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Textural analysis might give new insights into the quantitative characterization of metabolically active tumors. More than thirty textural parameters have been investigated in former F18-FDG studies already. The purpose of the paper is to declare basic requirements as a selection strategy to identify the most appropriate heterogeneity parameters to measure textural features. Our predefined requirements were: a reliable heterogeneity parameter has to be volume independent, reproducible, and suitable for expressing quantitatively the degree of heterogeneity. Based on this criteria, we compared various suggested measures of homogeneity. A homogeneous cylindrical phantom was measured on three different PET/CT scanners using the commonly used protocol. In addition, a custom-made inhomogeneous tumor insert placed into the NEMA image quality phantom was imaged with a set of acquisition times and several different reconstruction protocols. PET data of 65 patients with proven lung lesions were retrospectively analyzed as well. Four heterogeneity parameters out of 27 were found as the most attractive ones to characterize the textural properties of metabolically active tumors in FDG PET images. These four parameters included Entropy, Contrast, Correlation, and Coefficient of Variation. These parameters were independent of delineated tumor volume (bigger than 25–30 ml), provided reproducible values (relative standard deviation< 10%), and showed high sensitivity to changes in heterogeneity. Phantom measurements are a viable way to test the reliability of heterogeneity parameters that would be of interest to nuclear imaging clinicians.

ObjectivesIntra-individual spatial overlap analysis of tumor volumes assessed by MRI, the amino acid PET tracer [18F]-FET and the nucleoside PET tracer [18F]-FLT in high-grade gliomas (HGG).MethodsMRI, [18F]-FET and [18F]-FLT PET data sets were retrospectively analyzed in 23 HGG patients. Morphologic tumor volumes on MRI (post-contrast T1 (cT1) and T2 images) were calculated using a semi-automatic image segmentation method. Metabolic tumor volumes for [18F]-FET and [18F]-FLT PETs were determined by image segmentation using a threshold-based volume of interest analysis. After co-registration with MRI the morphologic and metabolic tumor volumes were compared on an intra-individual basis in order to estimate spatial overlaps using the Spearman's rank correlation coefficient and the Mann-Whitney U test.Results[18F]-FLT uptake was negative in tumors with no or only moderate contrast enhancement on MRI, detecting only 21 of 23 (91%) HGG. In addition, [18F]-FLT uptake was mainly restricted to cT1 tumor areas on MRI and [18F]-FLT volumes strongly correlated with cT1 volumes (r = 0.841, p<0.001). In contrast, [18F]-FET PET detected 22 of 23 (96%) HGG. [18F]-FET uptake beyond areas of cT1 was found in 61% of cases and [18F]-FET volumes showed only a moderate correlation with cT1 volumes (r = 0.573, p<0.001). Metabolic tumor volumes beyond cT1 tumor areas were significantly larger for [18F]-FET compared to [18F]-FLT tracer uptake (8.3 vs. 2.7 cm3, p<0.001).ConclusionIn HGG [18F]-FET but not [18F]-FLT PET was able to detect metabolic active tumor tissue beyond contrast enhancing tumor on MRI. In contrast to [18F]-FET, blood-brain barrier breakdown seems to be a prerequisite for [18F]-FLT tracer uptake.

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The aim of this study was to assess the reproducibility of standard, Dixon-based attenuation correction (MR-AC) in PET/MR imaging. A further aim was to estimate a patient-specific lean body mass (LBM) from these MR-AC data. Methods: Ten subjects were positioned in a fully integrated PET/MR system, and 3 consecutive multibed acquisitions of the standard MR-AC image data were acquired. For each subject and MR-AC map, the following compartmental volumes were calculated: total body, soft tissue (ST), fat, lung, and intermediate tissue (IT). Intrasubject differences in the total body and subcompartmental volumes (ST, fat, lung, and IT) were assessed by means of coefficients of variation (CVs) calculated across the 3 consecutive measurements and, again, across these measurements but excluding those affected by major artifacts. All subjects underwent a body composition measurement using air displacement plethysmography (ADP) that was used to calculate a reference LBM ADP . A second LBM estimate was derived from available MR-AC data using a formula incorporating the respective tissue volumes and densities as well as the subject-specific body weights. A third LBM estimate was obtained from a sex-specific formula (LBM Formula ). Pearson correlation was calculated for LBM ADP , LBM MR-AC , and LBM Formula . Further, linear regression analysis was performed on LBM MR-AC and LBM ADP. Results: The mean CV for all 30 scans was 2.1 ± 1.9% (TB). When missing tissue artifacts were excluded, the CV was reduced to 0.3 ± 0.2%. The mean CVs for the subcompartments before and after exclusion of artifacts were 0.9 ± 1.1% and 0.7 ± 0.7% for the ST, 2.9 ± 4.1% and 1.3 ± 1.0% for fat, and 3.6 ± 3.9% and 1.3 ± 0.7% for the IT, respectively. Correlation was highest for LBM MR-AC and LBM ADP (r 5 0.99). Linear regression of data excluding artifacts resulted in a scaling factor of 1.06 for LBM MR-AC . Conclusion: LBM MR-AC is shown to correlate well with standard LBM measurements and thus offers routine LBM-based SUV quantification in PET/MR. However, MR-AC images must be controlled for systematic artifacts, including missing tissue and tissue swaps. Efforts to minimize these artifacts could help improve the reproducibility of MR-AC. Dual -modality PET/CT imaging systems, combining CT with PET into a single imaging system, have become standard for acquiring colocalized metabolic and anatomic information in clinical practice (1). The effectiveness of PET/CT is due, in part, to the complementary role of CT in providing both an anatomic framework and reliable, quantitative attenuation values for tissues inside the field of view of the PET for CT-based attenuation correction (2). After the introduction of dual-modality MR and PET imaging (PET/MR), novel clinical applications of hybrid imaging are being actively developed (3). PET/MR imaging has the advantage of offering superior soft-tissue (ST) contrast and a wide array of functional, morphologic, and even metabolic clinical imaging protocols (4) within a single examination. However, correct att...

Objective: Joint space width (JSW) has been the gold standard to assess loss of cartilage in knee osteoarthritis (OA). Here we describe a novel quantitative measure of joint space width: standardized JSW (stdJSW). We assess the performance of this quantitative metric for JSW at tracking Osteoarthritis Research Society International (OARSI) joint space narrowing grade (JSN) changes and provide reference values for different JSN grades and their annual change. Methods: We collected 18,934 individual knee images along with JSW and JSN readings from baseline up to month 48 (4 follow-ups) from the OAI study. Standardized JSW and 12-month JSN grade changes were calculated for each knee. For each JSN grade and 12-month grade change, the distribution of JSW loss was calculated for JSW and stdJSW. Area under the ROC curves was calculated on discrimination between different JSN grades for JSW and stdJSW. Standardized response mean (SRM) was used to compare the responsiveness of the two measures to changes in JSN grade. Results: The areas under the receiver operating characteristic (ROC) curve (AUC) for stdJSW at discriminating between successive JSN grades were AUC stdJSW ¼ 0.87, 0.95, and 0.96, for JSN>0, JSN>1 and JSN>2, respectively, whereas these were AUC fJSW ¼ 0.79, 0.90, 0.98 for absolute JSW. We find that standardized JSW is significantly more responsive than absolute JSW, as measured by the SRM. Conclusions: Our results show that stdJSW outperforms absolute JSW at discriminating and tracking changes in JSN and further that this effect is in part because stdJSW cancels JSW variations attributed to patient height variations.

Quantifying tumour heterogeneity from [18F]FDG-PET images promises benefits for treatment selection of cancer patients.Here, the calculation of texture parameters mandates an initial discretization step (binning) to reduce the number of intensity

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H&E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.