The correct administered activity of 18F-FCH is 0.1–0.14 mCi/Kg, which is equivalent to 3.7–5.2 MBq/kg.

The correct administered activity of 18F-FCH is 0.1-0.14 mCi/Kg, which is equivalent to 3.7-5.2 MBq/kg.

The article Digital vs. analog PET/CT: intra-subject comparison of the SUVmax in target lesions and reference regions, written by Francisco Fuentes-Ocampo, Diego Alfonso López-Mora, Albert Flotats, Gabriela Paillahueque.

Objective Normalization to an appropriate reference region in 18F-FDG PET imaging may enhance diagnostic performance in Huntington disease (HD). We aimed to identify stable brain areas that could be used to model neurometabolic degeneration in HD correlating imaging (SUVrvalues at the basal ganglia [BBGG]) and clinical parameters (disease burden score [DBS]). Materials and Methods We performed brain 18F-FDG PET/CT in 38 manifest HD patients (meanage ± SD, 54 ± 14.3 years; CAGrepeats ± SD, 44.2 ± 3.1), 20 premanifest HD patients (meanage ± SD, 42.7 ± 11.7 years; CAGrepeats ± SD, 40 ± 3.8), and 18 healthy controls (NC; meanage ± SD, 45 ± 13.2 years). For quantitative analysis, we selected (a) defined reference regions from the Montreal Neurological Institute space atlas (pons, whole cerebellum, cerebral white matter, thalamus, and a pons–cerebellar vermis region of interest), and (b) reference clusters obtained by voxelwise statistical comparison across groups (P < 0.05 FWE; extent voxel threshold k = 200). Each candidate reference region and reference cluster was quantitatively assessed using imaging and clinical parameters. Results Comparing HD and NC groups, we obtained a reference cluster in the cerebellum, and in temporal and frontal lobes. Comparing manifest HD and premanifest HD patients, we observed reference clusters in the cerebellum, pons, thalamus, parietal lobe, and cuneus. The set of reference regions showed a significant correlation between SUVrvalues at the BBGG and DBS in all HD patients. In premanifest HD patients, the correlation between SUVrvalues at the BBGG and DBS was significant using the pons–cerebellar vermis region of interest, the thalamus as defined reference regions, and the pons and thalamus as reference clusters. In manifest HD patients, the correlation was significant using the temporal and white matter frontal lobe clusters. Variance between SUVrvalues in the set of reference regions and reference clusters was minimal within NC. Conclusions The pons may be a stable and reliable region to calculate SUVrvalues to model the neurometabolic degeneration in quantitative 18F-FDG PET imaging in HD.

The purpose of this study was to compare image quality and lesion detection capability between a digital and an analog PET/CT system in oncological patients.

OBJECTIVES:The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.MATERIALS AND METHODS:From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.RESULTS:A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).CONCLUSIONS:The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.

Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level.

OBJECTIVES:The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin's lymphoma (NHL) progression or response severity. MATERIALS AND METHODS:A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis. RESULTS:The set of proposed indicators achieved Pearson's correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUV mean, SUV max, SUV peak), and total lesion glycolysis] by more than 40%. CONCLUSION:Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios.