Hybrids of two destructive subterranean termites established in the field, revealing a potential for gene flow between species.

Hybridization between invasive pest species may lead to significant genetic and economic impacts that require close monitoring. The two most invasive and destructive termite species worldwide, Coptotermes formosanus Shiraki and Coptotermes gestroi (Wasmann), have the potential for hybridization in the field. A three-year field survey conducted during the dispersal flight season of Coptotermes in Taiwan identified alates with atypical morphology, which were confirmed as hybrids of the two Coptotermes species using microsatellite and mitochondrial analyses. Out of 27,601 alates collected over three years, 4.4% were confirmed as hybrid alates, and some advanced hybrids (>F1 generations) were identified. The hybrid alates had a dispersal flight season that overlapped with the two parental species 13 out of 15 times. Most of the hybrid alates were females, implying that mating opportunities beyond F1 may primarily be possible through female hybrids. However, the incipient colony growth results from all potential mating combinations suggest that only backcross colonies with hybrid males could sometimes lead to brood development. The observed asymmetrical viability and fertility of hybrid alates may critically reduce the probability of advanced-hybrid colonies being established in the field.

Nationwide Termite Pest Survey Conducted in Taiwan as a Citizen Science Project.

Information regarding the species composition and dispersal flight season of termites is crucial for termite management. The major obstacles to collecting such information are a lack of access to private buildings and shortage of workers to monitor and report on termite swarming. To overcome these difficulties, we launched a citizen science project in which members of the public and pest management professionals were invited to collect termite samples. We created the website, Taiwan Termite Identification Service, on which populace could log the collection information, and ship termite samples to our laboratory for identification. We also established a Facebook group, called the "Termite Forum," to publicize this project. A total of 3024 samples were collected from 2015 to 2020, and we identified the species of >93% of the samples. Based on 1499 samples collected from buildings, five structural termite pests were identified, and species composition in each county of Taiwan is available. According to 844 dispersal flight events, termite dispersal flight timing peak and degree of centralization were estimated using a Gaussian model. The collected data demonstrated that the invasive termite species, Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae), continued northward expansion. The first intercepted alate of Schedorhinotermes sp. (Blattodea: Rhinotermitidae) indicated that it may be a new invasive pest from Southeast Asia. This study reports on a successful case of a citizen science project where urban pest data were collected on a national scale.

Termite Pest Identification Method Based on Deep Convolution Neural Networks.

Several species of drywood termites, subterranean termites, and fungus-growing termites cause extensive economic losses annually worldwide. Because no universal method is available for controlling all termites, correct species identification is crucial for termite management. Despite deep neural network technologies' promising performance in pest recognition, a method for automatic termite recognition remains lacking. To develop an automated deep learning classifier for termite image recognition suitable for mobile applications, we used smartphones to acquire 18,000 original images each of four termite pest species: Kalotermitidae: Cryptotermes domesticus (Haviland); Rhinotermitidae: Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima); and Termitidae: Odontotermes formosanus (Shiraki). Each original image included multiple individuals, and we applied five image segmentation techniques for capturing individual termites. We used 24,000 individual-termite images (4 species × 2 castes × 3 groups × 1,000 images) for model development and testing. We implemented a termite classification system by using a deep learning-based model, MobileNetV2. Our models achieved high accuracy scores of 0.947, 0.946, and 0.929 for identifying soldiers, workers, and both castes, respectively, which is not significantly different from human expert performance. We further applied image augmentation techniques, including geometrical transformations and intensity transformations, to individual-termite images. The results revealed that the same classification accuracy can be achieved by using 1,000 augmented images derived from only 200 individual-termite images, thus facilitating further model development on the basis of many fewer original images. Our image-based identification system can enable the selection of termite control tools for pest management professionals or homeowners.

124I-MIBG PET/CT to Monitor Metastatic Disease in Children with Relapsed Neuroblastoma.

The metaiodobenzylguanidine (MIBG) scan is one of the most sensitive noninvasive lesion detection modalities for neuroblastoma. Unlike 123I-MIBG, 124I-MIBG allows high-resolution PET. We evaluated 124I-MIBG PET/CT for its diagnostic performance as directly compared with paired 123I-MIBG scans. Methods: Before 131I-MIBG therapy, standard 123I-MIBG imaging (5.2 MBq/kg) was performed on 7 patients, including whole-body (anterior-posterior) planar imaging, focused-field-of-view SPECT/CT, and whole-body 124I-MIBG PET/CT (1.05 MBq/kg). After therapy, 2 of 7 patients also completed 124I-MIBG PET/CT as well as paired 123I-MIBG planar imaging and SPECT/CT. One patient underwent 124I-MIBG PET/CT only after therapy. We evaluated all 8 patients who showed at least 1 123I-MIBG-positive lesion with a total of 10 scans. In 8 pairs, 123I-MIBG and 124I-MIBG were performed within 1 mo of each other. The locations of identified lesions, the number of total lesions, and the curie scores were recorded for the 123I-MIBG and 124I-MIBG scans. Finally, for 5 patients who completed at least 3 PET/CT scans after administration of 124I-MIBG, we estimated the effective dose of 124I-MIBG. Results:123I-MIBG whole-body planar scans, focused-field-of-view SPECT/CT scans, and whole-body 124I-MIBG PET scans found 25, 32, and 87 total lesions, respectively. There was a statistically significant difference in lesion detection for 124I-MIBG PET/CT versus 123I-MIBG planar imaging (P < 0.0001) and 123I-MIBG SPECT/CT (P < 0.0001). The curie scores were also higher for 124I-MIBG PET/CT than for 123I-MIBG planar imaging and SPECT/CT in 6 of 10 patients. 124I-MIBG PET/CT demonstrated better detection of lesions throughout the body, including the chest, spine, head and neck, and extremities. The effective dose estimated for patient-specific 124I-MIBG was approximately 10 times that of 123I-MIBG; however, given that we administered a very low activity of 124I-MIBG (1.05 MBq/kg), the effective dose was only approximately twice that of 123I-MIBG despite the large difference in half-lives (100 vs. 13.2 h). Conclusion: The first-in-humans use of low-dose 124I-MIBG PET for monitoring disease burden demonstrated tumor detection capability superior to that of 123I-MIBG planar imaging and SPECT/CT.

Factors to influence the accuracy of albumin adjusted free valproic acid concentration.

BACKGROUND: Therapeutic drug monitoring (TDM) is recommended during treatment with valproic acid (VPA), as is the measurement of free VPA concentration (MfVPA). However, MfVPA is unavailable in many institutions. Based on the highly protein-bound characteristics of VPA, an albumin-adjusted formula has been proposed to predict free VPA concentration (PfVPA). Nevertheless, the factors affecting the accuracy of this formula remain unknown, as does the concordance between MfVPA and PfVPA. METHODS: Adult patients receiving VPA and undergoing TDM were enrolled. Free and total serum concentration (TVPA) were categorized as subtherapeutic, therapeutic, or supratherapeutic based on the reference range of 5-15 and 50-100 µg/mL, respectively. Concordance was defined as MfVPA and PfVPA, or MfVPA and TVPA, falling within the same category. Multivariate logistic regression with generalized estimating equation was adopted to identify factors affecting concordance, and the receiver operating characteristic curve was applied to determine the cutoff values of predictors. RESULTS: A total of 98 data points from 51 participants were included for analysis. The concordance of MfVPA and PfVPA, and MfVPA and TVPA, was 72% and 44%, respectively. Blood urea nitrogen (BUN) (0.97 [0.95-0.99], P = 0.01) and TVPA (0.97 [0.95-0.99], P = 0.02) had a significant influence on the concordance of MfVPA and PfVPA. The cutoff values of TVPA and BUN for the accuracy of the albumin-adjusted formula were 56.4 µg/mL and 51.05 mg/dL, respectively. CONCLUSION: If MfVPA is not available, the albumin-adjusted formula should be applied before VPA dosage adjustment when TVPA is < 56.4 µg/mL and BUN is < 51.05 mg/dL.

Safety range of free valproic acid serum concentration in adult patients.

BACKGROUND: Therapeutic drug monitoring (TDM) is recommended during valproic acid (VPA) use, and total serum concentration has been widely adopted. However, the free form of VPA is responsible for its pharmacologic and toxic effects, and the total and free concentrations are highly discordant because of VPA's highly protein bound and saturable binding characteristics. Therefore, free VPA monitoring is increasingly advocated. Nevertheless, the correlation between free VPA concentration and associated adverse effects remains unknown. OBJECTIVE: To determine the optimal safety range of free VPA concentration in adult patients. MATERIALS AND METHODS: This prospective cohort study enrolled adult patients undergoing VPA therapy with TDM. Patient characteristics, VPA use, and adverse effects (thrombocytopenia, hyperammonemia, and hepatotoxicity) were recorded. A multivariate logistic regression model was applied to identify the predictors of adverse effects, and the receiver operating characteristic curve was applied to locate the cutoff point of free VPA concentration. RESULTS: A total of 98 free serum concentrations from 51 patients were included for final analysis. In total, 31 (31.6%), 27 (27.6%), and 4 (4.1%) episodes of hyperammonemia, thrombocytopenia, and hepatotoxicity were observed, respectively. Free VPA concentration was a predicting factor for thrombocytopenia but not for hyperammonemia. A free VPA concentration of >14.67 mcg/mL had the greatest discriminating power (area under the curve = 0.77) for the occurrence of thrombocytopenia. CONCLUSIONS: A free VPA serum concentration of 14.67 mcg/mL had the optimal discriminating power for the occurrence of thrombocytopenia. Ammonemia should be monitored even if free VPA concentration is within the safety range.

Technical Note: Simplified and practical pretherapy tumor dosimetry - A feasibility study for 131 I-MIBG therapy of neuroblastoma using 124 I-MIBG PET/CT.

PURPOSE: Radiation dose calculated on tumors for radiopharmaceutical therapy varies significantly from tumor to tumor and from patient to patient. Accurate estimation of radiation dose requires multiple time point measurements using radionuclide imaging modalities such as SPECT or PET. In this report, we show our technical development of reducing the number of scans needed for reasonable estimation of tumor and normal organ dose in our pretherapy imaging and dosimetry platform of 124 I-metaiodobenzylguanidine (MIBG) positron emission tomography/computed tomography (PET/CT) for 131 I-MIBG therapy of neuroblastoma. METHODS: We analyzed the simplest kinetic data, areas of two-time point data for five patients with neuroblastoma who underwent 3 or 4 times of 124 I-MIBG PET/CT scan prior to 131 I-MIBG therapy. The data for which we derived areas were percent of injected activity (%IA) and standardized uptake value of tumors. These areas were correlated with time-integrated activity coefficients (TIACs) from full data (3 or 4 time points). TIACs are direct correlates with radiation dose as long as the volume and the radionuclide are known. RESULTS: The areas of %IAs between data obtained from all the two-time points with time points 1 and 2 (day 0 and day 1), time points 2 and 3 (day 1 and day 2), and time points 1 and 3 (day 0 and day 2) showed reasonable correlation (Pearson's correlation coefficient |r| > 0.5) with not only tumor and organ TIACs but also tumor and organ absorbed doses. The tumor and organ doses calculated using %IA areas of time point 1 and time point 2 were our best fits at about 20% individual percent difference compared to doses calculated using 3 or 4 time points. CONCLUSIONS: We could achieve reasonable accuracy of estimating tumor doses for subsequent radiopharmaceutical therapy using only the two-time point imaging sessions. Images obtained from these time points (within the 48-h after administration of radiopharmaceutical) were also viewed as useful for diagnostic reading. Although our analysis was specific to 124 I-MIBG PET/CT pretherapy imaging data for 131 I-MIBG therapy of neuroblastoma and the number of imaging datasets was not large, this feasible methodology would generally be applicable to other imaging and therapeutic radionuclides with an appropriate data analysis similar to our analysis to other imaging and therapeutic radiopharmaceuticals.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival.

Simultaneous detections of genetic fragment and single nucleotide mutation with a three-tiered output for tuberculosis diagnosis.

Tuberculosis (TB) remains one of the major infectious diseases worldwide. The pathogenic bacterium, Mycobacterium tuberculosis (M.tb), continuously evolves strains carrying drug-resistance genes, thus posing a growing challenge to TB prevention and treatment. We report a diagnostic system that uses a molecular beacon probe and an assistant strand as the core to simultaneously interact with an M.tb-specific fragment (in IS6110) and a single nucleotide substitution (SNS)-encoded segment (in rpoB) associated with drug resistance. A single fluorescent output in three-tiered levels was produced for combinatorial interpretations based on formation of a four-way DNA junction (4WJ). The SNS caused the 4WJ to partially dissociate, thus resulting in medium-level fluorescence. By contrast, high- and low-level fluorescence, represented the complete complementary complex and absence of either targeted fragments, respectively. Manipulating the length of the analyte-binding arm realized the medium output. The thermodynamics and kinetics of 4WJ construction were investigated to maximize the tiered-output performance. Biocatalytic amplification driven by the Klenow Fragment and Nt.AlwI was incorporated into the method to enhance the signal 64-fold and ensure long-term stability of the three-tiered output. The detection accuracy of the sensing system was verified using unpurified amplicons with templates of extracted DNA and boiled bacterial solutions. The tiered-output mechanism was usable at bacterial loads ranging from 4 × 100 to 4 × 103 CFU per reaction. The interference caused by nontuberculous mycobacteria was minimal. The results demonstrated the integrity of the sensing method as an alternative strategy for rapid screening of M.tb and detecting rifampin-resistance.

Patient-specific dosimetry using pretherapy [¹²4I]m-iodobenzylguanidine ([¹²4I]mIBG) dynamic PET/CT imaging before [¹³¹I]mIBG targeted radionuclide therapy for neuroblastoma.

PURPOSE: Iodine-131-m-iodobenzylguanidine ([(131)I]mIBG)-targeted radionuclide therapy (TRT) is a standard treatment for recurrent or refractory neuroblastoma with response rates of 30-40 %. The aim of this study is to demonstrate patient-specific dosimetry using quantitative [(124)I]mIBG positron emission tomography/X-ray computed tomography (PET/CT) imaging with a GEometry ANd Tracking 4 (Geant4)-based Monte Carlo method for better treatment planning. PROCEDURES: A Monte Carlo dosimetry method was developed using the Geant4 toolkit with voxelized anatomical geometry and source distribution as input. The presegmented hybrid computational human phantoms developed by the University of Florida and the National Cancer Institute (UF/NCI) were used as a surrogate to characterize the anatomy of a given patient. S values for I-131 were estimated by the phantoms coupled with Geant4 and compared with those estimated by OLINDA|EXM and MCNPX for the newborn model. To obtain patient-specific biodistribution of [(131)I]mIBG, a 10-year-old girl with relapsed neuroblastoma was imaged with [(124)I]mIBG PET/CT at four time points prior to the planned [(131)I]mIBG TRT. The organ- and tumor-absorbed doses of the clinical case were estimated with the Geant4 method using the modified UF/NCI 10-year-old phantom with tumors and the patient-specific residence time. RESULTS: For the newborn model, the Geant4 S values were consistent with the MCNPX S values. The S value ratio of the Geant4 method to OLINDA|EXM ranged from 0.08 to 6.5 of all major organs. The [(131)I]mIBG residence time quantified from the pretherapy [(124)I]mIBG PET/CT imaging of the 10-year-old patient was mostly comparable to those previously reported. Organ-absorbed dose for the salivary glands was 98.0 Gy, heart wall 36.5 Gy, and liver 34.3 Gy, while tumor-absorbed dose ranged from 143.9 to 1,641.3 Gy in different sites. CONCLUSIONS: Patient-specific dosimetry for [(131)I]mIBG TRT was accomplished using pretherapy [(124)I]mIBG PET/CT imaging and a Geant4-based Monte Carlo dosimetry method. The Geant4 method with quantitative pretherapy imaging can provide dose estimates to normal organs and tumors with more realistic simulation geometry, and thus may improve treatment planning for [(131)I]mIBG TRT.

The Effect of Magnetic Field on Positron Range and Spatial Resolution in an Integrated Whole-Body Time-Of-Flight PET/MRI System.

Simultaneous imaging systems combining positron emission tomography (PET) and magnetic resonance imaging (MRI) have been actively investigated. A PET/MR imaging system (GE Healthcare) comprised of a time-of-flight (TOF) PET system utilizing silicon photomultipliers (SiPMs) and 3-tesla (3T) MRI was recently installed at our institution. The small-ring (60 cm diameter) TOF PET subsystem of this PET/MRI system can generate images with higher spatial resolution compared with conventional PET systems. We have examined theoretically and experimentally the effect of uniform magnetic fields on the spatial resolution for high-energy positron emitters. Positron emitters including 18F, 124I, and 68Ga were simulated in water using the Geant4 Monte Carlo toolkit in the presence of a uniform magnetic field (0, 3, and 7 Tesla). The positron annihilation position was tracked to determine the 3D spatial distribution of the 511-keV gammy ray emission. The full-width at tenth maximum (FWTM) of the positron point spread function (PSF) was determined. Experimentally, 18F and 68Ga line source phantoms in air and water were imaged with an investigational PET/MRI system and a PET/CT system to investigate the effect of magnetic field on the spatial resolution of PET. The full-width half maximum (FWHM) of the line spread function (LSF) from the line source was determined as the system spatial resolution. Simulations and experimental results show that the in-plane spatial resolution was slightly improved at field strength as low as 3 Tesla, especially when resolving signal from high-energy positron emitters in the air-tissue boundary.

A semiempirical linear model of indirect, flat-panel x-ray detectors.

PURPOSE: It is important to understand signal and noise transfer in the indirect, flat-panel x-ray detector when developing and optimizing imaging systems. For optimization where simulating images is necessary, this study introduces a semiempirical model to simulate projection images with user-defined x-ray fluence interaction. METHODS: The signal and noise transfer in the indirect, flat-panel x-ray detectors is characterized by statistics consistent with energy-integration of x-ray photons. For an incident x-ray spectrum, x-ray photons are attenuated and absorbed in the x-ray scintillator to produce light photons, which are coupled to photodiodes for signal readout. The signal mean and variance are linearly related to the energy-integrated x-ray spectrum by empirically determined factors. With the known first- and second-order statistics, images can be simulated by incorporating multipixel signal statistics and the modulation transfer function of the imaging system. To estimate the semiempirical input to this model, 500 projection images (using an indirect, flat-panel x-ray detector in the breast CT system) were acquired with 50-100 kilovolt (kV) x-ray spectra filtered with 0.1-mm tin (Sn), 0.2-mm copper (Cu), 1.5-mm aluminum (Al), or 0.05-mm silver (Ag). The signal mean and variance of each detector element and the noise power spectra (NPS) were calculated and incorporated into this model for accuracy. Additionally, the modulation transfer function of the detector system was physically measured and incorporated in the image simulation steps. For validation purposes, simulated and measured projection images of air scans were compared using 40 kV∕0.1-mm Sn, 65 kV∕0.2-mm Cu, 85 kV∕1.5-mm Al, and 95 kV∕0.05-mm Ag. RESULTS: The linear relationship between the measured signal statistics and the energy-integrated x-ray spectrum was confirmed and incorporated into the model. The signal mean and variance factors were linearly related to kV for each filter material (r(2) of signal mean to kV: 0.91, 0.93, 0.86, and 0.99 for 0.1-mm Sn, 0.2-mm Cu, 1.5-mm Al, and 0.05-mm Ag, respectively; r(2) of signal variance to kV: 0.99 for all four filters). The comparison of the signal and noise (mean, variance, and NPS) between the simulated and measured air scan images suggested that this model was reasonable in predicting accurate signal statistics of air scan images using absolute percent error. Overall, the model was found to be accurate in estimating signal statistics and spatial correlation between the detector elements of the images acquired with indirect, flat-panel x-ray detectors. CONCLUSIONS: The semiempirical linear model of the indirect, flat-panel x-ray detectors was described and validated with images of air scans. The model was found to be a useful tool in understanding the signal and noise transfer within indirect, flat-panel x-ray detector systems.

The characterization of breast anatomical metrics using dedicated breast CT.

PURPOSE: Accurate anatomical characterization of the breast is useful in breast phantom development and computer modeling of breast imaging technologies. Capitalizing on the three-dimensional capabilities of dedicated breast CT (bCT), a number of parameters which describe breast shape and fibroglandular distribution are defined. METHODS: Among 219 bCT data sets, the effective diameter and length of the pendant breast as well as the breast volume were measured and characterized for each bra cup size. The volume glandular fraction (VGF) was determined as a function of patient age, BIRADS density, bra cup size, and breast diameter. The glandular fraction was examined in coronal and sagittal planes of the breast, and the radial distribution of breast glandular fraction within a coronal bCT image was examined for three breast regions. The areal glandular fraction (AGF) was estimated from two-dimensional projections of the breast (simulated by projecting bCT data sets) and was compared to the corresponding VGF. RESULTS: The effective breast diameter and length increase with increasing bra cup size. The mean breast diameters (+/- standard error) of bra cup sizes A/AA, B, C, and D/DD were 11.1 +/- 0.5, 11.4 +/- 0.3, 13.0 +/- 0.2, and 13.7 +/- 0.2 cm, respectively. VGF was lower among older women and those with larger breast diameter and larger bra cup size. VGF increased as a function of the reported BIRADS density. AGF increased with VGF. Fibroglandular tissue was distributed primarily in the central portion of the breast. CONCLUSIONS: Breast metrics were examined and a number of parameters were defined which may be useful for breast modeling. The reported data may provide researchers with useful information for characterizing the breast for various imaging or dosimetry tasks.

Experimentally determined spectral optimization for dedicated breast computed tomography.

PURPOSE: The current study aimed to experimentally identify the optimal technique factors (x-ray tube potential and added filtration material/thickness) to maximize soft-tissue contrast, microcalcification contrast, and iodine contrast enhancement using cadaveric breast specimens imaged with dedicated breast computed tomography (bCT). Secondarily, the study aimed to evaluate the accuracy of phantom materials as tissue surrogates and to characterize the change in accuracy with varying bCT technique factors. METHODS: A cadaveric breast specimen was acquired under appropriate approval and scanned using a prototype bCT scanner. Inserted into the specimen were cylindrical inserts of polyethylene, water, iodine contrast medium (iodixanol, 2.5 mg/ml), and calcium hydroxyapatite (100 mg/ml). Six x-ray tube potentials (50, 60, 70, 80, 90, and 100 kVp) and three different filters (0.2 mm Cu, 1.5 mm Al, and 0.2 mm Sn) were tested. For each set of technique factors, the intensity (linear attenuation coefficient) and noise were measured within six regions of interest (ROIs): Glandular tissue, adipose tissue, polyethylene, water, iodine contrast medium, and calcium hydroxyapatite. Dose-normalized contrast to noise ratio (CNRD) was measured for pairwise comparisons among the six ROIs. Regression models were used to estimate the effect of tube potential and added filtration on intensity, noise, and CNRD. RESULTS: Iodine contrast enhancement was maximized using 60 kVp and 0.2 mm Cu. Microcalcification contrast and soft-tissue contrast were maximized at 60 kVp. The 0.2 mm Cu filter achieved significantly higher CNRD for iodine contrast enhancement than the other two filters (p = 0.01), but microcalcification contrast and soft-tissue contrast were similar using the copper and aluminum filters. The average percent difference in linear attenuation coefficient, across all tube potentials, for polyethylene versus adipose tissue was 1.8%, 1.7%, and 1.3% for 0.2 mm Cu, 1.5 mm Al, and 0.2 mm Sn, respectively. For water versus glandular tissue, the average percent difference was 2.7%, 3.9%, and 4.2% for the three filter types. CONCLUSIONS: Contrast-enhanced bCT, using injected iodine contrast medium, may be optimized for maximum contrast of enhancing lesions at 60 kVp with 0.2 mm Cu filtration. Soft-tissue contrast and microcalcification contrast may also benefit from lower tube potentials (60 kVp). The linear attenuation coefficients of water and polyethylene slightly overestimate the values of their corresponding tissues, but the reported differences may serve as guidance for dosimetry and quality assurance using tissue equivalent phantoms.

Noise variance analysis using a flat panel x-ray detector: a method for additive noise assessment with application to breast CT applications.

PURPOSE: A simplified linear model approach was proposed to accurately model the response of a flat panel detector used for breast CT (bCT). METHODS: Individual detector pixel mean and variance were measured from bCT projection images acquired both in air and with a polyethylene cylinder, with the detector operating in both fixed low gain and dynamic gain mode. Once the coefficients of the linear model are determined, the fractional additive noise can be used as a quantitative metric to evaluate the system's efficiency in utilizing x-ray photons, including the performance of different gain modes of the detector. RESULTS: Fractional additive noise increases as the object thickness increases or as the radiation dose to the detector decreases. For bCT scan techniques on the UC Davis prototype scanner (80 kVp, 500 views total, 30 frames/s), in the low gain mode, additive noise contributes 21% of the total pixel noise variance for a 10 cm object and 44% for a 17 cm object. With the dynamic gain mode, additive noise only represents approximately 2.6% of the total pixel noise variance for a 10 cm object and 7.3% for a 17 cm object. CONCLUSIONS: The existence of the signal-independent additive noise is the primary cause for a quadratic relationship between bCT noise variance and the inverse of radiation dose at the detector. With the knowledge of the additive noise contribution to experimentally acquired images, system modifications can be made to reduce the impact of additive noise and improve the quantum noise efficiency of the bCT system.

Contrast-enhanced dedicated breast CT: initial clinical experience.

PURPOSE: To quantify contrast material enhancement of breast lesions scanned with dedicated breast computed tomography (CT) and to compare their conspicuity with that at unenhanced breast CT and mammography. MATERIALS AND METHODS: Approval of the institutional review board and the Radiation Use Committee and written informed consent were obtained for this HIPAA-compliant study. Between September 2006 and April 2009, 46 women (mean age, 53.2 years; age range, 35-72 years) with Breast Imaging Reporting and Data System category 4 or 5 lesions underwent unenhanced breast CT and contrast material-enhanced breast CT before biopsy. Two radiologists independently scored lesion conspicuity for contrast-enhanced breast CT versus mammography and for contrast-enhanced breast CT versus unenhanced breast CT. Mean lesion voxel intensity was measured in Hounsfield units and normalized to adipose tissue intensity on manually segmented images obtained before and after administration of contrast material. Regression models focused on conspicuity and quantified enhancement were used to estimate the effect of pathologic diagnosis (benign vs malignant), lesion type (mass vs calcifications), breast density, and interradiologist variability. RESULTS: Fifty-four lesions (25 benign, 29 malignant) in 46 subjects were analyzed. Malignant lesions were seen significantly better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) or mammography (P < .001). Malignant calcifications (malignant lesions manifested mammographically as microcalcifications only, n = 7) were seen better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) and were seen similarly at contrast-enhanced breast CT and mammography. Malignant lesions enhanced 55.9 HU +/- 4.0 (standard error), whereas benign lesions enhanced 17.6 HU +/- 6.1 (P < .001). Ductal carcinoma in situ (n = 5) enhanced a mean of 59.6 HU +/- 2.8. Receiver operating characteristic curve analysis of lesion enhancement yielded an area under the receiver operating characteristic curve of 0.876. CONCLUSION: Conspicuity of malignant breast lesions, including ductal carcinoma in situ, is significantly improved at contrast-enhanced breast CT. Quantifying lesion enhancement may aid in the detection and diagnosis of breast cancer.

An X-Ray computed tomography/positron emission tomography system designed specifically for breast imaging.

Mammography has served the population of women who are at-risk for breast cancer well over the past 30 years. While mammography has undergone a number of changes as digital detector technology has advanced, other modalities such as computed tomography have experienced technological sophistication over this same time frame as well. The advent of large field of view flat panel detector systems enable the development of breast CT and several other niche CT applications, which rely on cone beam geometry. The breast, it turns out, is well suited to cone beam CT imaging because the lack of bones reduces artifacts, and the natural tapering of the breast anteriorly reduces the x-ray path lengths through the breast at large cone angle, reducing cone beam artifacts as well. We are in the process of designing a third prototype system which will enable the use of breast CT for image guided interventional procedures. This system will have several copies fabricated so that several breast CT scanners can be used in a multi-institutional clinical trial to better understand the role that this technology can bring to breast imaging.

The effect of skin thickness determined using breast CT on mammographic dosimetry.

The effect of breast skin thickness on dosimetry in mammography was investigated. Breast computed tomography (CT) acquisition techniques, combined with algorithms designed for determining specific breast metrics, were useful for estimating skin thickness. A radial-geometry edge detection scheme was implemented on coronal reconstructed breast CT (bCT) images to measure the breast skin thickness. Skin thickness of bilateral bCT volume data from 49 women and unilateral bCT volume data from 2 women (10 healthy women and 41 women with BIRADS 4 and 5 diagnoses) was robustly measured with the edge detection scheme. The mean breast skin thickness (+/-inter-breast standard deviation) was found to be 1.45 +/- 0.30 mm. Since most current published normalized glandular dose (DgN) coefficients are based on the assumption of a 4-mm breast skin thickness, the DgN values computed with Monte Carlo techniques will increase up to 18% due to the thinner skin layers (e.g., 6-cm 50% glandular breast, 28 kVp Mo-Mo spectrum). The thinner skin dimensions found in this study suggest that the current DgN values used for mammographic dosimetry lead to a slight underestimate in glandular dose.

Noise power properties of a cone-beam CT system for breast cancer detection.

The noise power properties of a cone-beam computed tomography (CT) system dedicated for breast cancer detection were investigated. Uniform polyethylene cylinders of various diameters were scanned under different system acquisition conditions. Noise power spectra were calculated from difference data generated by subtraction between two identical scans. Multidimensional noise power spectra (NPS) were used as the metric to evaluate the noise properties of the breast CT (bCT) under different system acquisition and reconstruction conditions. A comprehensive investigation of the noise properties was performed in regard to system acquisition parameters including kVp, mA, number of cone-beam projection images used, cone angle, and object size. The influence on reconstruction parameters including interpolation method, reconstruction filter, field of view, matrix size, and slice thickness were also studied. Under certain conditions, the zero-dimensional NPS (image variance) was used as a quantitative index to compare the influence from different scan parameters, especially the radiation dose. If the total scan dose is changed by linearly changing the total number of projection images while the dose per frame is kept constant, the noise power has a linear relationship with the reciprocal of the total dose. If the total scan dose is changed by linearly changing the dose per frame while the total number of projection images is kept constant, the noise power has a quadratic relationship with the reciprocal of the total dose. With the same amount of total dose, using fewer projection images results in lower image noise power in the CT image. Quantitative results from this noise power analysis provide guidance for the bCT system operation, optimization, and data reconstruction.