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Background: A coded aperture X-ray diffraction (XRD) imaging system can measure the X-ray diffraction form factor from an object in three dimensions -X, Y and Z (depth), broadening the potential application of this technology. However, to optimize XRD systems for specific applications, it is critical to understand how to predict and quantify system performance for each use case., Objective: The purpose of this work is to present and validate 3D spatial resolution models for XRD imaging systems with a detector-side coded aperture., Methods: A fan beam coded aperture XRD system was used to scan 3D printed resolution phantoms placed at various locations throughout the system's field of view. The multiplexed scatter data were reconstructed using a model-based iterative reconstruction algorithm, and the resulting volumetric images were evaluated using multiple resolution criteria to compare against the known phantom resolution. We considered the full width at half max and Sparrow criterion as measures of the resolution and compared our results against analytical resolution models from the literature as well as a new theory for predicting the system resolution based on geometric arguments., Results: We show that our experimental measurements are bounded by the multitude of theoretical resolution predictions, which accurately predict the observed trends and order of magnitude of the spatial and form factor resolutions. However, we find that the expected and observed resolution can vary by approximately a factor of two depending on the choice of metric and model considered. We observe depth resolutions of 7-16 mm and transverse resolutions of 0.6-2 mm for objects throughout the field of view. Furthermore, we observe tradeoffs between the spatial resolution and XRD form factor resolution as a function of sample location., Conclusion: The theories evaluated in this study provide a useful framework for estimating the 3D spatial resolution of a detector side coded aperture XRD imaging system. The assumptions and simplifications required by these theories can impact the overall accuracy of describing a particular system, but they also can add to the generalizability of their predictions. Furthermore, understanding the implications of the assumptions behind each theory can help predict performance, as shown by our data's placement between the conservative and idealized theories, and better guide future systems for optimized designs.

Climate change has led to an increase in heat-related morbidity and mortality. The impact of heat on health is unequally distributed amongst different socioeconomic and demographic groups. We use high-resolution daily air temperature-based heat wave intensity (HWI) and neighborhood-scale sociodemographic information from the conterminous United States to evaluate the spatial patterning of extreme heat exposure disparities. Assuming differences in spatial patterns at national, regional, and local scales; we assess disparities in heat exposure across race, housing characteristics, and poverty level. Our findings indicate small differences in HWI based on these factors at the national level, with the magnitude and direction of the differences varying by region. The starkest differences are present over the Northeast and Midwest, where primarily Black neighborhoods are exposed to higher HWI than predominantly White areas. At the local level, we find the largest difference by socioeconomic status. We also find that residents of nontraditional housing are more vulnerable to heat exposure. Previous studies have either evaluated such disparities for specific cities and/or used a satellite-based land surface temperature, which, although correlated with air temperature, does not provide the true measure of heat exposure. This study is the first of its kind to incorporate high-resolution gridded air temperature-based heat exposure in the evaluation of sociodemographic disparities at a national scale. The analysis suggests the unequal distribution of heat wave intensities across communities-with higher heat exposures characterizing areas with high proportions of minorities, low socioeconomic status, and homes in need of retrofitting to combat climate change., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 Oak Ridge National Laboratory and The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Purpose: Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on developing algorithms that maximize the performance toward decision-making and diagnostic tasks. In this study, we present the implementation of and comparison between rules-based and machine learning (ML) classifiers on XRD images of medically relevant phantoms to explore the potential for increased classification performance., Methods: Medically relevant phantoms were utilized to provide well-characterized ground-truths for comparing classifier performance. Water and polylactic acid (PLA) plastic were used as surrogates for cancerous and healthy tissue, respectively, and phantoms were created with varying levels of spatial complexity and biologically relevant features for quantitative testing of classifier performance. Our previously developed X-ray scanner was used to acquire co-registered X-ray transmission and diffraction images of the phantoms. For classification algorithms, we explored and compared two rules-based classifiers (cross-correlation, or matched-filter, and linear least-squares unmixing) and two ML classifiers (support vector machines and shallow neural networks). Reference XRD spectra (measured by a commercial diffractometer) were provided to the rules-based algorithms, while 60% of the measured XRD pixels were used for training of the ML algorithms. The area under the receiver operating characteristic curve (AUC) was used as a comparative metric between the classification algorithms, along with the accuracy performance at the midpoint threshold for each classifier., Results: The AUC values for material classification were 0.994 (cross-correlation [CC]), 0.994 (least-squares [LS]), 0.995 (support vector machine [SVM]), and 0.999 (shallow neural network [SNN]). Setting the classification threshold to the midpoint for each classifier resulted in accuracy values of CC = 96.48%, LS = 96.48%, SVM = 97.36%, and SNN = 98.94%. If only considering pixels ±3 mm from water-PLA boundaries (where partial volume effects could occur due to imaging resolution limits), the classification accuracies were CC = 89.32%, LS = 89.32%, SVM = 92.03%, and SNN = 96.79%, demonstrating an even larger improvement produced by the machine-learned algorithms in spatial regions critical for imaging tasks. Classification by transmission data alone produced an AUC of 0.773 and accuracy of 85.45%, well below the performance levels of any of the classifiers applied to XRD image data., Conclusions: We demonstrated that ML-based classifiers outperformed rules-based approaches in terms of overall classification accuracy and improved the spatially resolved classification performance on XRD images of medical phantoms. In particular, the ML algorithms demonstrated considerably improved performance whenever multiple materials existed in a single voxel. The quantitative performance gains demonstrate an avenue to extract and harness XRD imaging data to improve material analysis for research, industrial, and clinical applications., (© 2021 American Association of Physicists in Medicine.)

Objectives: Quantifying radiation burden is essential for justification, optimization, and personalization of CT procedures and can be characterized by a variety of risk surrogates inducing different radiological risk reflections. This study compared how twelve such metrics can characterize risk across patient populations., Methods: This study included 1394 CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogates were considered: volume computed tomography dose index (CTDI vol ), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (ED k ), dose to a defining organ (OD D ), effective dose and risk index based on organ doses (ED OD , RI), and risk index for a 20-year-old patient (RI rp ). The last three metrics were also calculated for a reference ICRP-110 model (OD D,0 , ED 0 , and RI 0 ). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as [Formula: see text]. A linear regression was applied to assess each metric's dependency on RI. The results were characterized in terms of risk sensitivity index (RSI) and risk differentiability index (RDI)., Results: The analysis reported significant differences between the metrics with ED r showing the best concordance with RI in terms of RSI and RDI. Across all metrics and protocols, RSI ranged between 0.37 (SSDE) and 1.29 (RI 0 ); RDI ranged between 0.39 (ED k ) and 0.01 (ED r ) cancers × 10 3 patients × 100 mGy., Conclusion: Different risk surrogates lead to different population risk characterizations. ED r exhibited a close characterization of population risk, also showing the best differentiability. Care should be exercised in drawing risk predictions from unrepresentative risk metrics applied to a population., Key Points: • Radiation risk characterization in CT populations is strongly affected by the surrogate used to describe it. • Different risk surrogates can lead to different characterization of population risk. • Healthcare professionals should exercise care in ascribing an implicit risk to factors that do not closely reflect risk., (© 2021. European Society of Radiology.)

X-ray transmission imaging has been used in a variety of applications for high-resolution measurements based on shape and density. Similarly, X-ray diffraction (XRD) imaging has been used widely for molecular structure-based identification of materials. Combining these X-ray methods has the potential to provide high-resolution material identification, exceeding the capabilities of either modality alone. However, XRD imaging methods have been limited in application by their long measurement times and poor spatial resolution, which has generally precluded combined, rapid measurements of X-ray transmission and diffraction. In this work, we present a novel X-ray fan beam coded aperture transmission and diffraction imaging system, developed using commercially available components, for rapid and accurate non-destructive imaging of industrial and biomedical specimens. The imaging system uses a 160 kV Bremsstrahlung X-ray source while achieving a spatial resolution of ≈ 1 × 1 mm 2 and a spectral accuracy of > 95% with only 15 s exposures per 150 mm fan beam slice. Applications of this technology are reported in geological imaging, pharmaceutical inspection, and medical diagnosis. The performance of the imaging system indicates improved material differentiation relative to transmission imaging alone at scan times suitable for a variety of industrial and biomedical applications.

X-ray diffraction (XRD) imaging yields spatially resolved, material-specific information, which can aid medical diagnosis and inform treatment. In this work we used simulations to analyze the utility of fan beam coded aperture XRD imaging for fast, high-resolution scatter imaging of biospecimens for tissue assessment. To evaluate the proposed system's utility in a specific task, we employed a deterministic model to produce simulated data from biologically realistic breast tissue phantoms and model-based reconstruction to recover a spatial map of the XRD signatures throughout the phantoms. We found an XRD spatial resolution of ≈1 mm with a mean reconstructed spectral accuracy of 0.98 ± 0.01 for a simulated 1 × 150 mm 2 fan beam operating at 160 kVp, 10 mA, and 4.5 s exposures. A classifier for cancer detection was developed utilizing cross-correlation of XRD spectra against a spectral library, with a receiver operating characteristic curve with an area under the curve value of 0.972. Our results indicated a potential diagnostic modality that could aid in tasks ranging from analysis of ex-vivo pathology biospecimens to intraoperative cancer margin assessment, motivating future work to develop an experimental system while enabling the development of improved algorithms for imaging and tissue analysis-based classification performance.

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 (CTDI vol ) 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.

X-ray diffraction tomography (XDT) records the spatially-resolved X-ray diffraction profile of an extended object. Compared to conventional transmission-based tomography, XDT displays high intrinsic contrast among materials of similar electron density and improves the accuracy in material identification thanks to the molecular structural information carried by diffracted photons. However, due to the weak diffraction signal, a tomographic scan covering the entire object typically requires a synchrotron facility to make the acquisition time more manageable. Imaging applications in medical and industrial settings usually do not require the examination of the entire object. Therefore, a diffraction tomography modality covering only the region of interest (ROI) and subsequent image reconstruction techniques with truncated projections are highly desirable. Here we propose a table-top diffraction tomography system that can resolve the spatially-variant diffraction form factor from internal regions within extended samples. We demonstrate that the interior reconstruction maintains the material contrast while reducing the imaging time by 6 folds. The presented method could accelerate the acquisition of XDT and be applied in portable imaging applications with a reduced radiation dose.

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.

Although transmission-based x-ray imaging is the most commonly used imaging approach for breast cancer detection, it exhibits false negative rates higher than 15%. To improve cancer detection accuracy, x-ray coherent scatter computed tomography (CSCT) has been explored to potentially detect cancer with greater consistency. However, the 10-min scan duration of CSCT limits its possible clinical applications. The coded aperture coherent scatter spectral imaging (CACSSI) technique has been shown to reduce scan time through enabling single-angle imaging while providing high detection accuracy. Here, we use Monte Carlo simulations to test analytical optimization studies of the CACSSI technique, specifically for detecting cancer in ex vivo breast samples. An anthropomorphic breast tissue phantom was modeled, a CACSSI imaging system was virtually simulated to image the phantom, a diagnostic voxel classification algorithm was applied to all reconstructed voxels in the phantom, and receiver-operator characteristics analysis of the voxel classification was used to evaluate and characterize the imaging system for a range of parameters that have been optimized in a prior analytical study. The results indicate that CACSSI is able to identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) in tissue samples with a cancerous voxel identification area-under-the-curve of 0.94 through a scan lasting less than 10 s per slice. These results show that coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue within ex vivo samples. Furthermore, the results indicate potential CACSSI imaging system configurations for implementation in subsequent imaging development studies.

A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice.

Breast cancer patients undergoing surgery often choose to have a breast conserving surgery (BCS) instead of mastectomy for removal of only the breast tumor. If post-surgical analysis such as histological assessment of the resected tumor reveals insufficient healthy tissue margins around the cancerous tumor, the patient must undergo another surgery to remove the missed tumor tissue. Such re-excisions are reported to occur in 20%-70% of BCS patients. A real-time surgical margin assessment technique that is fast and consistently accurate could greatly reduce the number of re-excisions performed in BCS. We describe here a tumor margin assessment method based on x-ray coherent scatter computed tomography (CSCT) imaging and demonstrate its utility in surgical margin assessment using Monte Carlo simulations. A CSCT system was simulated in GEANT4 and used to simulate two virtual anthropomorphic CSCT scans of phantoms resembling surgically resected tissue. The resulting images were volume-rendered and found to distinguish cancerous tumors embedded in complex distributions of adipose and fibroglandular breast tissue (as is expected in the breast). The images exhibited sufficient spatial and spectral (i.e. momentum transfer) resolution to classify the tissue in any given voxel as healthy or cancerous. ROC analysis of the classification accuracy revealed an area under the curve of up to 0.97. These results indicate that coherent scatter imaging is promising as a possible fast and accurate surgical margin assessment technique.

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.

Here, we present an innovative imaging technology for breast cancer using gamma-ray stimulated spectroscopy based on the nuclear resonance fluorescence (NRF) technique. In NRF, a nucleus of a given isotope selectively absorbs gamma rays with energy exactly equal to one of its quantized energy states, emitting an outgoing gamma ray with energy nearly identical to that of the incident gamma ray. Due to its application of NRF, gamma-ray stimulated spectroscopy is sensitive to trace element concentration changes, which are suspected to occur at early stages of breast cancer, and therefore can be potentially used to noninvasively detect and diagnose cancer in its early stages. Using Monte-Carlo simulations, we have designed and demonstrated an imaging system that uses gamma-ray stimulated spectroscopy for visualizing breast cancer. We show that gamma-ray stimulated spectroscopy is able to visualize breast cancer lesions based primarily on the differences in the concentrations of trace elements between diseased and healthy tissue, rather than differences in density that are crucial for X-ray mammography. The technique shows potential for early breast cancer detection; however, improvements are needed in gamma-ray laser technology for the technique to become a clinically feasible method of detecting and diagnosing cancer at early stages.

This work describes a simulation study investigating the application of neutron stimulated emission computed tomography (NSECT) for noninvasive 3D imaging of renal cancer in vivo. Using MCNP5 simulations, we describe a method of diagnosing renal cancer in the body by mapping the 3D distribution of elements present in tumors using the NSECT technique. A human phantom containing the kidneys and other major organs was modeled in MCNP5. The element composition of each organ was based on values reported in literature. The two kidneys were modeled to contain elements reported in renal cell carcinoma (RCC) and healthy kidney tissue. Simulated NSECT scans were executed to determine the 3D element distribution of the phantom body. Elements specific to RCC and healthy kidney tissue were then analyzed to identify the locations of the diseased and healthy kidneys and generate tomographic images of the tumor. The extent of the RCC lesion inside the kidney was determined using 3D volume rendering. A similar procedure was used to generate images of each individual organ in the body. Six isotopes were studied in this work - (32)S, (12)C, (23)Na, (14)N, (31)P and (39)K. The results demonstrated that through a single NSECT scan performed in vivo, it is possible to identify the location of the kidneys and other organs within the body, determine the extent of the tumor within the organ, and to quantify the differences between cancer and healthy tissue-related isotopes with p ≤ 0.05. All of the images demonstrated appropriate concentration changes between the organs, with some discrepancy observed in (31)P, (39)K and (23)Na. The discrepancies were likely due to the low concentration of the elements in the tissue that were below the current detection sensitivity of the NSECT technique.

We present a quantitative analysis of the image quality obtained using filtered back-projection (FBP) with Ram-Lak filtering and maximum likelihood-expectation maximization (ML-EM)-with no post-reconstruction filtering in either case-in neutron stimulated emission computed tomography (NSECT) imaging using Monte Carlo simulations in the context of clinically relevant models of liver iron overload. The ratios of pixel intensities for several regions of interest and lesion shape detection using an active-contours segmentation algorithm are assessed for accuracy across different scanning configurations and reconstruction algorithms. The modulation transfer functions (MTFs) are also computed for the cases under study and are applied to determine a minimum detectable lesion spacing as a form of sensitivity analysis. The accuracy of NSECT imaging in measuring relative tissue concentration is presented for simulated clinical liver cases. When using the 15th iteration, ML-EM provides at least 25% better resolution than FBP and proves to be highly robust under low-signal high-noise conditions prevalent in NSECT. However, FBP gives more accurate lesion pixel intensity ratios and size estimates in some cases; due to advantages provided by both reconstruction algorithms, it is worth exploring the development of an algorithm that is a hybrid of the two. We also show that NSECT imaging can be used to accurately detect 3-cm lesions in backgrounds that are a significant fraction (one-quarter) of the concentration of the lesion, down to a 4-cm spacing between lesions.

Neutron stimulated emission computed tomography (NSECT) is being developed as a non-invasive imaging modality to detect and quantify iron overload in the human liver. NSECT uses gamma photons emitted by the inelastic interaction between monochromatic fast neutrons and iron nuclei in the body to detect and quantify the disease. Previous simulated and physical experiments with phantoms have shown that NSECT has the potential to accurately diagnose iron overload with reasonable levels of radiation dose. In this work, we describe the results of a simulation study conducted to determine the sensitivity of the NSECT system for hepatic iron quantification in patients of different sizes. A GEANT4 simulation of the NSECT system was developed with a human liver and two torso sizes corresponding to small and large patients. The iron concentration in the liver ranged between 0.5 and 20 mg g(-1), corresponding to clinically reported iron levels in iron-overloaded patients. High-purity germanium gamma detectors were simulated to detect the emitted gamma spectra, which were background corrected using suitable water phantoms and analyzed to determine the minimum detectable level (MDL) of iron and the sensitivity of the NSECT system. These analyses indicate that for a small patient (torso major axis = 30 cm) the MDL is 0.5 mg g(-1) and sensitivity is ∼13 ± 2 Fe counts/mg/mSv and for a large patient (torso major axis = 40 cm) the values are 1 mg g(-1) and ∼5 ± 1 Fe counts/mg/mSv, respectively. The results demonstrate that the MDL for both patient sizes lies within the clinically significant range for human iron overload.

Iron overload disorders have been the focus of several quantification studies involving non-invasive imaging modalities. Neutron spectroscopic techniques have demonstrated great potential in detecting iron concentrations within biological tissue. We are developing a neutron spectroscopic technique called neutron stimulated emission computed tomography (NSECT), which has the potential to diagnose iron overload in the liver at clinically acceptable patient dose levels through a non-invasive scan. The technique uses inelastic scatter interactions between atomic nuclei in the sample and incoming fast neutrons to non-invasively determine the concentration of elements in the sample. This paper discusses a non-tomographic application of NSECT investigating the feasibility of detecting elevated iron concentrations in the liver. A model of iron overload in the human body was created using bovine liver tissue housed inside a human torso phantom and was scanned with a 5 MeV pulsed beam using single-position spectroscopy. Spectra were reconstructed and analyzed with algorithms designed specifically for NSECT. Results from spectroscopic quantification indicate that NSECT can currently detect liver iron concentrations of 6 mg g(-1) or higher and has the potential to detect lower concentrations by optimizing the acquisition geometry to scan a larger volume of tissue. The experiment described in this paper has two important outcomes: (i) it demonstrates that NSECT has the potential to detect clinically relevant concentrations of iron in the human body through a non-invasive scan and (ii) it provides a comparative standard to guide the design of iron overload phantoms for future NSECT liver iron quantification studies.

This paper describes the implementation of neutron-stimulated emission computed tomography (NSECT) for non-invasive imaging and reconstruction of a multi-element phantom. The experimental apparatus and process for acquisition of multi-spectral projection data are described along with the reconstruction algorithm and images of the two elements in the phantom. Independent tomographic reconstruction of each element of the multi-element phantom was performed successfully. This reconstruction result is the first of its kind and provides encouraging proof of concept for proposed subsequent spectroscopic tomography of biological samples using NSECT.

A Monte Carlo simulation has been developed for neutron stimulated emission computed tomography (NSECT) using the GEANT4 toolkit. NSECT is a new approach to biomedical imaging that allows spectral analysis of the elements present within the sample. In NSECT, a beam of high-energy neutrons interrogates a sample and the nuclei in the sample are stimulated to an excited state by inelastic scattering of the neutrons. The characteristic gammas emitted by the excited nuclei are captured in a spectrometer to form multi-energy spectra. Currently, a tomographic image is formed using a collimated neutron beam to define the line integral paths for the tomographic projections. These projection data are reconstructed to form a representation of the distribution of individual elements in the sample. To facilitate the development of this technique, a Monte Carlo simulation model has been constructed from the GEANT4 toolkit. This simulation includes modeling of the neutron beam source and collimation, the samples, the neutron interactions within the samples, the emission of characteristic gammas, and the detection of these gammas in a Germanium crystal. In addition, the model allows the absorbed radiation dose to be calculated for internal components of the sample. NSECT presents challenges not typically addressed in Monte Carlo modeling of high-energy physics applications. In order to address issues critical to the clinical development of NSECT, this paper will describe the GEANT4 simulation environment and three separate simulations performed to accomplish three specific aims. First, comparison of a simulation to a tomographic experiment will verify the accuracy of both the gamma energy spectra produced and the positioning of the beam relative to the sample. Second, parametric analysis of simulations performed with different user-defined variables will determine the best way to effectively model low energy neutrons in tissue, which is a concern with the high hydrogen content in biological tissue. Third, determination of the energy absorbed in tissue during neutron interrogation in order to estimate the dose. Results from these three simulation experiments demonstrate that GEANT4 is an effective simulation platform that can be used to facilitate the future development and optimization of NSECT.

Neutron stimulated emission computed tomography (NSECT) is being developed to noninvasively determine concentrations of trace elements in biological tissue. Studies have shown prominent differences in the trace element concentration of normal and malignant breast tissue. NSECT has the potential to detect these differences and diagnose malignancy with high accuracy with dose comparable to that of a single mammogram. In this study, NSECT imaging was simulated for normal and malignant human breast tissue samples to determine the significance of individual elements in determining malignancy. The normal and malignant models were designed with different elemental compositions, and each was scanned spectroscopically using a simulated 2.5 MeV neutron beam. The number of incident neutrons was varied from 0.5 million to 10 million neutrons. The resulting gamma spectra were evaluated through receiver operating characteristic (ROC) analysis to determine which trace elements were prominent enough to be considered markers for breast cancer detection. Four elemental isotopes (133Cs, 81Br, 79Br, and 87Rb) at five energy levels were shown to be promising features for breast cancer detection with an area under the ROC curve (A(Z)) above 0.85. One of these elements--87Rb at 1338 keV--achieved perfect classification at 10 million incident neutrons and could be detected with as low as 3 million incident neutrons. Patient dose was calculated for each gamma spectrum obtained and was found to range from between 0.05 and 0.112 mSv depending on the number of neutrons. This simulation demonstrates that NSECT has the potential to noninvasively detect breast cancer through five prominent trace element energy levels, at dose levels comparable to other breast cancer screening techniques.