Purpose: The aim of this study is to optimize different parameters in the time-of-flight (TOF) reconstruction for the Philips GEMINI TF. The use of TOF in iterative reconstruction introduces additional variables to be optimized compared to conventional PET reconstruction. The different parameters studied are the TOF kernel width, the kernel truncation (used to reduce reconstruction time) and the scatter correction method.
Methods: These parameters are optimized using measured phantom studies. All phantom studies were acquired with a very high number of counts to limit the effects of noise. A high number of iterations (33 subsets and 3 iterations) was used to reach convergence. The figures of merit are the uniformity in the background, the cold spot recovery and the hot spot contrast. As reference results we used the non-TOF reconstruction of the same data sets.
Results: It is shown that contrast recovery loss can only be avoided if the kernel is extended to more than 3 standard deviations. To obtain uniform reconstructions the recommended scatter correction is TOF single scatter simulation (SSS). This also leads to improved cold spot recovery and hot spot contrast. While the daily measurements of the system show a timing resolution in the range of 590–600 ps, the optimal reconstructions are obtained with a TOF kernel full-width at half-maximum (FWHM) of 650–700 ps. The optimal kernel width seems to be less critical for the recovered contrast but has an important effect on the background uniformity. Using smaller or wider kernels results in a less uniform background and reduced hot and cold contrast recovery.
Conclusion: The different parameters studied have a large effect on the quantitative accuracy of the reconstructed images. The optimal settings from this study can be used as a guideline to make an objective comparison of the gains obtained with TOF PET versus PET reconstruction.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00259-009-1164-3 | DOI Listing |
J Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFJ Alzheimers Dis
January 2025
Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.
Background: The introduction of therapeutics for Alzheimer's disease has led to increased interest in precisely quantifying amyloid-β (Aβ) burden for diagnosis, treatment monitoring, and further clinical research. Recent positron emission tomography (PET) hardware innovations including digital detectors have led to superior resolution and sensitivity, improving quantitative accuracy. However, the effect of PET scanner on Centiloid remains relatively unexplored and is assumed to be minimized by harmonizing PET resolutions.
View Article and Find Full Text PDFConf Proc Int Conf Image Form Xray Comput Tomogr
August 2024
Department of Radiology, Perelman School of Medicine, Philadelphia, PA, USA.
Respiratory motion phantoms can be used for evaluation of CT imaging technologies such as motion artifact reduction algorithms and deformable image registration. However, current respiratory motion phantoms do not exhibit detailed lung tissue structures and thus do not provide a realistic testing environment. This paper presents PixelPrint, a method for 3D-printing deformable lung phantoms featuring highly realistic internal structures, suitable for a broad range of CT evaluations, optimizations, and research.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Neuro-Electronics Research Flanders, Kapeldreef 75, Leuven, 3001, Belgium.
The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.).
Rationale And Objectives: To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences.
Materials And Methods: Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!