SPECT can provide valuable diagnostic and treatment response information in large-scale multicenter clinical trials. However, SPECT has been limited in providing consistent quantitative functional parametric values across the centers, largely because of a lack of standardized procedures to correct for attenuation and scatter. Recently, a novel software package has been developed to reconstruct quantitative SPECT images and assess cerebral blood flow (CBF) at rest and after acetazolamide challenge from a single SPECT session. This study was aimed at validating this technique at different institutions with a variety of SPECT devices and imaging protocols. Methods: Twelve participating institutions obtained a series of SPECT scans on physical phantoms and clinical patients. The phantom experiments included the assessment of septal penetration for each collimator used and of the accuracy of the reconstructed images. Clinical studies were divided into 3 protocols, including intrainstitutional reproducibility, a comparison with PET, and rest-rest study consistency. The results from 46 successful studies were analyzed. Results: Activity concentration estimation (Bq/mL) in the reconstructed SPECT images of a uniform cylindric phantom showed an interinstitution variation of 65.1%, with a systematic underestimation of concentration by 12.5%. CBF values were reproducible both at rest and after acetazolamide on the basis of repeated studies in the same patient (mean 6 SD difference, 20.4 6 5.2 mL/min/100 g, n 5 44). CBF values were also consistent with those determined using PET (26.1 6 5.1 mL/min/100 g, n 5 6). Conclusion: This study demonstrates that SPECT can quantitatively provide physiologic functional images of rest and acetazolamide challenge CBF, using a quantitative reconstruction software package.Key Words: 123 I-iodoamphetamine; cerebral blood flow; acetazolamide; SPECT; vascular reactivity; quantitation J Nucl Med 2010; 51: 1624 -1631 DOI: 10.2967 Current clinical practice using SPECT relies largely on interpretation of qualitative images reflecting physiologic function. Quantitative functional parametric images may be obtained by applying mathematic modeling to SPECT data corrected for attenuation and scatter. Quantitative regional cerebral blood flow (CBF) (1-3) and cerebral vascular reactivity (CVR) in response to acetazolamide challenge (4-6) have been obtained with these techniques. One major application of such quantitative SPECT (QSPECT) approaches is the evaluation of ischemic status in patients with occlusion or stenosis in their middle cerebral arteries, to provide prognostic information of the outcome of revascularization therapies (7). Quantitative analysis in SPECT has also been demonstrated in the assessment of binding potential for several neuroreceptor ligands (8,9), for the quantitative assessment of regional myocardial perfusion (10,11), and for the assessment of radioaerosol deposition and clearance in healthy and diseased lungs (12). However, providing the standardized quantitative approach...
IntroductionA physical 3-dimensional phantom that simulates PET/SPECT images of static regional cerebral blood flow in grey matter with a realistic head contour has been developed. This study examined the feasibility of using this phantom for evaluating PET/SPECT images.MethodsThe phantom was constructed using a transparent, hydrophobic photo-curable polymer with a laser-modelling technique. The phantom was designed to contain the grey matter, the skull, and the trachea spaces filled with a radioactive solution, a bone-equivalent solution of K2HPO4, and air, respectively. The grey matter and bone compartments were designed to establish the connectivity. A series of experiments was performed to confirm the accuracy and reproducibility of the phantom using X-ray CT, SPECT, and PET.ResultsThe total weight was 1997 ± 2 g excluding the inner liquid, and volumes were 563 ± 1 and 306 ± 2 mL, corresponding to the grey matter and bone compartments, respectively. The apparent attenuation coefficient averaged over the whole brain was 0.168 ± 0.006 cm−1 for Tc-99 m, which was consistent with the previously reported value for humans (0.168 ± 0.010 cm−1). Air bubbles were well removed from both grey-matter and bone compartments, as confirmed by X-ray CT. The phantom was well adapted to experiments using PET and SPECT devices.ConclusionThe 3-dimensional brain phantom constructed in this study may be of use for evaluating the adequacy of SPECT/PET reconstruction software programs.
Positron emission tomography (PET) with 15O tracers provides essential information in patients with cerebral vascular disorders, such as cerebral blood flow (CBF), oxygen extraction fraction (OEF), and metabolic rate of oxygen (CMRO2). However, most of techniques require an additional C15O scan for compensating cerebral blood volume (CBV). We aimed to establish a technique to calculate all functional images only from a single dynamic PET scan, without losing accuracy or statistical certainties. The technique was an extension of previous dual-tracer autoradiography (DARG) approach, but based on the basis function method (DBFM), thus estimating all functional parametric images from a single session of dynamic scan acquired during the sequential administration of H215O and 15O2. Validity was tested on six monkeys by comparing global OEF by PET with those by arteriovenous blood sampling, and tested feasibility on young healthy subjects. The mean DBFM-derived global OEF was 0.57±0.06 in monkeys, in an agreement with that by the arteriovenous method (0.54±0.06). Image quality was similar and no significant differences were seen from DARG; 3.57%±6.44% and 3.84%±3.42% for CBF, and −2.79%±11.2% and −6.68%±10.5% for CMRO2. A simulation study demonstrated similar error propagation between DBFM and DARG. The DBFM method enables accurate assessment of CBF and CMRO2 without additional CBV scan within significantly shortened examination period, in clinical settings.
Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)–based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%–60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for “data-hungry” technologies, such as supervised machine learning approaches, in various clinical applications.
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