Determining Organ Doses from CT with Direct Measurements in Postmortem Subjects: Part 2--Correlations with Patient-specific Parameters.

PURPOSE: To generate empirical sets of equations that can be used to calculate patient-specific organ doses resulting from a group of computed tomographic (CT) studies by using data from direct dose measurements performed within a human body. MATERIALS AND METHODS: Organ dose measurements were obtained in eight postmortem female subjects. A chest-abdomen-pelvis protocol was used for this study. The relationships among measured organ doses, body mass index, effective diameter (D(eff)), and volume CT dose index (CTDI(vol)) were investigated. Organ dose equations were developed by means of linear regression from organ dose data, with CTDI(vol) and D(eff) as variables, by using Pearson correlation coefficients and P values to determine correlation strength of fit. Measured organ doses were compared with corresponding size-specific dose estimates (SSDEs). RESULTS: The central-section D(eff) presented similar correlations with organ doses to those from D(eff) measured at specific organ locations. The strongest correlations were observed between the central-section D(eff) and CTDI(vol)-normalized organ doses (R(2): 0.478-0.941). The average of measured organ doses for each subject resulted in an average difference of only 5% from SSDE-calculated doses; however, individual organ doses differed from +31% to -61% from the calculated SSDE. CONCLUSION: The organ dose equations developed represent a method for organ dose estimation from direct organ dose measurements that can estimate organ doses more accurately than the calculated SSDE, which provides a less-specific patient dose estimate.

Determining Organ Doses from CT with Direct Measurements in Postmortem Subjects: Part 1--Methodology and Validation.

PURPOSE: To develop a methodology that allows direct measurement of organ doses from computed tomographic (CT) examinations of postmortem subjects. MATERIALS AND METHODS: In this institutional review board approved study, the x-ray linear attenuation coefficients of various tissues were calculated from the mean CT numbers of images that were obtained in eight embalmed adult female cadavers and compared with the corresponding linear attenuation coefficients calculated from CT images obtained in eight living patients that were body mass index (BMI)-matched. Dosimetry was performed in three of the cadavers by accessing organs of interest and affixing partially sealed vinyl tubes inside them. Optically stimulated luminescent dosimeters (OSLDs) were inserted into the tubes and positioned within the organs of interest and on the skin. OSLDs were read with an InLight MicroStar (Landauer, Glenwood, Ill) reader, and readings were corrected for energy and scatter response. Fifteen tubes containing dosimeters were used, and imaging was repeated twice in each cadaver, for a total of five standard clinical protocols. Average dosimetry values were used for analysis. RESULTS: Differences in linear attenuation coefficients between living and embalmed cadaveric tissues were within 3% for the tissues investigated. Measured organ doses for a chest-abdomen-pelvis CT protocol were less than 32 mGy for all organs measured. Organs that were completely irradiated during a given examination received similar doses, whereas organs that were partially irradiated displayed a large variation in measured organ dose. CONCLUSION: The anatomic and radiation attenuation characteristics of cadavers are comparable to those of living human tissue. This methodology allows direct measurement of organ doses from clinical CT examinations.

Modeling of surface acoustic wave strain sensors using coupling-of-modes analysis.

SAW devices may be configured as strain sensors, providing passive, wireless strain measurement in demanding conditions. A key consideration is the modeling of the sensors, enabling different device designs to be considered. This paper presents a simulation scheme using coupling-of-modes (COM) analysis which allows both the frequency response of a SAW strain sensor and its bias sensitivity to be evaluated. Example applications are presented to demonstrate the use of the model.