTS: The validation study showed an agreement between the Monte Carlo and physical TLD measurements with a maximum percent difference of 15.5% and a mean difference of 3.5% across all organs. The XCAT population study showed that breast dose from AP localizers was the highest with a mean value of 0.24 mGy across patients, while the lung dose was relatively consistent across different localizer geometries. The organ dose estimates were found to vary across the patient population, partially explained by the changes in the patient chest diameter. The average effective dose was 0.18 mGy for AP, 0.09 mGy for lateral, and 0.08 mGy for PA localizer. CONCLUSION: A platform to estimate organ doses in CT localizer scans using Monte Carlo methods was implemented and validated based on comparison with physical dose measurements. The simulation platform was applied to a virtual patient population, where the localizer organ doses were found to range within 0.4%-8.6% of corresponding organ doses for a typical CT scan, 0.2%-3.3% of organ doses for a CT pulmonary angiography scan, and 1.1%-20.8% of organ doses for a low-dose lung cancer screening scan.

The Need for Practical and Accurate Measures of Value for Radiology.

Radiologists play a critical role in helping the health care system achieve greater value. Unfortunately, today radiology is often judged by simple "checkbox" metrics, which neither directly reflect the value radiologists provide nor the outcomes they help drive. To change this system, first, we must attempt to better define the elusive term value and, then, quantify the value of imaging through more relevant and meaningful metrics that can be more directly correlated with outcomes. This framework can further improve radiology's value by enhancing radiologists' integration into the care team and their engagement with patients. With these improvements, we can maximize the value of imaging in the overall care of patients.

DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography.

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT scanner (Somatom Definition Flash; Siemens Healthcare). The real and simulated images were compared in terms of image contrast, noise magnitude, noise texture, and spatial resolution. The relative error between the clinical and simulated images was less than 1.4%, 0.5%, 2.6%, and 3%, for image contrast, noise magnitude, noise texture, and spatial resolution, respectively, demonstrating the high realism of DukeSim. The runtime, dependent on the imaging task and the hardware, was approximately 2-3 minutes per rotation in our study using a computer with 4 GPUs. DukeSim, when combined with realistic human phantoms, provides the necessary toolset with which to perform large-scale and realistic virtual clinical trials in a patient and scanner-specific manner.