Selective Internal Radiation Therapy (SIRT) is a common way to treat liver cancer that cannot be treated surgically. SIRT involves administration of Yttrium – 90 (<sup>90</sup>Y) microspheres via the hepatic artery after a diagnostic procedure using <sup>99m</sup>Technetium (Tc)-macroaggregated albumin (MAA) to detect extrahepatic shunting to the lung or the gastrointestinal tract. Accurate quantification of radionuclide administered to patients and radiation dose absorbed by different organs is of importance in SIRT. Accurate dosimetry for SIRT allows optimization of dose delivery to the target tumor and may allow for the ability to assess the efficacy of the treatment. In this study, we proposed a method that can efficiently estimate radiation absorbed dose from <sup>90</sup>Y bremsstrahlung SPECT/CT images of liver and the surrounding organs. Bremsstrahlung radiation from <sup>90</sup>Y was simulated using the Compton window of <sup>99m</sup>Tc (78keV at 57%). <sup>99m</sup>Tc images acquired at the photopeak energy window were used as a standard to examine the accuracy of dosimetry prediction by the simulated bremsstrahlung images. A Liqui-Phil abdominal phantom with liver, stomach and two tumor inserts was imaged using a Philips SPECT/CT scanner. The Dose Point Kernel convolution method was used to find the radiation absorbed dose at a voxel level for a three dimensional dose distribution. This method will allow for a complete estimate of the distribution of radiation absorbed dose by tumors, liver, stomach and other surrounding organs at the voxel level. The method provides a quantitative predictive method for SIRT treatment outcome and administered dose response for patients who undergo the treatment.
Purpose: To develop and validate an automatic algorithm for the detection and functional assessment of lung tumors on three-dimensional respiratory gated PET/CT images. Method and Materials: First the algorithm will automatically segment lung regions in CT images, then identify and localize focal increases of activity in lung regions of PET images at each gated bin. Once the tumor voxels have been determined, an integration algorithm will include all the tumor counts collected at different bins within the respiratory cycle into one reference bin. Then the total activity (Bq), concentration (Bq/ml), functional volume (ml) and standard uptake values (SUV) are calculated for each tumor on PET images. Validation of the automatic algorithm was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System at Baptist Hospital of Miami. Tumor variables to be controlled were: volume, total number of counts (activity), maximum and average number of counts. These values were the gold standard to which the results of the algorithm were compared. The tumor's motion was also controlled with different respiratory periods and amplitudes. Results: Validation, feasibility and robustness of the algorithm were demonstrated. With the algorithm, the best compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become faster and more precise.
Lung cancer is the cause of more than 150,000 deaths annually in the United States. Early and accurate detection of lung
tumors with Positron Emission Tomography has enhanced lung tumor diagnosis. However, respiratory motion during the
imaging period of PET results in the reduction of accuracy of detection due to blurring of the images. Chest motion can
serve as a surrogate for tracking the motion of the tumor. For tracking chest motion, an optical laser system was designed
which tracks the motion of a patterned card placed on the chest by illuminating the pattern with two structured light
sources, generating 8 positional markers. The position of markers is used to determine the vertical, translational, and
rotational motion of the card. Information from the markers is used to decide whether the patient's breath is abnormal
compared to their normal breathing pattern. The system is developed with an inexpensive web-camera and two low-cost
laser pointers. The experiments were carried out using a dynamic phantom developed in-house, to simulate chest
movement with different amplitudes and breathing periods. Motion of the phantom was tracked by the system developed
and also by a pressure transducer for comparison. The studies showed a correlation of 96.6% between the respiratory
tracking waveforms by the two systems, demonstrating the capability of the system. Unlike the pressure transducer
method, the new system tracks motion in 3 dimensions. The developed system also demonstrates the ability to track a
sliding motion of the patient in the direction parallel to the bed and provides the potential to stop the PET scan in case of
In this study we present an automatic algorithm for the detection and functional assessment of lung nodules on three-dimensional slices derived from a hybrid PET/CT scanner. In addition to differentiate malignant from benign lesions, the algorithm was mainly designed for assessing the response of lung cancer to therapy. The automated algorithm involves three major steps. First, the lung region is segmented from low resolution multislice CT images. Once the lung is segmented on CT images, a search of seed pixels with maximum activity of <sup>18</sup>FDG is undertaken into the lung regions of the electronically registered PET images. A 3D growing algorithm identified the lesion pixels around the maximum <sup>18</sup>FDG activity seed pixels. In the third step, the total activity (Bq), concentration (Bq/ml), metabolically active volume (ml) and standard uptake values (SUV) were calculated for lesions on PET images. A threshold and filtering method was applied to high resolution CT scans to determine the CT volume of these lesions identified on PET images. All PET images were corrected for attenuation and partial volume effect and cross calibrated with a standard activity measured in a dose calibrator. Studies were performed using a hybrid PET/CT Discovery LS (GE Medical Systems). The feasibility and robustness of the automatic algorithm was demonstrated in studies with a lung-chest phantom and by retrospective analysis of clinical studies.