Publications by authors named "D J Kadrmas"

Article Synopsis
  • New imaging technology called PSMA PET is really good at finding prostate cancer spread compared to older methods like [F]FDG PET.
  • This study used special balls to mimic cancer spots and looked at how two different ways of making pictures (OSEM and BSREM) affected how accurately we could see and measure those spots.
  • The new "gradient-based segmentation" method was found to be better than the standard 40% method for measuring tumor sizes and activity.*
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Purpose: Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs.

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Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important.

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Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92kg patient.

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Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers.

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