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© 2015 Am. Assoc. Phys. Med. Purpose: To investigate the feasibility of detecting breast microcalcification (μCa) with a dedicated breast computed tomography (CT) system based on energy-resolved photon-counting silicon (Si) strip detectors. Methods: The proposed photon-counting breast CT system and a bench-top prototype photoncounting breast CT system were simulated using a simulation package written in MATLAB to determine the smallest detectable μCa. A 14 cm diameter cylindrical phantom made of breast tissue with 20% glandularity was used to simulate an average-sized breast. Five different size groups of calcium carbonate grains, from 100 to 180 μm in diameter, were simulated inside of the cylindrical phantom. The images were acquired with a mean glandular dose (MGD) in the range of 0.7-8 mGy. A total of 400 images was used to perform a reader study. Another simulation study was performed using a 1.6 cm diameter cylindrical phantom to validate the experimental results from a bench-top prototype breast CT system. In the experimental study, a bench-top prototype CT system was constructed using a tungsten anode x-ray source and a single line 256-pixels Si strip photon-counting detector with a pixel pitch of 100 μm. Calcium carbonate grains, with diameter in the range of 105-215 μm, were embedded in a cylindrical plastic resin phantom to simulate μCas. The physical phantoms were imaged at 65 kVp with an entrance exposure in the range of 0.6-8 mGy. A total of 500 images was used to perform another reader study. The images were displayed in random order to three blinded observers, who were asked to give a 4-point confidence rating on each image regarding the presence of μCa. The μCa detectability for each image was evaluated by using the average area under the receiver operating characteristic curve (AUC) across the readers. Results: The simulation results using a 14 cm diameter breast phantom showed that the proposed photon-counting breast CT system can achieve high detection accuracy with an average AUC greater than 0.89 ± 0.07 for μCas larger than 120 μm in diameter at a MGD of 3 mGy. The experimental results using a 1.6 cm diameter breast phantom showed that the prototype system can achieve an average AUC greater than 0.98 ± 0.01 for μCas larger than 140 μm in diameter using an entrance exposure of 1.2 mGy. Conclusions: The proposed photon-counting breast CT system based on a Si strip detector can potentially offer superior image quality to detect μCa with a lower dose level than a standard two-view mammography.

© 2014 American Association of Physicists in Medicine. Purpose: To investigate the feasibility of characterizing a Si strip photon-counting detector using x-ray fluorescence. Methods: X-ray fluorescence was generated by using a pencil beam from a tungsten anode x-ray tube with 2 mm Al filtration. Spectra were acquired at 90° from the primary beam direction with an energy-resolved photon-counting detector based on an edge illuminated Si strip detector. The distances from the source to target and the target to detector were approximately 19 and 11 cm, respectively. Four different materials, containing silver (Ag), iodine (I), barium (Ba), and gadolinium (Gd), were placed in small plastic containers with a diameter of approximately 0.7 cm for x-ray fluorescence measurements. Linear regression analysis was performed to derive the gain and offset values for the correlation between the measured fluorescence peak center and the known fluorescence energies. The energy resolutions and charge-sharing fractions were also obtained from analytical fittings of the recorded fluorescence spectra. An analytical model, which employed four parameters that can be determined from the fluorescence calibration, was used to estimate the detector response function. Results: Strong fluorescence signals of all four target materials were recorded with the investigated geometry for the Si strip detector. The average gain and offset of all pixels for detector energy calibration were determined to be 6.95 mV/keV and -66.33 mV, respectively. The detector's energy resolution remained at approximately 2.7 keV for low energies, and increased slightly at 45 keV. The average charge-sharing fraction was estimated to be 36% within the investigated energy range of 20-45 keV. The simulated detector output based on the proposed response function agreed well with the experimental measurement. Conclusions: The performance of a spectral imaging system using energy-resolved photoncounting detectors is very dependent on the energy calibration of the detector. The proposed x-ray fluorescence technique o ffers an accurate and efficient way to calibrate the energy response of a photon-counting detector.

Purpose: The possible clinical applications which can be performed using a newly developed detector depend on the detector's characteristic performance in a number of metrics including the dynamic range, resolution, uniformity, and stability. The authors have evaluated a prototype energy resolved fast photon counting x-ray detector based on a silicon (Si) strip sensor used in an edge-on geometry with an application specific integrated circuit to record the number of x-rays and their energies at high flux and fast frame rates. The investigated detector was integrated with a dedicated breast spectral computed tomography (CT) system to make use of the detector's high spatial and energy resolution and low noise performance under conditions suitable for clinical breast imaging. The aim of this article is to investigate the intrinsic characteristics of the detector, in terms of maximum output count rate, spatial and energy resolution, and noise performance of the imaging system. Methods: The maximum output count rate was obtained with a 50 W x-ray tube with a maximum continuous output of 50 kVp at 1.0 mA. A109Cd source, with a characteristic x-ray peak at 22 keV from Ag, was used to measure the energy resolution of the detector. The axial plane modulation transfer function (MTF) was measured using a 67 μm diameter tungsten wire. The two-dimensional (2D) noise power spectrum (NPS) was measured using flat field images and noise equivalent quanta (NEQ) were calculated using the MTF and NPS results. The image quality parameters were studied as a function of various radiation doses and reconstruction filters. The one-dimensional (1D) NPS was used to investigate the effect of electronic noise elimination by varying the minimum energy threshold. Results: A maximum output count rate of 100 million counts per second per square millimeter (cps/mm2) has been obtained (1 million cps per 100 × 100 μm pixel). The electrical noise floor was less than 4 keV. The energy resolution measured with the 22 keV photons from a109Cd source was less than 9%. A reduction of image noise was shown in all the spatial frequencies in 1D NPS as a result of the elimination of the electronic noise. The spatial resolution was measured just above 5 line pairs per mm (lp/mm) where 10% of MTF corresponded to 5.4 mm-1. The 2D NPS and NEQ shows a low noise floor and a linear dependence on dose. The reconstruction filter choice affected both of the MTF and NPS results, but had a weak effect on the NEQ. Conclusions: The prototype energy resolved photon counting Si strip detector can offer superior imaging performance for dedicated breast CT as compared to a conventional energy-integrating detector due to its high output count rate, high spatial and energy resolution, and low noise characteristics, which are essential characteristics for spectral breast CT imaging. © 2014 American Association of Physicists in Medicine.

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications. © 2013 American Association of Physicists in Medicine.

Currently, there is no accepted standard for measuring breast density. Dual energy mammography, which has demonstrated accurate measurement in phantoms, has been proposed as one possible method. To examine the use of chemical analysis as a possible means to validate breast density measurements from dual energy mammography, a bovine tissue model was investigated. Known quantities of lean and adipose tissue were compared with composition values measured from dual energy images and chemical analysis.Theoretical simulations were performed to assess the impact variations in breast composition would have on measurement of breast density from a single calibration. Fourteen ex-vivo tissue samples composed of varying amounts of pure lean tissue and pure adipose tissue (lean percentage) from 0 to 100%, in increments of 10%, were imaged using dual energy mammography. This was followed by chemical analysis based on desiccation, trituration, and fat extraction with petroleum ether to determine water, lipid, and protein content. The volumetric lean percentage (VLP) as measured from images (VLP(I)) and as derived from chemical analysis data (VLP(CA)) were compared with the VLP calculated from measurements of sample mass with a scale (VLP(M)). Finally, data from the bovine tissue model in this study were compared to compositional data from a previous report of human tissue composition.The results from simulation suggest a substantial impact on measuring breast density is likely due to changes in anatomical breast composition. VLP(I) was related to the VLP(M) by VLP(I) = 1.53 VLP(M) + 10.0 (r20.99). VLP(CA) was related to VLP(M) by VLP(CA) = 0.76 VLP(M) + 22.8 (r20.99). VLP(I) was related to VLP(CA) by VLP(I) = 2.00 VLP(CA) - 35.6 (r20.99). Bovine adipose tissue was shown to be very similar to human adipose tissue in terms of water, lipid, and protein content with RMS differences of 1.2%. Bovine lean tissue was shown to be very similar to human skeletal muscle tissue and somewhat similar to human mammary gland tissue with RMS differences of 0.4 and 22.2%, respectively.The results of this study show strong linear relationships between volumetric lean percentage measurements using dual energy mammography, chemical analysis and the actual mass. Determining the existence of a relationship between VLP(I) and VLP(CA) was necessary before comparing density results from the dual energy technique to composition data from chemical analysis for samples of unknown composition.