Convolution-based estimation of organ dose in tube current modulated CT

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

PURPOSE

To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input.

METHODS

Despite recent advances in medical dosimetry, personalized CT dosimetry remains a labour-intensive process. Current state-of-the-art methods utilize time-consuming Monte Carlo (MC) based simulations for individual organ dose estimation in CT. The proposed method uses conditional generative adversarial networks (cGANs) to substitute MC simulations with fast dose image generation, based on image-to-image translation. The pix2pix architecture in conjunction with a regression model was utilized for the generation of the synthetic dose images. The lungs, heart, breast, bone and skin were manually segmented to estimate and compare organ doses calculated using both the original and synthetic dose images, respectively.

RESULTS

The average organ dose estimation error for the proposed method was 8.3% and did not exceed 20% for any of the organs considered. The performance of the method in the clinical environment was also assessed. Using segmentation tools developed in-house, an automatic organ dose calculation pipeline was set up. Calculation of organ doses for heart and lung for each CT slice took about 2 s.

CONCLUSIONS

This work shows that deep learning-enabled personalized CT dosimetry is feasible in real-time, using only patient CT images as input.

Organ dose in CT: Comparison between measurements and computational methods

PURPOSE

This study aims to compare two methods for the organ dose evaluation in computed tomography (CT) in the head- and thorax regions: an experimental method, using radiochromic films, and a computational one, using a commercial software.

METHODS

Gafchromic® XR-QA2 and EBT-3 were characterized in terms of energetic, angular, and irradiation configurations dependence. Two free-in-air irradiation calibration configurations were employed using a CT scanner: with the sensitive surface of the film orthogonal (OC) and parallel (PC) to the beam axis. Different dose-response curves were obtained by varying the irradiation configurations and the beam quality (BQ). Subsequently, films were irradiated within an anthropomorphic phantom using CT-thorax and -head protocols, and the organ dose values obtained were compared with those provided by the commercial software.

RESULTS

At different configurations, an unchanged dose response was achieved with EBT-3, while a dose response of 15% was obtained with XR-QA2. By varying BQ, XR-QA2 showed a different response below 10%, while EBT-3 showed a variation below 5% for dose values >20 mGy. For films irradiation angle equal to 90°, the normalized to 0° relative response was 41% for the XR-QA2 model and 83% for the EBT-3 one. Organ dose values obtained with EBT-3 for both configurations and with XR-QA2 for OC were in agreement with the DW values, showing percentage discrepancies of less than 25%.

CONCLUSIONS

The obtained results showed the potential of EBT-3 in CT patient dosimetry since the lower angular dependence, compared to XR-QA2, compensates for low sensitivity in the diagnostic dose range.

CT scanner-specific organ dose coefficients generated by Monte Carlo calculation for the ICRP adult male and female reference computational phantoms

Objective.Provide analyses of new organ dose coefficients (hereafter also referred to as normalized doses) for CT that have been developed to update the widely-utilized collection of data published 30 years ago in NRPB-SR250.Approach.In order to reflect changes in technology, and also ICRP recommendations concerning use of the computational phantoms adult male (AM) and adult female (AF), 102 series of new Monte Carlo simulations have been performed covering the range of operating conditions for 12 contemporary models of CT scanner from 4 manufacturers. Normalized doses (relative to free air on axis) have been determined for 39 organs, and for every 8 mm or 4.84 mm slab of AM and AF, respectively.Main results.Analyses of results confirm the significant influence (by up to a few tens of percent), on values of normalized organ (or contributions to effective dose (E_103,phan)), for whole body exposure arising from selection of tube voltage and beam shaping filter. Use of partial (when available) rather than a Full fan beam reduced both organ and effective dose by up to 7%. Normalized doses to AF were larger than corresponding figures for AM by up to 30% for organs and by 10% forE_103,phan. Additional simulations for whole body exposure have also demonstrated that: practical simplifications in the main modelling (point source, single slice thickness, neglect of patient couch and immobility of phantom arms) have sufficiently small (<5%) effect onE_103,phan; mis-centring of the phantom away from the axis of rotation by 5 mm (in any direction) leads to changes in normalized organ dose andE_103,phanby up to 20% and 6%, respectively; and angular tube current modulation can result in reductions by up to 35% and <15% in normalized organ dose andE_103,phan, respectively, for 100% cosine variation.Significance.These analyses help advance understanding of the influence of operational scanner settings on organ dose coefficients for contemporary CT, in support of improved patient protection. The results will allow the future development of a new dose estimation tool.

Patient-level dose monitoring in computed tomography: tracking cumulative dose from multiple multi-sequence exams with tube current modulation in children

BACKGROUND

In children exposed to multiple computed tomography (CT) exams, performed with varying z-axis coverage and often with tube current modulation, it is inaccurate to add volume CT dose index (CTDI_vol) and size-specific dose estimate (SSDE) to obtain cumulative dose values.

OBJECTIVE

To introduce the patient-size-specific z-axis dose profile and its dose line integral (DLI) as new dose metrics, and to use them to compare cumulative dose calculations against conventional measures.

MATERIALS AND METHODS

In all children with 2 or more abdominal-pelvic CT scans performed from 2013 through 2019, we retrospectively recorded all series kV, z-axis tube current profile, CTDI_vol, dose-length product (DLP) and calculated SSDE. We constructed dose profiles as a function of z-axis location for each series. One author identified the z-axis location of the superior mesenteric artery origin on each series obtained to align the dose profiles for construction of each patient's cumulative profile. We performed pair-wise comparisons between the peak dose of the cumulative patient dose profile and ΣSSDE, and between ΣDLI and ΣDLP.

RESULTS

We recorded dose data in 143 series obtained in 48 children, ages 0-2 years (n=15) and 8-16 years (n=33): ΣSSDE 12.7±6.7 and peak dose 15.1±8.1 mGy, ΣDLP 278±194 and ΣDLI 550±292 mGy·cm. Peak dose exceeded ΣSSDE by 20.6% (interquartile range [IQR]: 9.9-26.4%, P<0.001), and ΣDLI exceeded ΣDLP by 114% (IQR: 86.5-147.0%, P<0.001).

CONCLUSION

Our methodology represents a novel approach for evaluating radiation exposure in recurring pediatric abdominal CT examinations, both at the individual and population levels. Under a wide range of patient variables and acquisition conditions, graphic depiction of the cumulative z-axis dose profile across and beyond scan ranges, including the peak dose of the profile, provides a better tool for cumulative dose documentation than simple summations of SSDE. ΣDLI is advantageous in characterizing overall energy absorption over ΣDLP, which significantly underestimated this in all children.

A comprehensive Monte Carlo study of CT dose metrics proposed by the AAPM Reports 111 and 200

PURPOSE

A Monte Carlo (MC) modeling of single axial and helical CT scan modes has been developed to compute single and accumulated dose distributions. The radiation emission characteristics of an MDCT scanner has been modeled and used to evaluate the dose deposition in infinitely long head and body PMMA phantoms. The simulated accumulated dose distributions determined the approach to equilibrium function, H(L). From these H ( L ) curves, dose-related information was calculated for different head and body clinical protocols.

METHODS

The PENELOPE/penEasy package has been used to model the single axial and helical procedures and the radiation transport of photons and electrons in the phantoms. The bowtie filters, heel effect, focal-spot angle, and fan-beam geometry were incorporated. Head and body protocols with different pitch values were modeled for x-ray spectra corresponding to 80, 100, 120, and 140 kV. The analytical formulation for the single dose distributions and experimental measurements of single and accumulated dose distributions were employed to validate the MC results. The experimental dose distributions were measured with OSLDs and a thimble ion chamber inserted into PMMA phantoms. Also, the experimental values of the C T D I 100 along the center and peripheral axes of the CTDI phantom served to calibrate the simulated single and accumulated dose distributions.

RESULTS

The match of the simulated dose distributions with the reference data supports the correct modeling of the heel effect and the radiation transport in the phantom material reflected in the tails of the dose distributions. The validation of the x-ray source model was done comparing the CTDI ratios between simulated, measured and CTDosimetry data. The average difference of these ratios for head and body protocols between the simulated and measured data was in the range of 13-17% and between simulated and CTDosimetry data varied 10-13%. The distributions of simulated doses and those measured with the thimble ion chamber are compatible within 3%. In this study, it was demonstrated that the efficiencies of the C T D I 100 measurements in head phantoms with nT = 20 mm and 120 kV are 80.6% and 87.8% at central and peripheral axes, respectively. In the body phantoms with n T = 40 mm and 120 kV, the efficiencies are 56.5% and 86.2% at central and peripheral axes, respectively. In general terms, the clinical parameters such as pitch, beam intensity, and voltage affect the D_eq values with the increase of the pitch decreasing the D_eq and the beam intensity and the voltage increasing its value. The H(L) function does not change with the pitch values, but depends on the phantom axis (central or peripheral).

CONCLUSIONS

The computation of the pitch-equilibrium dose product, D ̂ eq , evidenced the limitations of the C T D I 100 method to determine the dose delivered by a CT scanner. Therefore, quantities derived from the C T D I 100 propagate this limitation. The developed MC model shows excellent compatibility with both measurements and literature quantities defined by AAPM Reports 111 and 200. These results demonstrate the robustness and versatility of the proposed modeling method.

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.

iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry

OBJECTIVE

This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients.

METHOD

Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams.

RESULT

iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors).

CONCLUSION

iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry.

SIGNIFICANCE

The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

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.

A comparison of COVID-19 and imaging radiation risk in clinical patient populations

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. 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. COVID-19 mortality showed a rapid rise for ages >30 years old (min: 0.30%; max: 30.20%), whereas only four deaths were reported in the analysed 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. 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.

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.

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.

Expanding the Concept of Diagnostic Reference Levels to Noise and Dose Reference Levels in CT

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.

Medical Physics 3.0: Ensuring Quality and Safety in Medical Imaging

Medical imaging often involves radiation and thus radiation protection. Radiation protection in medicine is only one component of the broader calling of health care professionals: fostering human health. As such, radiation risk needs to be put into the context of the larger mandate of improved outcomes in health care. Medical physicists, according to the new vision of Medical Physics 3.0, make a significant contribution to this mandate as they engage proactively and meaningfully in patient care. Facing the new realities of value-based, personalized, and evidence-based practice, Medical Physics 3.0 is an initiative to make physics inform every patient's care by fostering new skills and expanding horizons for the medical physics profession. It provides a framework by which medical physicists can maintain and improve their integral roles in, and contributions to, health care, its innovation, and its precision. One way that Medical Physics 3.0 will manifest itself in medical imaging practice is by engaging physicists to ensure the precise and optimized use of radiation. Optimization takes place through knowing the defining attributes of the technology in use, the specifics of the patient's situation, and the goals of the imaging and/or intervention. The safety as well as the quality of the procedure is ascertained quantitatively and optimized prospectively, ensuring a proper balance between quality and safety to offer maximum potential benefit to the patient. The results of procedures across the health care operation are then retrospectively analyzed to ensure that each procedure has, in actuality, delivered the targeted quality and safety objectives. Characterizing quality and safety in quantitative terms, objectively optimizing them in the practice of personalized care, and analyzing the results from clinical operations all require the unique combination of precision and innovation that physicists bring to the development and practice of medicine.

Estimating lung, breast, and effective dose from low-dose lung cancer screening CT exams with tube current modulation across a range of patient sizes

PURPOSE

The purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes.

METHODS

Monte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low-dose LCS protocol for a 64-slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi-automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDI_vol , dose length product (DLP), and CTDI_{vol, Low Att} , a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan-specific CTDI_vol and CTDI_{vol, Low Att} . Effective doses were normalized by scan-specific CTDI_vol and DLP. Absolute and normalized doses were reported as a function of Dw.

RESULTS

Lung doses normalized by CTDI_{vol, Low Att} were modeled as an exponential relationship with respect to Dw with coefficients of determination (R² ) of 0.80. Breast dose normalized by CTDI_{vol, Low Att} was modeled with an exponential relationship to Dw with an R² of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior.

CONCLUSION

Lung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDI_vol covering the thorax such as CTDI_{vol, Low Att} , while normalized breast dose can also be estimated with a regional metric of CTDI_vol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier.

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

Model-based dose reconstruction for CT dose estimation

PURPOSE

Our goal is to develop a model-based approach for CT dose estimation. We previously presented a CT dose estimation method that offered good accuracy in soft tissue regions but lower accuracy in bone regions. In this work, we propose an improved physic-based approach to achieve high accuracy for any materials and realistic clinical anatomies.

METHODS

Like Monte Carlo techniques, we start from a model or image of the patient and we model all relevant x-ray interaction processes. Unlike Monte Carlo techniques, we do not track each individual photon, but we compute the average behavior of the x-ray interactions, combining pencil-beam calculations for the first-order interactions and kernels for the higher order interactions. The new algorithm more accurately models the variation of materials in the human body, especially for higher attenuation materials such as bone, as well as the various x-ray attenuation processes. We performed validation experiments with analytic phantoms and a polychromatic x-ray spectrum, comparing to Monte Carlo simulation (GEANT4) as the ground truth.

RESULTS

The results show that the proposed method has improved accuracy in both soft tissue region and bone region: less than 6% voxel-wise errors and less than 3.2% ROI-based errors in an anthropomorphic phantom. The computational cost is on the order of a low-resolution filtered backprojection reconstruction.

CONCLUSIONS

We introduced improved physics-based models in a fast CT dose reconstruction approach. The improved approach demonstrated quantitatively good correspondence to a Monte Carlo gold standard in both soft tissue and bone regions in a chest phantom with a realistic polychromatic spectrum and could potentially be used for real-time applications such as patient- and organ-specific scan planning and organ dose reporting.

Characterization of radiation dose from tube current modulated CT examinations with considerations of both patient size and variable tube current

PURPOSE

The volume CT dose index (CTDI_vol ) and the size-specific dose estimate (SSDE) are widely used for monitoring patient dose from CT examinations. Both metrics may represent the average dose over the central scan plane of the CTDI phantom or the patient under constant tube current (mA), but they are not intended for the tube current modulation (TCM)-enabled CT examinations, in which the peak dose across the scanned range may not be at the scan range center. To overcome the limitation, this paper illustrates an alternative approach, its implementation, and the relationship between longitudinal dose distribution D_L (z) in water cylinder and mA line shape, scan length, as well as phantom diameter.

METHODS

A dose calculation algorithm and the published data by Li et al. [Med. Phys. 40, 031903 (10pp.) (2013); 41, 111910 (5pp.) (2014)] were used to calculate D_L (z) for the central and peripheral axes of 10- to 50-cm diameter water phantoms undergoing CT scans of one constant and three variable mA distributions, each of which in three scan lengths of 10, 28.6, and 50 cm. All scans had an identical average tube current over the scan ranges. The results in the scanned ranges were used to assess the D_L (z) to mA(z) ratios, and their coefficients of variation (CV = stdev/mean) were used to compare the line shapes of D_L (z) and mA(z) for congruence: identical line shapes would result in CV = 0, but largely different line shapes would result in high CV.

RESULTS

In 30-cm diameter water phantom, the line shape of D_L (z) was largely different from that of mA(z). CV was higher in a variable mA scan than in a constant mA scan. As the scan length of variable mA scan increased, CV mostly decreased, and the line shape of D_L (z) more closely resembled that of mA(z). When two phantom axes were compared, CV was smaller and the line shape of D_L (z) more closely resembled that of mA(z) on the peripheral axis than on the central axis. In 41 water phantoms included in this study, CV mostly increased with phantom diameter, and approached the limiting levels on the peripheral axes of large phantoms. In constant mA scans, CV ranged from 5.5% to 14.0% on the phantom central axes and from 4.6% to 6.4% on the phantom peripheral axes. However, in variable tube current scans, CV ranged from 7.4% to 70.0% on the phantom central axes and from 5.1% to 35.9% on the phantom peripheral axes.

CONCLUSION

D_L (z) (water) may be advantageous over current CT dose metrics in characterizing the dose dependences on both patient size and mA line shape from tube current modulated examinations. Evaluating D_L (z) (water) with the water equivalent diameter and tube current curve from clinical examinations has a potential to improve CT dose monitoring program.

Estimating organ doses from tube current modulated CT examinations using a generalized linear model

PURPOSE

Currently, available Computed Tomography dose metrics are mostly based on fixed tube current Monte Carlo (MC) simulations and/or physical measurements such as the size specific dose estimate (SSDE). In addition to not being able to account for Tube Current Modulation (TCM), these dose metrics do not represent actual patient dose. The purpose of this study was to generate and evaluate a dose estimation model based on the Generalized Linear Model (GLM), which extends the ability to estimate organ dose from tube current modulated examinations by incorporating regional descriptors of patient size, scanner output, and other scan-specific variables as needed.

METHODS

The collection of a total of 332 patient CT scans at four different institutions was approved by each institution's IRB and used to generate and test organ dose estimation models. The patient population consisted of pediatric and adult patients and included thoracic and abdomen/pelvis scans. The scans were performed on three different CT scanner systems. Manual segmentation of organs, depending on the examined anatomy, was performed on each patient's image series. In addition to the collected images, detailed TCM data were collected for all patients scanned on Siemens CT scanners, while for all GE and Toshiba patients, data representing z-axis-only TCM, extracted from the DICOM header of the images, were used for TCM simulations. A validated MC dosimetry package was used to perform detailed simulation of CT examinations on all 332 patient models to estimate dose to each segmented organ (lungs, breasts, liver, spleen, and kidneys), denoted as reference organ dose values. Approximately 60% of the data were used to train a dose estimation model, while the remaining 40% was used to evaluate performance. Two different methodologies were explored using GLM to generate a dose estimation model: (a) using the conventional exponential relationship between normalized organ dose and size with regional water equivalent diameter (WED) and regional CTDI_vol as variables and (b) using the same exponential relationship with the addition of categorical variables such as scanner model and organ to provide a more complete estimate of factors that may affect organ dose. Finally, estimates from generated models were compared to those obtained from SSDE and ImPACT.

RESULTS

The Generalized Linear Model yielded organ dose estimates that were significantly closer to the MC reference organ dose values than were organ doses estimated via SSDE or ImPACT. Moreover, the GLM estimates were better than those of SSDE or ImPACT irrespective of whether or not categorical variables were used in the model. While the improvement associated with a categorical variable was substantial in estimating breast dose, the improvement was minor for other organs.

CONCLUSIONS

The GLM approach extends the current CT dose estimation methods by allowing the use of additional variables to more accurately estimate organ dose from TCM scans. Thus, this approach may be able to overcome the limitations of current CT dose metrics to provide more accurate estimates of patient dose, in particular, dose to organs with considerable variability across the population.