Sleep-wake states change the interictal localization of candidate epileptic source generators.

STUDY OBJECTIVES: To compare estimated epileptic source localizations from 5 sleep-wake states (SWS): wakefulness (W), rapid eye movement sleep (REM), and non-REM 1-3. METHODS: Electrical source localization (sLORETA) of interictal spikes from different SWS on surface EEG from the epilepsy monitoring unit at spike peak and take-off, with results mapped to individual brain models for 75% of patients. Concordance was defined as source localization voxels shared between 2 and 5 SWS, and discordance as those unique to 1 SWS against 1-4 other SWS. RESULTS: 563 spikes from 16 prospectively recruited focal epilepsy patients across 161 day-nights. SWS exerted significant differences at spike peak but not take-off. Source localization size did not vary between SWS. REM localizations were smaller in multifocal than unifocal patients (28.8% vs. 54.4%, p = .0091). All five SWS contributed about 45% of their localizations to converge onto 17.0 ± 15.5% voxels. Against any one other SWS, REM was least concordant (54.4% vs. 66.9%, p = .0006) and most discordant (39.3% vs. 29.6%, p = .0008). REM also yielded the most unique localizations (20.0% vs. 8.6%, p = .0059). CONCLUSIONS: REM was best suited to identify candidate epileptic sources. sLORETA proposes a model in which an "omni-concordant core" of source localizations shared by all five SWS is surrounded by a "penumbra" of source localizations shared by some but not all SWS. Uniquely, REM spares this core to "move" source voxels from the penumbra to unique cortex not localized by other SWS. This may reflect differential intra-spike propagation in REM, which may account for its reported superior localizing abilities.

Modeling the temporal-spatial nature of the readout of an electronic portal imaging device (EPID).

PURPOSE: In real-time electronic portal imaging device (EPID) dosimetry applications where on-treatment measured transmission images are compared to an ideal predicted image, ideally a tight tolerance should be set on the quantitative image comparison in order to detect a wide variety of possible delivery errors. However, this is currently not possible due to the appearance of banding artifacts in individual frames of the measured EPID image sequences. The purpose of this work was to investigate simulating banding artifacts in our cine-EPID predicted image sequences to improve matching of individual image frames to the acquired image sequence. Increased sensitivity of this method to potential treatment delivery errors would represent an improvement in patient safety and treatment accuracy. METHODS: A circuit board was designed and built to capture the target current (TARG-I) and forward power signals produced by the linac to help model the discrete beam-formation process of the linac. To simulate the temporal-spatial nature of the EPID readout, a moving read out mask was applied with the timing of the application of the readout mask synchronized to the TARG-I pulses. Since identifying the timing of the first TARG-I pulse affected the location of the banding artifacts throughout the image sequence, and furthermore the first several TARG-I pulses at the beginning of "beam on" are not at full height yet (i.e., dose rate is ramping up), the forward-power signal was also used to assist in reliable detection of the first radiation pulse of the beam delivery. The predicted EPID cine-image sequence obtained using a comprehensive physics-based model was modified to incorporate the discrete nature of the EPID frame readout. This modified banding predicted EPID (MBP-EPID) image sequence was then compared to its corresponding measured EPID cine-image sequence on a frame-by-frame basis. The EPID was mounted on a Clinac 2100ix linac (Varian Medical Systems, Palo Alto, CA). The field size was set to 21.4  ×  28.6 cm2 with no MLC modulation, beam energy of 6 MV, dose rate of 600 MU/min, and 700 MU were delivered for each clockwise (CW) and counter-clockwise (CCW) arc. No phantoms were placed in the beam. RESULTS: The dose rate ramp up effect was observed at the beginning irradiations, and the identification and timing of the radiation pulses, even during the dose rate ramp up, were able to be quantified using the TARG-I and forward power signals. The approach of capturing individual dose pulses and synchronizing with the mask image applied to the original predicted EPID image sequence was demonstrated to model the actual EPID readout. The MBP-EPID image sequences closely reproduced the location and magnitude of the banding features observed in the acquired (i.e., measured) image sequence, for all test irradiations examined here. CONCLUSIONS: The banding artifacts observed in the measured EPID cine-frame sequences were reproduced in the predicted EPID cine-frames by simulating the discrete temporal-spatial nature of the EPID read out. The MBP-EPID images showed good agreement qualitatively to the corresponding measured EPID frame sequence of a simple square test field, without any phantom in the beam. This approach will lead to improved image comparison tolerances for real-time patient dosimetry applications.

Modeling the time dependent biodistribution of Samarium-153 ethylenediamine tetramethylene phosphonate using compartmental analysis.

AIM: The main purpose of this work was to develop a pharmacokinetic model for the bone pain palliation agent Samarium-153 ethylenediamine tetramethylene phosphonate ([(153)Sm]-EDTMP) in normal rats to analyze the behavior of the complex. BACKGROUND: The use of compartmental analysis allows a mathematical separation of tissues and organs to determine the concentration of activity in each fraction of interest. Biodistribution studies are expensive and difficult to carry out in humans, but such data can be obtained easily in rodents. MATERIALS AND METHODS: We have developed a physiologically based pharmacokinetic model for scaling up activity concentration in each organ versus time. The mathematical model uses physiological parameters including organ volumes, blood flow rates, and vascular permabilities; the compartments (organs) are connected anatomically. This allows the use of scale-up techniques to predict new complex distribution in humans in each organ. RESULTS: The concentration of the radiopharmaceutical in various organs was measured at different times. The temporal behavior of biodistribution of (153)Sm-EDTMP was modeled and drawn as a function of time. CONCLUSIONS: The variation of pharmaceutical concentration in all organs is described with summation of 6-10 exponential terms and it approximates our experimental data with precision better than 2%.