In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: 1) the reconstruction algorithms do not make full use of projection statistics; and 2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10 to 40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.
The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural databse (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them.
Purpose: This is a dosimetric study comparing stereotactic body radiotherapy (SBRT) plans of spine tumors using Brainlab Elements Spine planning module against Eclipse RapidArc plans. Dose conformity, dose gradient, dose fall-off, and patient-specific quality assurance (QA) metrics were evaluated. Methods: Twenty patients were immobilized in supine position using half Vac‐Lok. A prescription dose of 16 Gy in a single fraction was planned for Varian TrueBeam. Conformal arc plans were generated with Pencil beam (PB), MonteCarlo (MC) in Elements, and RapidArc with Acuros XB algorithm in Eclipse using identical treatment geometry. Results: Eclipse, Elements PB, and Elements MC generated dosimetrically conformal plans having Inverse Paddick Conformity Index (IPCI) <1.3. All plans satisfied the dose constraints to target and OARs. Elements PB had a sharper gradient than Elements MC with average GI of 3.67(95% CI: 3.52-3.82) and 4.06 (95% CI: 3.93-4.20) respectively. Eclipse plans were more homogeneous with mean HI= 1.22 (95% CI: 1.20-1.23) that is lower than others. Average maximum clinical target volume (CTV) doses were higher in Elements MC with 22.31Gy (95% CI: 21.87-22.74), while PB plans have 21.15Gy (95% CI: 20.36-21.96), respectively. Elements MC and PB plans had lower average dose to 0.35 cc of spinal cord (D0.35cc) of 7.60Gy (95% CI: 7.18-8.02) and 8.42Gy (95% CI: 7.83-9.01). All plans had >95% points passing the gamma QA criteria at 3%/2 mm. Conclusion: All treatment plans achieved clinically acceptable target coverage >95% and meet spinal cord dose limits. Smart optimization in Brainlab Elements spine module produced dosimetrically superior plans by better spinal cord sparing.
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