Purpose:To determine effective dose (ED) per unit dose-length product (DLP) conversion factors for computed tomographic (CT) dosimetry. Materials and Methods:A CT dosimetry spreadsheet was used to compute patient ED values and corresponding DLP values. The ratio of ED to DLP was determined with 16-section CT scanners from four vendors, as well as with five models from one manufacturer that spanned more than 25 years. ED-to-DLP ratios were determined for 2-cm scan lengths along the patient axis, as well as for typical scan lengths encountered at head and body CT examinations. The dependence of the ratio of ED to DLP on x-ray tube voltage (in kilovolts) was investigated, and the values obtained with the spreadsheet were compared with those obtained by using two other commercially available CT dosimetry software packages. Results:For 2-cm scan lengths, changes in the scan region resulted in differences to ED of a factor of 30, but much lower variation was obtained for typical scan lengths at clinical head and body imaging. Inter-and intramanufacturer differences for ED/DLP were generally small. Representative values of ED/DLP at 120 kV were 2.2 Sv/mGy ⅐ cm (head scans), 5.4 Sv/mGy ⅐ cm (cervical spine scans), and 18 Sv/mGy ⅐ cm (body scans). For head scans, ED/DLP was approximately independent of x-ray tube voltage, but for body scans, the increase from 80 to 140 kV increased the ratio of ED to DLP by approximately 25%. Agreement in ED/DLP data for all three software packages was generally very good, except for cervical spine examinations where one software package determined an ED/DLP ratio that was approximately double that of the other two. Conclusion:This article describes a method of providing CT users with a practical and reliable estimate of adult patient EDs by using the DLP displayed on the CT console at the end of any given examination.
Digital radiographic imaging systems, such as those using photostimulable storage phosphor, amorphous selenium, amorphous silicon, CCD, and MOSFET technology, can produce adequate image quality over a much broader range of exposure levels than that of screen/film imaging systems. In screen/film imaging, the final image brightness and contrast are indicative of over- and underexposure. In digital imaging, brightness and contrast are often determined entirely by digital postprocessing of the acquired image data. Overexposure and underexposures are not readily recognizable. As a result, patient dose has a tendency to gradually increase over time after a department converts from screen/film-based imaging to digital radiographic imaging. The purpose of this report is to recommend a standard indicator which reflects the radiation exposure that is incident on a detector after every exposure event and that reflects the noise levels present in the image data. The intent is to facilitate the production of consistent, high quality digital radiographic images at acceptable patient doses. This should be based not on image optical density or brightness but on feedback regarding the detector exposure provided and actively monitored by the imaging system. A standard beam calibration condition is recommended that is based on RQA5 but uses filtration materials that are commonly available and simple to use. Recommendations on clinical implementation of the indices to control image quality and patient dose are derived from historical tolerance limits and presented as guidelines.
Our purpose in this study was to investigate the image quality and absorbed dose characteristics of a digital mammography imaging system with a CsI scintillator, and to identify an optimal x-ray tube voltage for imaging simulated masses in an average size breast with 50% glandularity. Images were taken of an ACR accreditation phantom using a LORAD digital mammography system with a Mo target and a Mo filter. In one experiment, exposures were performed at 80 mAs with x-ray tube voltages varying between 24 and 34 kVp. In a second experiment, the x-ray tube voltage was kept constant at 28 kVp and the technique factor was varied between 5 and 500 mAs. The average glandular dose at each x-ray tube voltage was determined from measurements of entrance skin exposure and x-ray beam half-value layer. Image contrast was measured as the fractional digital signal intensity difference for the image of a 4 mm thick acrylic disk. Image noise was obtained from the standard deviation in a uniformly exposed region of interest expressed as a fraction of the background intensity. The measured digital signal intensity was proportional to the mAs and to the kVp5.8. Image contrast was independent of mAs, and dropped by 21% when the x-ray tube voltage increased from 24 to 34 kVp. At a constant x-ray tube voltage, image noise was shown to be approximately proportional to (mAs)(-05), which permits the image contrast to noise ratio (CNR) to be modified by changing the mAs. At 80 mAs, increasing the x-ray tube voltage from 24 to 34 kVp increased the CNR by 78%, and increased the average glandular dose by 285%. At a constant lesion CNR, the lowest average glandular dose value occurred at 27.3 kVp. Increasing or decreasing the x-ray tube voltage by 2.3 kVp from the optimum kVp increased the average glandular dose values by 5%. These results show that imaging simulated masses in a 4.2 cm compressed breast at approximately 27 kVp with a Mo/Mo target/filter results in the lowest average glandular dose.
Additive manufacturing and bio-printing, with the potential for direct fabrication of complex patient-specific anatomies derived from medical scan data, are having an ever-increasing impact on the practice of medicine. Anatomic structures are typically derived from CT or MRI scans, and there are multiple steps in the model derivation process that influence the geometric accuracy of the printed constructs. In this work, we compare the dimensional accuracy of 3-D printed constructs of an L1 vertebra derived from CT data for an ex vivo cadaver T-L spine with the original vertebra. Processing of segmented structures using binary median filters and various surface extraction algorithms is evaluated for the effect on model dimensions. We investigate the effects of changing CT reconstruction kernels by scanning simple geometric objects and measuring the impact on the derived model dimensions. We also investigate if there are significant differences between physical and virtual model measurements. The 3-D models were printed using a commercial 3-D printer, the Replicator 2 (MakerBot, Brooklyn, NY) using polylactic acid (PLA) filament. We found that changing parameters during the scan reconstruction, segmentation, filtering, and surface extraction steps will have an effect on the dimensions of the final model. These effects need to be quantified for specific situations that rely on the accuracy of 3-D printed models used in medicine or tissue engineering applications.
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