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.

Fetal XCMR: a numerical phantom for fetal cardiovascular magnetic resonance imaging.

BACKGROUND: Validating new techniques for fetal cardiovascular magnetic resonance (CMR) is challenging due to random fetal movement that precludes repeat measurements. Consequently, fetal CMR development has been largely performed using physical phantoms or postnatal volunteers. In this work, we present an open-source simulation designed to aid in the development and validation of new approaches for fetal CMR. Our approach, fetal extended Cardiac-Torso cardiovascular magnetic resonance imaging (Fetal XCMR), builds on established methods for simulating CMR acquisitions but is tailored toward the dynamic physiology of the fetal heart and body. We present comparisons between the Fetal XCMR phantom and data acquired in utero, resulting in image quality, anatomy, tissue signals and contrast. METHODS: Existing extended Cardiac-Torso models are modified to create maternal and fetal anatomy, combined according to simulated motion, mapped to CMR contrast, and converted to CMR data. To provide a comparison between the proposed simulation and experimental fetal CMR images acquired in utero, images from a typical scan of a pregnant woman are included and simulated acquisitions were generated using matching CMR parameters, motion and noise levels. Three reconstruction (static, real-time, and CINE), and two motion estimation methods (translational motion, fetal heart rate) from data acquired in transverse, sagittal, coronal, and short-axis planes of the fetal heart were performed to compare to in utero acquisitions and demonstrate feasibility of the proposed simulation framework. RESULTS: Overall, CMR contrast, morphologies, and relative proportions of the maternal and fetal anatomy are well represented by the Fetal XCMR images when comparing the simulation to static images acquired in utero. Additionally, visualization of maternal respiratory and fetal cardiac motion is comparable between Fetal XCMR and in utero real-time images. Finally, high quality CINE image reconstructions provide excellent delineation of fetal cardiac anatomy and temporal dynamics for both data types. CONCLUSION: The fetal CMR phantom provides a new method for evaluating fetal CMR acquisition and reconstruction methods by simulating the underlying anatomy and physiology. As the field of fetal CMR continues to grow, new methods will become available and require careful validation. The fetal CMR phantom is therefore a powerful and convenient tool in the continued development of fetal cardiac imaging.

Estimation of threshold thickness of residual normal tissue in lung dysfunction detectable by dynamic chest radiography: A virtual imaging trial.

BACKGROUND: Dynamic chest radiography (DCR) is a recently developed functional x-ray imaging technique that detects pulmonary ventilation impairment as a decrease in changes in lung density during respiration. However, the diagnostic performance of DCR is uncertain owing to an insufficient number of clinical cases. One solution is virtual imaging trials (VITs), which is an emerging alternative method for efficiently evaluating medical imaging technology via computer simulation techniques. PURPOSE: This study aimed to estimate the typical threshold thickness of residual normal tissue below which the presence of emphysema may be detected by DCR via VITs using virtual patients with different physiques and a user-defined ground truth. METHODS: Twenty extended cardiac-torso (XCAT) phantoms that exhibited changes in lung density during respiration were generated to simulate virtual patients. To simulate a locally collapsed lung, an air sphere was inserted into each lung regions in the phantom. The XCAT phantom was virtually projected using an x-ray simulator. The respiratory changes in pixel value (ΔPV) were measured on the projected air spheres (simulated lesions) to calculate the percentage of decrease (ΔPV%) relative to ΔPVexp-ins in the absence of an air sphere. The relationship between the amount of residual normal tissue and ΔPV% was fitted to a cubic approximation curve (hereafter, performance curve), and the threshold at which the ΔPV% began to decrease (normal-tissuethre) was determined. The goodness of fit for each performance curve was evaluated according to the coefficient of determination (R2) and the 95% confidence interval derived from the standard errors between the measured and theoretical values corresponding to each performance curve. The ΔPV% was also visualized as a color scaling to validate the results of the VITs in both virtual and clinical patients. RESULTS: For each lung region in all body sizes, the ΔPV% decreased as the amount of residual normal tissue decreased and could be defined as a function of the amount of residual normal tissue in front of and behind the simulated lesions with high R2 values. Meanwhile, the difference between the measured and theoretical values corresponding to each performance curve was only partially included in the 95% confidence interval. The normal-tissuethre values were 146.0, 179.5, and 170.9 mm for the upper, middle, and lower lungs, respectively, which were demonstrated in virtual patients and one real patient, where the value of the residual normal tissue was less than that of normal-tissuethre; any reduction in the residual normal tissue was reflected as a reduced ΔPV and depicted as a reduced color intensity. CONCLUSIONS: The performance of DCR-based pulmonary impairment assessment depends on the amount of residual normal tissue in front of and behind the lesion rather than on the lesion size. The performance curve can be defined as a function of the amount of residual normal tissue in each lung region with a specific threshold of normal tissue remaining where lesions become detectable, shown as a decrease in ΔPV. The results of VITs are expected to accelerate future clinical trials for DCR-based pulmonary function assessment.

CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background. The first proposed solution here is to manually segment the regions, which is a time-consuming and error-prone procedure. In this context, many semi- or fully automatic solutions have been proposed recently, among which deep learning-based methods have revealed high performance in segmenting regions in CMRI data. In this study, a self-adaptive multi attention (SMA) module is introduced to adaptively leverage multiple attention mechanisms for better segmentation. The convolutional-based position and channel attention mechanisms with a patch tokenization-based vision transformer (ViT)-based attention mechanism in a hybrid and end-to-end manner are integrated into the SMA. The CNN- and ViT-based attentions mine the short- and long-range dependencies for more precise segmentation. The SMA module is applied in an encoder-decoder structure with a ResNet50 backbone named CardSegNet. Furthermore, a deep supervision method with multi-loss functions is introduced to the CardSegNet optimizer to reduce overfitting and enhance the model's performance. The proposed model is validated on the ACDC2017 (n=100), M&Ms (n=321), and a local dataset (n=22) using the 10-fold cross-validation method with promising segmentation results, demonstrating its outperformance versus its counterparts.

Coronary stenosis quantification in cardiac computed tomography angiography: multi-factorial optimization of image quality and radiation dose.

Background: The accuracy and variability of quantification in computed tomography angiography (CTA) are affected by the interplay of imaging parameters and patient attributes. The assessment of these combined effects has been an open engineering challenge. Purpose: In this study, we developed a framework that optimizes imaging parameters for accurate and consistent coronary stenosis quantification in cardiac CTA while accounting for patient-specific variables. Methods: The framework utilizes a task-specific image quality index, the estimability index (e'), approximated by a surrogate estimability polynomial function (EPF) capable of finding the optimal protocol that (1) maximizes image quality with an upper bound for desired radiation dose or (2) minimizes the dose level with a lower bound of acceptable image quality. The optimization process was formulated with the decision variables being subject to a set of constraints. The methodology was verified using CTA data from a prior clinical trial (prospective multi-center imaging study for evaluation of chest pain) by assessing the concordance of its prediction with the trial results. Further, the framework was used to derive an optimum protocol for each case based on the patient attributes, gauging how much improvement would have been possible if the derived optimized protocol would have been deployed. Results: The framework produced results consistent with imaging physics principles with approximated EPFs of 97% accuracy. The feature importance evaluation demonstrated a close match with earlier studies. The verification study found e' scores closely predicting the cardiologist scores to within 95% in terms of the area under the receiver operating characteristic curve and predicting potential for either an average of fourfold increase in e' within a targeted dose or a reduction in radiation dose by an average of 57% without reducing the image quality. Conclusions: The protocol optimization framework provides means to assess and optimize CTA in terms of either image quality or radiation dose objectives with its results predicting prior clinical trial findings.

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.

Quantitative analysis of changes in lung density by dynamic chest radiography in association with CT values: a virtual imaging study and initial clinical corroboration.

Dynamic chest radiography (DCR) identifies pulmonary impairments as decreased changes in radiographic lung density during respiration (Δpixel values), but not as scaled/standardized computed tomography (CT) values. Quantitative analysis correlated with CT values is beneficial for a better understanding of Δpixel values in DCR-based assessment of pulmonary function. The present study aimed to correlate Δpixel values from DCR with changes in CT values during respiration (ΔCT values) through a computer-based phantom study. A total of 20 four-dimensional computational phantoms during forced breathing were created to simulate both CT and projection images of the same virtual patients. The Δpixel and ΔCT values of the lung fields were correlated on a regression line, and the inclination was statistically evaluated to determine whether there were significant differences among physical types, sex, and breathing methods. The resulting conversion expression was also assessed in the DCR images of 37 patients. The resulting Δpixel values for 30/37 (81%) real patients, 6/7 (86%) normal controls, and 24/30 (80%) chronic obstructive pulmonary disorder patients were within the range of ΔCT values ± standard deviation (SD) reported in a previous study. In addition, no significant differences were detected for each condition of thoracic breathing, suggesting that the same regression line inclination values measured across the entire lung can be used for the conversion of Δpixel values, providing a quantitative analysis that can be correlated with ΔCT values. The developed conversion expression may be helpful for improving the understanding of respiratory changes using radiographic lung densities from DCR-based assessments of pulmonary function.

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.

A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated.Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along thex/y/zdirection in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm.Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies.

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).

Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were [Formula: see text] and [Formula: see text], compared with that of real CT ([Formula: see text]) and CBCT ([Formula: see text]). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials.

Impact of Using Uniform Attenuation Coefficients for Heterogeneously Dense Breasts in a Dedicated Breast PET/X-ray Scanner.

We investigated PET image quantification when using a uniform attenuation coefficient (µ) for attenuation correction (AC) of anthropomorphic density phantoms derived from high-resolution breast CT scans. A breast PET system was modeled with perfect data corrections except for AC. Using uniform µ for AC resulted in quantitative errors roughly proportional to the difference between µ used in AC (µ AC) and local µ, yielding approximately ± 5% bias, corresponding to the variation of µ for 511 keV photons in breast tissue. Global bias was lowest when uniform µ AC was equal to the phantom mean µ (µ mean). Local bias in 10-mm spheres increased as the sphere µ deviated from µ mean, but remained only 2-3% when the µ sphere was 6.5% higher than µ mean. Bias varied linearly with and was roughly proportional to local µ mismatch. Minimizing local bias, e.g., in a small sphere, required the use of a uniform µ value between the local µ and the µ mean. Thus, biases from using uniform-µ AC are low when local µ sphere is close to µ mean. As the µ sphere increasingly differs from the phantom µ mean, bias increases, and the optimal uniform µ is less predictable, having a value between µ sphere and the phantom µ mean.

Development of a scanner-specific simulation framework for photon-counting computed tomography.

The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.

Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model.

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Automated, patient-specific estimation of regional imparted energy and dose from tube current modulated computed tomography exams across 13 protocols.

Currently, computed tomography (CT) dosimetry relies on surrogates for dose, such as CT dose index and size-specific dose estimates, rather than dose per se. Organ dose is considered as the gold standard for radiation dosimetry. However, organ dose estimation requires precise knowledge of organ locations. Regional imparted energy and dose can also be used to quantify radiation burden and are beneficial because they do not require knowledge of organ size or location. This work investigated an automated technique to retrospectively estimate the imparted energy from tube current-modulated (TCM) CT exams across 13 protocols. Monte Carlo simulations of various head and body TCM CT examinations across various tube potentials and TCM strengths were performed on 58 adult computational extended cardiac-torso phantoms to develop relationships between scanned mass and imparted energy normalized by dose length product. Results from the Monte Carlo simulations indicate that normalized imparted energy increases with increasing both scanned mass and tube potential, but it is relatively unaffected by the strength of the TCM. The automated algorithm was tested on 40 clinical datasets with a 98% success rate.

Synthetic breast phantoms from patient based eigenbreasts.

PURPOSE: The limited number of 3D patient-based breast phantoms available could be augmented by synthetic breast phantoms in order to facilitate virtual clinical trials (VCTs) using model observers for breast imaging optimization and evaluation. METHODS: These synthetic breast phantoms were developed using Principal Component Analysis (PCA) to reduce the number of dimensions needed to describe a training set of images. PCA decomposed a training set of M breast CT volumes (with millions of voxels each) into an M-1-dimensional space of eigenvectors, which we call eigenbreasts. Each of the training breast phantoms was compactly represented by the mean image plus a weighted sum of eigenbreasts. The distribution of weights observed from training was then sampled to create new synthesized breast phantoms. RESULTS: The resulting synthesized breast phantoms demonstrated a high degree of realism, as supported by an observer study. Two out of three experienced physicist observers were unable to distinguish between the synthesized breast phantoms and the patient-based phantoms. The fibroglandular density and noise power law exponent of the synthesized breast phantoms agreed well with the training data. CONCLUSIONS: Our method extends our series of digital breast phantoms based on breast CT data, providing the capability to generate new, statistically varying ensembles consisting of tens of thousands of virtual subjects. This work represents an important step toward conducting future virtual trials for task-based assessment of breast imaging, where it is vital to have a large ensemble of realistic phantoms for statistical power as well as clinical relevance.

Breast dose reduction with organ-based, wide-angle tube current modulated CT.

This study aimed to estimate the organ dose reduction potential for organ-dose-based tube current modulated (ODM) thoracic computed tomography (CT) with a wide dose reduction arc. Twenty-one computational anthropomorphic phantoms (XCAT) were used to create a virtual patient population with clinical anatomic variations. The phantoms were created based on patient images with normal anatomy (age range: 27 to 66 years, weight range: 52.0 to 105.8 kg). For each phantom, two breast tissue compositions were simulated: [Formula: see text] and [Formula: see text] (glandular-to-adipose ratio). A validated Monte Carlo program (PENELOPE, Universitat de Barcelona, Spain) was used to estimate the organ dose for standard tube current modulation (TCM) (SmartmA, GE Healthcare) and ODM (GE Healthcare) for a commercial CT scanner (Revolution, GE Healthcare) using a typical clinical thoracic CT protocol. Both organ dose and [Formula: see text]-to-organ dose conversion coefficients ([Formula: see text] factors) were compared between TCM and ODM. ODM significantly reduced all radiosensitive organ doses ([Formula: see text]). The breast dose was reduced by [Formula: see text]. For [Formula: see text] factors, organs in the anterior region (e.g., thyroid and stomach) exhibited substantial decreases, and the medial, distributed, and posterior region saw either an increase of less than 5% or no significant change. ODM significantly reduced organ doses especially for radiosensitive superficial anterior organs such as the breasts.

CT breast dose reduction with the use of breast positioning and organ-based tube current modulation.

PURPOSE: This study aimed to investigate the breast dose reduction potential of a breast-positioning (BP) technique for thoracic CT examinations with organ-based tube current modulation (OTCM). METHODS: This study included 13 female anthropomorphic computational phantoms (XCAT, age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modified to simulate three breast sizes in standard supine geometry. The modeled breasts were then morphed to emulate BP that constrained the majority of the breast tissue inside the 120° anterior tube current (mA) reduction zone. The OTCM mA value was modeled using a ray-tracing program, which reduced the mA to 20% in the anterior region with a corresponding increase to the posterior region. The organ doses were estimated by a validated Monte Carlo program for a typical clinical CT system (SOMATOM Definition Flash, Siemens Healthcare). The simulated organ doses and organ doses normalized by CTDIvol were used to compare three CT protocols: attenuation-based tube current modulation (ATCM), OTCM, and OTCM with BP (OTCMBP ). RESULTS: On average, compared to ATCM, OTCM reduced breast dose by 19.3 ± 4.5%, whereas OTCMBP reduced breast dose by 38.6 ± 8.1% (an additional 23.8 ± 9.4%). The dose saving of OTCMBP was more significant for larger breasts (on average 33, 38, and 44% reduction for 0.5, 1, and 2 kg breasts, respectively). Compared to ATCM, OTCMBP also reduced thymus and heart dose by 15.1 ± 7.4% and 15.9 ± 6.2% respectively. CONCLUSIONS: In thoracic CT examinations, OTCM with a breast-positioning technique can markedly reduce unnecessary exposure to radiosensitive organs in anterior chest wall, specifically breast tissue. The breast dose reduction is more notable for women with larger breasts.

Assessment of individual organ doses in a realistic human phantom from neutron and gamma stimulated spectroscopy of the breast and liver.

PURPOSE: Understanding the radiation dose to a patient is essential when considering the use of an ionizing diagnostic imaging test for clinical diagnosis and screening. Using Monte Carlo simulations, the authors estimated the three-dimensional organ-dose distribution from neutron and gamma irradiation of the male liver, female liver, and female breasts for neutron- and gamma-stimulated spectroscopic imaging. METHODS: Monte Carlo simulations were developed using the Geant4 GATE application and a voxelized XCAT human phantom. A male and a female whole body XCAT phantom was voxelized into 256 × 256 × 600 voxels (3.125 × 3.125 × 3.125 mm(3)). A monoenergetic rectangular beam of 5.0 MeV neutrons or 7.0 MeV photons was made incident on a 2 cm thick slice of the phantom. The beam was rotated at eight different angles around the phantom ranging from 0° to 180°. Absorbed dose was calculated for each individual organ in the body and dose volume histograms were computed to analyze the absolute and relative doses in each organ. RESULTS: The neutron irradiations of the liver showed the highest organ dose absorption in the liver, with appreciably lower doses in other proximal organs. The dose distribution within the irradiated slice exhibited substantial attenuation with increasing depth along the beam path, attenuating to ~15% of the maximum value at the beam exit side. The gamma irradiation of the liver imparted the highest organ dose to the stomach wall. The dose distribution from the gammas showed a region of dose buildup at the beam entrance, followed by a relatively uniform dose distribution to all of the deep tissue structures, attenuating to ~75% of the maximum value at the beam exit side. For the breast scans, both the neutron and gamma irradiation registered maximum organ doses in the breasts, with all other organs receiving less than 1% of the breast dose. Effective doses ranged from 0.22 to 0.37 mSv for the neutron scans and 41 to 66 mSv for the gamma scans. CONCLUSIONS: Neutron and gamma irradiation of a primary target organ was found to impart the majority of the total dose to the primary target organ (and other large organs) within the beam plane and considerably lower dose to proximal organs outside of the beam. These results also indicate that despite the use of a highly scattering particle such as a neutron, the dose from neutron stimulated emission computed tomography scans is on par with other clinical imaging techniques such as x-ray computed tomography (x-ray CT). Given the high nonuniformity in the dose across an organ during the neutron scan, care must be taken when computing average doses from neutron irradiations. The effective doses from neutron scanning were found to be comparable to x-ray CT. Further technique modifications are needed to reduce the effective dose levels from the gamma scans.