Comparison of the scanning linear estimator (SLE) and ROI methods for quantitative SPECT imaging

In quantitative emission tomography, tumor activity is typically estimated from calculations on a region of interest (ROI) identified in the reconstructed slices. In these calculations, unpredictable bias arising from the null functions of the imaging system affects ROI estimates. The magnitude of this bias depends upon the tumor size and location. In prior work it has been shown that the scanning linear estimator (SLE), which operates on the raw projection data, is an unbiased estimator of activity when the size and location of the tumor are known. In this work, we performed analytic simulation of SPECT imaging with a parallel-hole medium-energy collimator. Distance-dependent system spatial resolution and non-uniform attenuation were included in the imaging simulation. We compared the task of activity estimation by the ROI and SLE methods for a range of tumor sizes (diameter: 1-3 cm) and activities (contrast ratio: 1-10) added to uniform and non-uniform liver backgrounds. Using the correct value for the tumor shape and location is an idealized approximation to how task estimation would occur clinically. Thus we determined how perturbing this idealized prior knowledge impacted the performance of both techniques. To implement the SLE for the non-uniform background, we used a novel iterative algorithm for pre-whitening stationary noise within a compact region. Estimation task performance was compared using the ensemble mean-squared error (EMSE) as the criterion. The SLE method performed substantially better than the ROI method (i.e. EMSE(SLE) was 23-174 times lower) when the background is uniform and tumor location and size are known accurately. The variance of the SLE increased when a non-uniform liver texture was introduced but the EMSE(SLE) continued to be 5-20 times lower than the ROI method. In summary, SLE outperformed ROI under almost all conditions that we tested

Performing tomographic reconstructions from a satellite looking toward Earth. Part 1: implementation and limitations

For imaging instruments that are in space looking toward the Earth, there are a variety of nuisance signals that can get in the way of performing certain imaging tasks, such as reflections from clouds, reflections from the ground, and emissions from the OH-airglow layer. A method for separating these signals is to perform tomographic reconstructions from the collected data. A lingering struggle for this method is altitude-axis resolution and different methods for helping with it are discussed. An implementation of the maximum likelihood expectation maximization algorithm is given and analyzed.Space Dynamics Laboratory12 month embargo; published online: 02 May 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Performing tomographic reconstructions from a satellite looking toward Earth. Part 2: analysis of image quality

This paper is part 2 of two papers that explore performing tomographic reconstructions from a space platform. A simplified model of short-wave infrared emissions in the atmosphere is given. Simulations were performed that tested the effectiveness of reconstructions given signal amplitude, frequency, signal-to-noise ratio, number of iterations run, and others. Maximum likelihood expectation maximization is shown to be effective for reconstructing low signal cases.Space Dynamics Laboratory12 month embargo; published online: 15 June 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Aperture size selection for improved brain tumor detection and quantification in multi-pinhole \ub9\ub2\ub3I-CLINDE SPECT imaging

Abstract: A next-generation multi-pinhole system dedicated to brain SPECT imaging is being constructed by our research team, which we call AdaptiSPECT-C. The system prototype used herein consists of 25 square detector modules and a total of 100 apertures grouped by 4 per module. The system is specifically designed for multi-purpose brain imaging and capable of adapting in real-time each aperture size and whether it is open or shuttered closed. The use of such system would provide optimum high-performance patient-personalized imaging for a wide range of brain imaging tasks. In this work we investigated the effect of pinhole diameter variation on spherical tumor quantification for the promising brain tumor imaging agent 123 I-CLINDE. To assess the quality of the images reconstructed for the different aperture sizes, we used a customized multiple-sphere tumor phantom derived from the XCAT software with a tumor size of 1 cm in diameter. Our results suggest through quantification and visual inspection that an aperture diameter in the range of 2 to 5 mm in diameter for the adaptive AdaptiSPECT-C system is likely the most suited for high performance brain tumor 123I-CLINDE imaging. In addition, our study concludes that a 4 mm pinhole diameter given its excellent spatial-resolution-to-sensitivity trade-off is promising for scout acquisition in localizing target tumor regions within the brain. We have initiated a task-based performance on the tumor detection and localization accuracy for a range of simulated tumor sizes using the channelized non-pre-whitening (CNPW) matched-filter scanning-observer

Optimization of duplex velocity criteria for diagnosis of internal carotid artery (ICA) stenosis: A report of the Intersocietal Accreditation Commission (IAC) Vascular Testing Division Carotid Diagnostic Criteria Committee

Diagnostic criteria to classify severity of internal carotid artery (ICA) stenosis vary across vascular laboratories. Consensus-based criteria, proposed by the Society of Radiologists in Ultrasound in 2003 (SRUCC), have been broadly implemented but have not been adequately validated. We conducted a multicentered, retrospective correlative imaging study of duplex ultrasound versus catheter angiography for evaluation of severity of ICA stenosis. Velocity data were abstracted from bilateral duplex studies performed between 1/1/2009 and 12/31/2015 and studies were interpreted using SRUCC. Percentage ICA stenosis was determined using North American Symptomatic Carotid Endarterectomy Trial (NASCET) methodology. Receiver operating characteristic analysis evaluated the performance of SRUCC parameters compared with angiography. Of 448 ICA sides (from 224 patients), 299 ICA sides (from 167 patients) were included. Agreement between duplex ultrasound and angiography was moderate (κ = 0.42), with overestimation of degree of stenosis for both moderate (50–69%) and severe (⩾ 70%) ICA lesions. The primary SRUCC parameter for ⩾ 50% ICA stenosis of peak-systolic velocity (PSV) of ⩾ 125 cm/sec did not meet prespecified thresholds for adequate sensitivity, specificity, and accuracy (sensitivity 97.8%, specificity 64.2%, accuracy 74.5%). Test performance was improved by raising the PSV threshold to ⩾ 180 cm/sec (sensitivity 93.3%, specificity 81.6%, accuracy 85.2%) or by adding the additional parameter of ICA/common carotid artery (CCA) PSV ratio ⩾ 2.0 (sensitivity 94.3%, specificity 84.3%, accuracy 87.4%). For ⩾ 70% ICA stenosis, analysis was limited by a low number of cases with angiographically severe disease. Interpretation of carotid duplex examinations using SRUCC resulted in significant overestimation of severity of ICA stenosis when compared with angiography; raising the PSV threshold for ⩾ 50% ICA stenosis to ⩾ 180 cm/sec as a single parameter or requiring the ICA/CCA PSV ratio ⩾ 2.0 in addition to PSV of ⩾ 125 cm/sec for laboratories using the SRUCC is recommended to improve the accuracy of carotid duplex examinations