Physical Anthropomorphic Phantoms Research Articles

Analysing Student Feedback on the Integration of 3D Printing in the Teaching of Mammography for Radiologic Technologists

The objective of this study is to assess the suitability of using printed breast models as educational tools for instructing radiographer students on positioning and compression techniques in X-ray mammography. A physical anthropomorphic breast phantom, created with a stereolithography 3D printer based on a computational breast model, served as the main tool in the teaching approach. The practical exercise, conducted in three sessions for first, second, and third-year radiographer students, involved a structured approach in front of the mammography device. A questionnaire assessed participants' opinions on the 3D breast phantom and the exercise's impact on positioning, compression, and mammography device teachings. Among the 83 students surveyed, 52 provided responses, demonstrating a success rate of 71.2% in mastering the exercise utilizing the physical breast phantom. A significant portion of students express the importance of integrating contemporary digital technologies into their academic curriculum, based on the practical experience. The instructor demonstrated thorough preparation and competence in guiding the practical session. The positive experience in this teaching approach strongly encourages future professionals to integrate these new technologies into their daily practice, especially for acquiring skills in compression techniques and breast positioning.

Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.

Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system. Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline. We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom. The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.

TEMPy: a toolkit for the modeling of weighted tissue equivalent material in diagnostic imaging

Objective. Accurate simulation of human tissues is imperative for advancements in diagnostic imaging, particularly in the fields of dosimetry and image quality evaluation. Developing Tissue Equivalent Materials (TEMs) with radiological characteristics akin to those of human tissues is essential for ensuring the reliability and relevance of imaging studies. This study presents the development of a mathematical model and a new toolkit (TEMPy) for obtaining the best composition of materials that mimic the radiological characteristics of human tissues. The model and the toolkit are described, along with an example showcasing its application to obtain desired TEMs. Approach. The methodology consisted of fitting volume fractions of the components of TEM in order to determine its linear attenuation coefficient as close as possible to the linear attenuation coefficient of the reference material. The fitting procedure adopted a modified Least Square Method including a weight function. This function reflects the contribution of the x-ray spectra in the suitable energy range of interest. TEMPy can also be used to estimate the effective atomic number and electron density of the resulting TEM. Main results. TEMPy was used to obtain the chemical composition of materials equivalent to water and soft tissue, in the energy range used in x-ray imaging (10 −150 keV) and for breast tissue using the energy range (5–40 keV). The maximum relative difference between the linear attenuation coefficients of the developed and reference materials was ±5% in the considered energy ranges. Significance. TEMPy facilitates the formulation of TEMs with radiological properties closely mimicking those of real tissues, aiding in the preparation of physical anthropomorphic or geometric phantoms for various applications. The toolkit is freely available to interested readers.

Digital phantom versus patient-specific radiation dosimetry in adult routine thorax CT examinations.

The aim of this study was to compare the organ doses assessed through a digital phantom-based and a patient specific-based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms. Two Monte Carlo based dose calculation tools were used to assess organ doses in routine adult thorax CT examinations. These were a digital phantom-based dosimetry tool (NCICT, National Cancer Institute, USA) and a patient-specific individualized dosimetry tool (ImpactMC, CT Imaging GmbH, Germany). Digital phantoms and patients were classified in four groups according to their water equivalent diameter (Dw). Normalized to volume computed tomography dose index (CTDIvol), organ dose was assessed for lungs, esophagus, heart, breast, active bone marrow, and skin. Organ doses were compared to measurements performed using thermoluminescent detectors (TLDs) in two physical anthropomorphic phantoms that simulate the average adult individual as a male (Alderson Research Labs, USA) and as a female (ATOM Phantoms, USA). The average percent difference of NCICT to TLD and ImpactMC to TLD dose measurements across all organs in both sexes was 13% and 6%, respectively. The average±1 standard deviation in dose values across all organs with NCICT, ImpactMC, and TLDs was±0.06 (mGy/mGy),±0.19 (mGy/mGy), and±0.13 (mGy/mGy), respectively. Organ doses decreased with increasing Dw in both NCICT and ImpactMC. Organ doses estimated with ImpactMC were in closer agreement to TLDs compared to NCICT. This may be attributed to the inherent property of ImpactMC methodology to generate phantoms that resemble the realistic anatomy of the examined patient as opposed to NCICT methodology that incorporates an anatomical discrepancy between phantoms and patients.

Development and validation of the low-cost pregnant female physical phantom for fetal dosimetry in MV photon radiotherapy.

Monte Carlo (MC) simulations or measurements in anthropomorphic phantoms are recommended for estimating fetal dose in pregnant patients in radiotherapy. Among the many existing phantoms, there is no commercially available physical phantom representing the entire pregnant woman. In this study, the development of a low-cost, physical pregnant female phantom was demonstrated using commercially available materials. This phantom is based on the previously published computational phantom. Three tissue substitution materials (soft tissue, lung and bone tissue substitution) were developed. To verify Tena's substitution tissue materials, their radiation properties were assessed and compared to ICRP and ICRU materials using MC simulations in MV radiotherapy beams. Validation of the physical phantom was performed by comparing fetal doses obtained by measurements in the phantom with fetal doses obtained by MC simulations in computational phantom, during an MV photon breast radiotherapy treatment. Materials used for building Tena phantom are matched to ICRU materials using physical density, radiation absorption properties and effective atomic number. MC simulations showed that percentage depth doses of Tena and ICRU material comply within 5% for soft and lung tissue, up to 25cm depth. In the bone tissue, the discrepancy is higher, but again within 5% up to the depth of 5cm. When the phantom was used for fetal dose measurements in MV photon breast radiotherapy, measured fetal doses complied with fetal doses calculated using MC simulation within 15%. Physical anthropomorphic phantom of pregnant patient can be manufactured using commercial materials and with low expenses. The files needed for 3D printing are now freely available. This enables further studies and comparison of numerical and physical experiments in diagnostic radiology or radiotherapy.

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss using in silico and physical phantom studies.

Recent research suggests that image quality degradation with reduced radiation exposure in mammography can be mitigated by postprocessing mammograms with denoising algorithms based on convolutional neural networks. Breast microcalcifications, along with extended soft-tissue lesions, are the primary breast cancer biomarkers in a clinical x-ray examination, with the former being more sensitive to quantum noise. We test one such publicly available denoising method to observe if an improvement in detection of small microcalcifications can be achieved when deep learning-based denoising is applied to half-dose phantom scans. An existing denoiser model (that was previously trained on clinical data) was applied to mammograms of an anthropomorphic physical phantom with hydroxyapatite microcalcifications. In addition, another model trained and tested using all synthetic (Monte Carlo) data was applied to a similar digital compressed breast phantom. Human reader studies were conducted to assess and compare image quality in a set of binary signal detection 4-AFC experiments, with proportion of correct responses used as a performance metric. In both physical phantom/clinical system and simulation studies, we saw no apparent improvement in small microcalcification signal detection in denoised half-dose mammograms. However, in a Monte Carlo study, we observed a noticeable jump in 4-AFC scores, when readers analyzed denoised half-dose images processed by the neural network trained on a dataset composed of 50% signal-present (SP) and 50% signal-absent regions of interest (ROIs). Our findings conjecture that deep-learning denoising algorithms may benefit from enriching training datasets with SP ROIs, at least in cases with clusters of 5 to 10 microcalcifications, each of size .

The effect of added filtration on radiation dose and image quality in digital radiography of newborns.

Repeated chest and/or abdomen radiographs are needed on mobile digital radiography (DR) units to assess infants' health status. Optimization of DR tube peak kilovoltage (kVp) and tube current time product (mAs) to derive images of diagnostic quality at as low as reasonably achievable radiation dose has been a challenging task. To investigate the effect of exposure parameters and additional filtration on entrance skin dose and image quality in newborn DR imaging. A physical anthropomorphic phantom that simulates the average full-term neonate was used. Chest and chest/abdomen DR images were acquired at the manufacturer's recommended kVp/mAs exposure parameters followed by a series of image acquisitions at different kVp/mAs and beam filtration combinations. Entrance skin dose (ESD) and signal difference to noise ratio (SdNR) for soft tissue, bone and feeding gastric tube were estimated in raw unprocessed images. A figure of merit (FOM) analysis provided the kVp/mAs and filtration that generated images of adequate quality at the lowest ESD. Signal difference increased with kVp and progressively decreased with increasing filtration. Compared to the manufacturer's recommended 53 kVp/1.6 mAs exposure parameters, ESD was reduced by 76% (47.61μGy versus 11.3μGy) in the chest and 66% (47.61μGy versus 16.14μGy) in the chest/abdomen when the exposure parameters and additional beam filtration provided by the FOM analysis were used. The results derived from this phantom study suggest that additional beam filtration along with the appropriate leverage of exposure parameters can lower ESD while maintaining image quality in full-term newborns.

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

Digital mammography is currently the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks associated with the x-ray exposure, the radiation dose must be kept as low as possible while maintaining the diagnostic utility of the generated images, thus minimizing patient risks. Many studies investigated the feasibility of dose reduction by restoring low-dose images using deep neural networks. In these cases, choosing the appropriate training database and loss function is crucial and impacts the quality of the results. In this work, we used a standard residual network (ResNet) to restore low-dose digital mammography images and evaluated the performance of several loss functions. For training purposes, we extracted 256,000 image patches from a dataset of 400 images of retrospective clinical mammography exams, where dose reduction factors of 75% and 50% were simulated to generate low and standard-dose pairs. We validated the network in a real scenario by using a physical anthropomorphic breast phantom to acquire real low-dose and standard full-dose images in a commercially available mammography system, which were then processed through our trained model. We benchmarked our results against an analytical restoration model for low-dose digital mammography. Objective assessment was performed through the signal-to-noise ratio (SNR) and the mean normalized squared error (MNSE), decomposed into residual noise and bias. Statistical tests revealed that the use of the perceptual loss (PL4) resulted in statistically significant differences when compared to all other loss functions. Additionally, images restored using the PL4 achieved the closest residual noise to the standard dose. On the other hand, perceptual loss PL3, structural similarity index (SSIM) and one of the adversarial losses achieved the lowest bias for both dose reduction factors. The source code of our deep neural network is available at https://github.com/WANG-AXIS/LdDMDenoising.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

Organ doses and normalized organ doses for various age groups in ultralow dose pediatric C-arm cone-beam CT.

To estimate organ dose to major radiosensitive organs during pediatric body C-arm CBCT and determine normalized organ doses using a state-of-the-art equipment. This is a study performed utilizing physical anthropomorphic phantoms. Four anthropomorphic phantoms that simulate the average individual as a neonate, 1-year-old, 5-year-old, and 10-year-old child were used. Personalized Monte Carlo (MC)-based dosimetry was performed to estimate organ doses in children referred to thorax and abdomen C-arm CBCT acquisitions on a recently released latest generation C-arm CBCT system. Age-specific normalized organ doses were generated and organ dose was estimated for skin, bone, breast, lungs, esophagus, thymus, and heart, in the thorax, and liver, adrenals, kidneys, pancreas, stomach, gall bladder, and spleen in the abdomen. Estimated doses were compared to corresponding values obtained with physical measurements performed using thermoluminescent dosimeters (TLD). The results consist of organ doses for thorax and abdomen acquisition protocols. The majority of organs received a dose below 1 mSv. For all ages, the normalized organ doses decreased from neonate to 10-year-old. The difference between the organ doses obtained with MC and TLDs was less than 8%. Normalized organ doses in pediatric C-arm CBCT varied with age. Pediatric C-arm CBCT with latest-generation systems may be performed with sub mGy dose for most organs. • The dose to the majority of organs from pediatric C-arm CBCT is in the sub mSv level. • The normalized organ doses decreased from neonate to 10-year-old. • Reported normalized organ doses may be used to estimate organ dose in pediatric C-arm cone-beam CT on modern equipment.

Technical note: Controlling the attenuation of 3D-printed physical phantoms for computed tomography with a single material.

The purpose of this work was to characterize and improve the ability of fused filament fabrication to create anthropomorphic physical phantoms for CT research. Specifically, we sought to develop the ability to create multiple levels of X-ray attenuation with a single material. CT images of 3D printed cylinders with different infill angles and printing patterns were assessed by comparing their 2D noise power spectra to determine the conditions that produced minimal and uniform noise. A backfilling approach in which additional polymer was extruded into an existing 3D printed background layer was developed to create multiple levels of image contrast. A print with nine infill angles and a rectilinear infill pattern was found to have the best uniformity, but the printed objects were not as uniform as a commercial phantom. An HU dynamic range of 600 was achieved by changing the infill percentage from 40% to 100%. The backfilling technique enabled control of up to eight levels of contrast within one object across a range of 200 HU, similar to the range of soft tissue. A contrast detail phantom with six levels of contrast and an anthropomorphic liver phantom with four levels of contrast were printed with a single material. This work improves the uniformity and levels of contrast that can be achieved with fused filament fabrication, thereby enabling researchers to easily create more detailed physical phantoms, including realistic, anthropomorphic textures.

Thermoplastic 3D printing technology using a single filament for producing realistic patient-derived breast models

Objective. This work describes an approach for producing physical anthropomorphic breast phantoms from clinical patient data using three-dimensional (3D) fused-deposition modelling (FDM) printing. Approach. The source of the anthropomorphic model was a clinical Magnetic Resonance Imaging (MRI) patient image set, which was segmented slice by slice into adipose and glandular tissues, skin and tumour formations; thus obtaining a four component computational breast model. The segmented tissues were mapped to specific Hounsfield Units (HU) values, which were derived from clinical breast Computed Tomography (CT) data. The obtained computational model was used as a template for producing a physical anthropomorphic breast phantom using 3D printing. FDM technology with only one polylactic acid filament was used. The physical breast phantom was scanned at Siemens SOMATOM Definition CT. Quantitative and qualitative evaluation were carried out to assess the clinical realism of CT slices of the physical breast phantom. Main results. The comparison between selected slices from the computational breast phantom and CT slices of the physical breast phantom shows similar visual x-ray appearance of the four breast tissue structures: adipose, glandular, tumour and skin. The results from the task-based evaluation, which involved three radiologists, showed a high degree of realistic clinical radiological appearance of the modelled breast components. Measured HU values of the printed structures are within the range of HU values used in the computational phantom. Moreover, measured physical parameters of the breast phantom, such as weight and linear dimensions, agreed very well with the corresponding ones of the computational breast model. Significance. The presented approach, based on a single FDM material, was found suitable for manufacturing of a physical breast phantom, which mimics well the 3D spatial distribution of the different breast tissues and their x-ray absorption properties. As such, it could be successfully exploited in advanced x-ray breast imaging research applications.

Validation of a deterministic linear Boltzmann transport equation solver for rapid CT dose computation using physical dose measurements in pediatric phantoms.

The risk of inducing cancer to patients undergoing CT examinations has motivated efforts for CT dose estimation, monitoring, and reduction, especially among pediatric population. The method investigated in this study is Acuros CTD (Varian Medical Systems, Palo Alto, CA), a deterministic linear Boltzmann transport equation (LBTE) solver aimed at generating rapid and reliable dose maps of CT exams. By applying organ contours, organ doses can also be obtained, thus patient-specific organ dose estimates can be provided. This study experimentally validated Acuros against measurements performed on a clinical CT system using a range of physical pediatric anthropomorphic phantoms and acquisition protocols. The study consisted of (1) the acquisition of dose measurements on a clinical CT scanner through thermoluminescent dosimeters (TLDs), and (2) the modeling in the Acuros platform of the measurement set up, which includes the modeling of the CT scanner and of the anthropomorphic phantoms. For the measurements, 1-year-old, 5-year-old, and 10-year-old anthropomorphic phantoms of the CIRS ATOM family were used. TLDs were placed in selected organ locations such as stomach, liver, lungs, and heart. The pediatric phantoms were scanned helically with the GE Discovery 750 HD clinical scanner for several examination protocols. For the simulations in Acuros, scanner-specific input, such as bowtie filters, overrange collimation, and tube current modulation schemes, were modeled. These scanner complexities were implemented by defining discretized X-ray beams whose spectral distribution, defined in Acuros by only six energy bins, varied across fan angle, cone angle, and slice position. The images generated during the CT acquisitions were used to create the geometrical models, by applying thresholding algorithms and assigning materials to the HU values. The TLDs were contoured in the phantom models as sensitive cylindrical volumes at the locations selected for dosimeters placement, to provide dose estimates, in terms of dose per unit photon. To compare measured doses with dose estimates, a calibration factor was derived from the CTDIvol displayed by the scanner, to account for the number of photons emitted by the X-ray tube during the procedure. The differences of the measured and estimated doses, in terms of absolute % errors, were within 13% for 153 TLD locations, with an error of 17% at the stomach for one study with the 10-year-old phantom. Root-mean-squared-errors (RMSE) across all TLD locations for all configurations were in the range of 3%-8%, with Acuros providing dose estimates in a time range of a few seconds up to 2 min. An overall good agreement between measurements and simulations was achieved, with average RMSE of 6% across all cases. The results demonstrate that Acuros can model a specific clinical scanner despite the required discretization in spatial and energy domains. The proposed deterministic tool has the potential to be part of a near real-time individualized dosimetry monitoring system for CT applications, providing patient-specific organ dose estimates.

Assessment of the uterine dose in digital mammography and digital breast tomosynthesis

IntroductionDigital Mammography (DM-2D) and more recently Digital Breast Tomosynthesis (DBT), are two of the most effective imaging modalities for breast cancer detection, often used in screening programmes. It may happen that exams using these two imaging modalities are inadvertently performed to pregnant women. The objective of this study is to assess the dose in the uterus due to DM-2D and DBT exams, according to two main irradiation scenarios: in the 1st scenario the exposure parameters were pre-selected directly by the imaging system, while in the 2nd scenario, the maximum exposure parameters were chosen. MethodsThe mammography equipment used was a Siemens Mammomat Inspiration. A physical anthropomorphic phantom, PMMA plates (simulating a breast thickness of 6 cm) and thermoluminescent dosimeters (TLDs) were used to measure entrance air kerma values on the phantom's breast and abdomen in order to successively estimate the mean glandular dose (MGD) and the dose in the uterus. For the two irradiation scenarios chosen, two-breast imaging modalities were selected: 1) DBT in Cranio-Caudal (CC) view (with 28 kV and 160 mAs as exposure parameters), 2) DBT and DM in Medio Lateral-Oblique (MLO) and CC views (with 34 kV and 250 mAs as exposure parameters). ResultsIn the 1st scenario, the TLD measurements did not detect significant dose values in the abdomen whereas the MGD estimated using the D.R. Dance model was in close agreement with data available in the literature. In the 2nd scenario, there was no significant difference in MGD estimation between the different views, whereas the air kerma values in the abdomen (in DBT mode, CC and MLO) were 0.049 mGy and 0.004 mGy respectively. In CC DM-2D mode the abdomen air kerma value was 0.026 mGy, with no significant detected value in MLO view. ConclusionsFor the dose in the uterus, the obtained values seem to indicate that DM-2D and DBT examinations inadvertently performed during pregnancy do not pose a significant radiological risk, even considering the case of overexposure in both breasts. Implications for practiceThe accurate knowledge of the doses in DM-2D and DBT will contribute to raise the awareness among medical practitioners involved in breast imaging empowering them to provide accurate information about dose levels in the uterus, improving their radiation risk communication skills and consequently helping to reduce the anxiety of pregnant women undergoing this type of examinations.

Use of voxelized thyroid models to develop a physical-anthropomorphic phantom for 3D printing

The monitoring of the intake os radionuclides by workers and public individuals, using in vivo measurements, requires the application of calibration factors obtained with physical-anthropomorphic phantoms. In the case of 131I, the lack of anatomical realism of the phantom might impair the quality and reliability of the monitoring result. Thus, in order to develop a thyroid phantom by means of 3D printing, images of the voxelized models provided in ICRP 110 were used. The superposition of the images of the original model and the treated model demonstrate the maintenance of morphological characteristics. Therefore, image manipulation techniques applied in this work aimed to smooth the sharp angles of the original image and the prototype printing were considered effective.

Virtual calibration of whole-body counters to consider the size dependency of counting efficiency using Monte Carlo simulations

The counting efficiencies obtained using anthropomorphic physical phantoms are generally used in whole-body counting measurements to determine the level of internal contamination in the body. Geometrical discrepancies between phantoms and measured individuals affect the counting efficiency, and thus, considering individual physical characteristics is crucial to improve the accuracy of activity estimates. In the present study, the counting efficiencies of whole-body counting measurements were calculated considering individual physical characteristics by employing Monte Carlo simulation for calibration. The NaI(Tl)-based stand-up and HPGe-based bed type commercial whole-body counters were used for calculating the counting efficiencies. The counting efficiencies were obtained from 19 computational phantoms representing various shapes and sizes of the measured individuals. The discrepancies in the counting efficiencies obtained using the computational and physical phantoms range from 2% to 33%, and the results indicate that the counting efficiency depends on the size of the measured individual. Taking into account the body size, the equations for estimating the counting efficiencies were derived from the relationship between the counting efficiencies and the body-build index of the subject. These equations can aid in minimizing the size dependency of the counting efficiency and provide more accurate measurements of internal contamination in whole-body counting measurements.

Dataset of patient-derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography.

To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61mm. The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.

Fabrication of a pediatric torso phantom with multiple tissues represented using a dual nozzle thermoplastic 3D printer.

PurposeTo demonstrate an on‐demand and nearly automatic method for fabricating tissue‐equivalent physical anthropomorphic phantoms for imaging and dosimetry applications using a dual nozzle thermoplastic three‐dimensional (3D) printer and two types of plastic.MethodsTwo 3D printing plastics were investigated: (a) Normal polylactic acid (PLA) as a soft tissue simulant and (b) Iron PLA (PLA‐Fe), a composite of PLA and iron powder, as a bone simulant. The plastics and geometry of a 1‐yr‐old computational phantom were combined with a dual extrusion 3D printer to fabricate an anthropomorphic imaging phantom. The volumetric fill density of the 3D‐printed parts was varied to approximate tissues of different radiographic density using a calibration curve relating the printer infill density setting to measured CT number. As a demonstration of our method we printed a 10 cm axial cross‐section of the computational phantom’s torso at full scale. We imaged the phantom on a CT scanner and compared HU values to those of a 1‐yr‐old patient and a commercial 5‐yr‐old physical phantom.ResultsThe phantom was printed in six parts over the course of a week. The printed phantom included 30 separate anatomical regions including soft tissue remainder, lungs (left and right), heart, esophagus, rib cage (left and right ribs 1 to 10), clavicles (left and right), scapulae (left and right), thoracic vertebrae (one solid object defining thoracic vertebrae T1 to T9). CT scanning of the phantom showed five distinct radiographic regions (heart, lung, soft tissue remainder, bone, and air cavity) despite using only two types of plastic. The 3D‐printed phantom demonstrated excellent similarity to commercially available phantoms, although key limitations in the printer and printing materials leave opportunity for improvement.ConclusionPatient‐specific anthropomorphic phantoms can be 3D printed and assembled in sections for imaging and dosimetry applications. Such phantoms will be useful for dose verification purposes when commercial phantoms are unavailable for purchase in the specific anatomies of interest.

Calculation of Radiomic Features to Validate the Textural Realism of Physical Anthropomorphic Phantoms for Digital Mammography.

In this paper, radiomic features are used to validate the textural realism of two anthropomorphic phantoms for digital mammography. One phantom was based off a computational breast model; it was 3D printed by CIRS (Computerized Imaging Reference Systems, Inc., Norfolk, VA) under license from the University of Pennsylvania. We investigate how the textural realism of this phantom compares against a phantom derived from an actual patient's mammogram ("Rachel", Gammex 169, Madison, WI). Images of each phantom were acquired at three kV in 1 kV increments using auto-time technique settings. Acquisitions at each technique setting were repeated twice, resulting in six images per phantom. In the raw ("FOR PROCESSING") images, 341 features were calculated; i.e., gray-level histogram, co-occurrence, run length, fractal dimension, Gabor Wavelet, local binary pattern, Laws, and co-occurrence Laws features. Features were also calculated in a negative screening population. For each feature, the middle 95% of the clinical distribution was used to evaluate the textural realism of each phantom. A feature was considered realistic if all six measurements in the phantom were within the middle 95% of the clinical distribution. Otherwise, a feature was considered unrealistic. More features were actually found to be realistic by this definition in the CIRS phantom (305 out of 341 features or 89.44%) than in the phantom derived from a specific patient's mammogram (261 out of 341 features or 76.54%). We conclude that the texture is realistic overall in both phantoms.

Evaluation of an organ-based tube current modulation tool in pediatric CT examinations

ObjectivesTo investigate the effect of an organ-based tube current modulation (OTCM) technique on organ absorbed dose and assess image quality in pediatric CT examinations.MethodsFour physical anthropomorphic phantoms that represent the average individual as neonate, 1-year-old, 5-year-old, and 10-year-old were used. Standard head and thorax acquisitions were performed with automatic tube current modulation (ATCM) and ATCM+OTCM. Dose calculations were performed by means of Monte Carlo simulations. Radiation dose was measured for superficial and centrally located radiosensitive organs. The angular range of the OTCM exposure window was determined for different tube rotation times (t) by means of a solid-state detector. Image noise was measured as the standard deviation of the Hounsfield unit value in regions of interest drawn at selected anatomical sites.ResultsATCM+OTCM resulted in a reduction of radiation dose to all radiosensitive organs. In head, eye lens dose was reduced by up to 13% in ATCM+OTCM compared with ATCM. In thorax, the corresponding reduction for breast dose was up to 10%. The angular range of the OTCM exposure window decreased with t. For t = 0.4 s, the angular range was limited to 74° in head and 135° for thorax. Image noise was significantly increased in ATCM+OTCM acquisitions across most examined phantoms (p < 0.05).ConclusionsOTCM reduces radiation dose to exposed radiosensitive organs with the eye lens and breast buds exhibiting the highest dose reduction. The OTCM exposure window is narrowed at short t. An increase in noise is inevitable in images located within the OTCM-activated imaged volume.Key Points• In pediatric CT, organ-based tube current modulation reduces radiation dose to all major primarily exposed radiosensitive organs.• Image noise increases within the organ-based tube current modulation enabled imaged volume.• The angular range of the organ-based tube current modulation low exposure window is reduced with tube rotation time.