The purpose of this study was to compare the resulting full width at half maximum of slice sensitivity profiles (SSP) generated by several commercially available point response phantoms, and determine an appropriate imaging technique and analysis method. Four CT phantoms containing point response objects designed to produce a delta impulse signal used in this study: a Fluke CT‐SSP phantom, a Gammex 464, a CatPhan 600, and a Kagaku Micro Disc phantom. Each phantom was imaged using 120 kVp, 325 mAs, head scan field of view, 32×0.625 mm helical scan with a 20 mm beam width and a pitch of 0.969. The acquired images were then reconstructed into all available slice thicknesses false(0.625 mm−5.0 mmfalse). A computer program was developed to analyze the images of each dataset for generating a SSP from which the full width at half maximum (FWHM) was determined. Two methods for generating SSPs were evaluated and compared by choosing the mean vs. maximum value in the ROI, along with two methods for evaluating the FWHM of the SSP, linear interpolation and Gaussian curve fitting. FWHMs were compared with the manufacturer's specifications using percent error and z‐test with a significance value of p<0.05. The FWHMs from each phantom were not significantly different false(p≥0.089false) with an average error of 3.5%. The FWHMs from SSPs generated from the mean value were statistically different false(p≤3.99×1013false). The FWHMs from the different FWHM methods were not statistically different false(p≤0.499false). Evaluation of the SSP is dependent on the ROI value used. The maximum value from the ROI should be used to generate the SSP whenever possible. SSP measurement is independent of the phantoms used in this study.PACS number: 87.
The software successfully implements most of the detector tests recommended by TG150. Comparison of these results with those of the parallel effort will validate the draft test definition.
Purpose: A key component of an effective MRI quality control program is radio frequency (RF) coil testing. Coil testing includes the assessment of SNR, uniformity, and ghosting. Performing RF coil analysis can be very time consuming due to the quantity of coils being assessed and the large number of ROIˈs that need to be drawn to obtain the coil performance data. Analysis of acquired data is extremely repetitious, follows a strict set of rules, and therefore, may be amenable to automation. Methods: Software was written using MATLAB (R2009b, The MathWorks, Inc.), a technical computing language. A graphical user interface provided an easy to use structure for the end user to analyze annual coil test images. The algorithm locates the image closest to the isocenter and will automatically place the 8 ROIˈs needed to perform the annual coil assessment as directed by the ACR MR Accreditation Program. Results are displayed in the GUI and exportable to text or a preformatted Excel Spreadsheet for inclusion in an annual physics report. Results: The program has been benchmarked against three physicists and demonstrates an increase in precision of 3% for SNR, 1% for uniformity, and 5% for ghosting. Furthermore, the program eliminates the variance of subsequent measurements of an identical image. Whereas, the measurements of a single physicist varied by 5% for SNR, 1% for uniformity, and 14% for ghosting. Additionally, the average time to process a coil was decreased from 4 minutes manually to less than 8 seconds. Conclusions: The preliminary results demonstrate that automation has the potential to eliminate some of the inherent variability of test results due to manual measurement. Higher precision may facilitate increased sensitivity of the tests for assessment of subtle changes in system performance that may indicate a change in system performance or impending equipment failure.
The purpose of this study was to evaluate several of the standardized image quality metrics proposed by the American Association of Physics in Medicine (AAPM) Task Group 150. The task group suggested region‐of‐interest (ROI)‐based techniques to measure nonuniformity, minimum signal‐to‐noise ratio (SNR), number of anomalous pixels, and modulation transfer function (MTF). This study evaluated the effects of ROI size and layout on the image metrics by using four different ROI sets, assessed result uncertainty by repeating measurements, and compared results with two commercially available quality control tools, namely the Carestream DIRECTVIEW Total Quality Tool (TQT) and the GE Healthcare Quality Assurance Process (QAP). Seven Carestream DRX‐1C (CsI) detectors on mobile DR systems and four GE FlashPad detectors in radiographic rooms were tested. Images were analyzed using MATLAB software that had been previously validated and reported. Our values for signal and SNR nonuniformity and MTF agree with values published by other investigators. Our results show that ROI size affects nonuniformity and minimum SNR measurements, but not detection of anomalous pixels. Exposure geometry affects all tested image metrics except for the MTF. TG‐150 metrics in general agree with the TQT, but agree with the QAP only for local and global signal nonuniformity. The difference in SNR nonuniformity and MTF values between the TG‐150 and QAP may be explained by differences in the calculation of noise and acquisition beam quality, respectively. TG‐150's SNR nonuniformity metrics are also more sensitive to detector nonuniformity compared to the QAP. Our results suggest that fixed ROI size should be used for consistency because nonuniformity metrics depend on ROI size. Ideally, detector tests should be performed at the exact calibration position. If not feasible, a baseline should be established from the mean of several repeated measurements. Our study indicates that the TG‐150 tests can be used as an independent standardized procedure for detector performance assessment.PACS number(s): 87.57.‐s, 87.57.C
#4075 Background: Breast density is possibly the strongest risk factor for breast cancer after genetic predisposition. Estimation of mammographic density can be highly subjective. More accurate volumetric approaches to measure density using mammography are under development, but require x-ray exposure. Dense breast stroma has a lower electrical resistance, which becomes increased as the stroma is replaced with fat. We have previously reported that the impedance of breast stroma correlates with age, obesity and inversely with position in menstrual cycle. It is unknown whether mammographic density also correlates with stromal impedance. When a high frequency sine wave is used to interrogate the breast the impedance of the overlying skin and the breast epithelium are significantly reduced with most of the remaining impedance being due to the stromal tissue and is defined as the sub-epithelial impedance (Zsub).
 Patients and Methods: With IRB and patient consent, electrical contact with ductal epithelium was established non-invasively, using a specially designed nipple sensor in 288 women. Measurements were made between the nipple sensor and skin surface electrodes placed in each of 4 quadrants of the breast. Zsub was measured at 60 KHz using a frequency response analyzer, and sine-wave correlation technique. Data were analyzed using a t-test, Mann-Whitney test, ANOVA, Pearson Moment Correlation, and stepwise regression as appropriate. In a subset of 112 women, in whom mammograms were available, a blinded review was performed to estimate the percent density averaged for two views of each breast.
 Results: Zsub correlated with age of the patients (n = 288), (0.58 Correlation Coefficient (CC), p < 0.0001). Zsub was elevated in women who underwent a biopsy demonstrating breast cancer (n = 52) or proliferative breast disease (n = 27) with values of 211 ± 19Ω (median ± SEM), and 216 ± 18Ω respectively, compared with women with benign biopsies (n = 75) and values of 131 ± 15Ω (p < 0.01). In a subset of patients (n=112) Zsub was inversely correlated with mammographic density (-0.68 CC, p < 0.0001). Body mass index (BMI) increased with age (0.20 CC, p < 0.05), Zsub (0.48 CC, p < 0.00001) and inversely with mammographic density (-0.51 CC, p < 0.0001). Using stepwise regression, mammographic density was predicted by a linear combination of age, BMI and Zsub with a coefficient of 150.22 ± 11.98 (SE), Age -0.64 ± 0.20 (p < 0.002), BMI -1.43 ± 0.42 (p < 0.001) and Zsub -0.21 ± 0.04 (p < 0.001).
 Discussion: Mammographic breast density decreases with increasing age and BMI because of replacement of stromal tissue with fat, which likely also affects the sub-epithelial electrical impedance of the breast. Breast density can be estimated non-invasively and objectively using measurements of Zsub. Increased impedance of the breast parenchyma may represent a novel biomarker of breast cancer risk associated with mammographic breast density. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4075.
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