A hybrid blood input function (BIF) model that incorporates region of interests (ROIs) based peak estimation and a two
exponential tail model was proposed to describe the blood input function. The hybrid BIF model was applied to the
single-input-multiple-output (SIMO) optimization based approach for BIF estimation using time activity curves (TACs)
obtained from ROIs defined at left ventricle (LV) blood pool and myocardium regions of dynamic PET images. The
proposed BIF estimation method was applied with 0, 1 and 2 blood samples as constraints for BIF estimation using
simulated small animal PET data. Relative percentage difference of the area-under-curve (AUC) measurement between
the estimated BIF and the true BIF was calculated to evaluate the BIF estimation accuracy. SIMO based BIF estimation
using Feng's input function model was also applied for comparison. The hybrid method provided improved BIF
estimation in terms of both mean accuracy and variability compared to Feng's model based BIF estimation in our
simulation study. When two blood samples were used as constraints, the percentage BIF estimation error was 0.82 ±
4.32% for the hybrid approach and 4.63 ± 10.67% for the Feng's model based approach. Using hybrid BIF, improved
kinetic parameter estimation was also obtained.
An extensive simulation study was performed to examine different point-to-surface registration techniques for intraoperative registration of preoperative patient data to points collected with electrophysiologic anatomy mapping systems. Three point-to-surface registration methods were evaluated using simulated points sampled from a preoperative heart model. Downhill Simplex (DS) based method outperformed the Iterative Closest Point (ICP) method and a chamfer transform based method. One hundred simulations were performed under a variety of noise and sampling conditions. Less than four pixels root mean squared distance (RMSD) error was observed when there was a 2-pixel standard deviation Gaussian noise in the point cloud coordinates. This registration error was mainly due to the added noise in the sampled points. A near optimal registration can be achieved when 50 or more points randomly sampled on the surface are used as point samples. Reasonable registration can be achieved when 25 points are used. A motion-compensating approach to registration was evaluated in order to account for the different transformation that each anatomical structure may undergo during the procedure due to respiratory motion and other factors. A piecewise registration method, which registers different anatomical structure independently, was evaluated, and favorable results were obtained as compared to a global registration approach. Further validation is in progress to evaluate the piecewise registration using realistic dynamic phantoms and in vivo animal studies.
Several approaches for registering a subset of imaged points to their true origins were analyzed and compared for seed based TRUS-fluoroscopy registration. The methods include the Downhill Simplex method (DS), the Powell's method (POW), the Iterative Closest Point (ICP) method, the Robust Point Matching method (RPM) and variants of RPM. Several modifications were made to the standard RPM method to improve its performance. One hundred simulations were performed for each combination of noise level, seed detection rate and spurious points and the registration accuracy was evaluated and compared. The noise level ranges from 0 to 5mm, the seed detection ratio ranges from 0.2 to 0.6, and the number of spurious points ranges from 0 to 20. An actual clinical post-implant dataset from permanent prostate brachytherapy was used for the simulation study. The experiments provided evidence that our modified RPM method is superior to other methods, especially when there are many outliers. The RPM based method produced the best results at all noise levels and seed detection rates. The DS based method performed reasonably well, especially at low noise levels without spurious points. There was no significant performance difference between the standard RPM and our modified RPM methods without spurious points. The modified RPM methods outperformed the standard RPM method with large number of spurious points. The registration error was within 2mm, even with 20 outlier points and a noise level of 3mm.
One of the problems in fluoroscopy based 3D seed reconstruction for prostate brachytherapy is the clustering of seeds in the fluoroscopic images. A template matching based method is proposed in this study to reconstruct the orientations and locations of individual seed images in the cluster. An idealized projection image of implanted seeds was used as a template to reconstruct the cluster, and different optimization strategies were implemented to find the best orientation and location for individual seeds. The four search methods compared were: 1) Down hill simplex method; 2) Powell method; 3) Multi-resolution based method with exhaustive initial search; and 4) Multi-resolution based method without exhaustive initial search. These methods were applied to 10 test images. Five of the ten images had only 2-seed clusters and five had 3-seed clusters. The results demonstrate that the first two methods didn’t perform well, and that the results were dependent on the initial guesses used to start the optimization process. The third method successfully found the best configurations for all of the 10 images while the fourth method succeeded in 9 of the 10 cases. We conclude therefore that multi-resolution approaches are appropriate for the seed image reconstruction problem. Since the possible configurations of the template are pre-computed, an additional advantage is that less execution time was needed for the multi-resolution methods. When applied to the seed image reconstruction process, this method will potentially significantly improve the accuracy of the 3D reconstruction of implanted seeds from fluoroscopic images used in prostate brachytherapy.
This study compared the performance of four different object size estimation methods using numerically created experimental images with features relevant to the practice of prostate brachytherapy. The four methods are: 1) pixel count of objects in the segmented binary mask; 2) half peak thresholding based on the previously created binary mask from method 1 and the original image; 3) gray scale averaging of pixels in the binary mask and the surrounding area; and 4) a point spread function corrected version of method 3. The first method demonstrated a 16% error in object size estimation while the other three methods exhibited average errors near 4%. Methods 3 and 4 gave a more consistent estimation of the size for different image contrasts. The first three methods were also applied on three fluoroscopic images of a prostate phantom with 64 implanted seeds. In combination with a classification algorithm, the seed image number and location were determined. Again, method 3 showed superior performance, because it correctly identified 64 seeds in 2 of 3 fluoroscopic images, while 4 false positives appeared in one of the three. This study confirms that effects of partial volume on the size estimation can be compensated by using a gray scale averaging technique. When applied to the seed image identification process, it will improve the accuracy of seed image detection.
A method using implanted seeds as fiducials to register ultrasound (US) images with fluoroscopic images for prostate
brachytherapy dose analysis is proposed. In a simulation study, transformed point clouds with 154 points were sampled
at different sampling rates with different levels of noise applied and then registered with the original imaging data.
Superior performance in comparison to conventional four point fiducial registration was demonstrated. The root-meansquared-
distance at registration was 0.962mm when 25% of the points were used as fiducials and with noise level at
3mm. A phantom with 64 implanted seeds was scanned by CT at 1.5mm intervals and by step-section US at 2.5mm
intervals. Fluoroscopic images of the phantom were also taken at several different projection angles. Coordinates of
implanted seeds were determined for each imaging modality. CT-US and fluoroscopy-US registration were then carried
out using the implanted seeds as fiducials. Over 90% overlap between the segmented CT prostate volume and US
prostate volume was observed at registration, and the distance between the centers of the registered volumes was 3mm.
The mean distance between the seed coordinates at registration was 2.5mm for CT and US, and 3mm for fluoroscopy
and US. These results suggest that registration of fluoroscopic images with US images of the prostate can be effectively
accomplished by using implanted seeds as fiducials. Consequently, accurate US-fluoroscopic image registration should
facilitate intraoperative radiation dosimetry for permanent prostate brachytherapy.