Simulated and experimental phantom data for multi-center quality assurance of quantitative susceptibility maps at 3 T, 7 T and 9.4 T.

PURPOSE: To develop methods for quality assurance of quantitative susceptibility mapping (QSM) using MRI at different magnetic field strengths, and scanners, using different MR-sequence protocols, and post-processing pipelines. METHODS: We built a custom phantom based on iron in two forms: homogeneous susceptibility ('free iron') and with fine-scaled variations in susceptibility ('clustered iron') at different iron concentrations. The phantom was measured at 3.0 T (two scanners), 7.0 T and 9.4 T using multi-echo, gradient echo acquisition sequences. A digital phantom analogue to the iron-phantom, tailored to obtain similar results as in experimentation was developed, with similar geometry and susceptibility values. Morphology enabled dipole inversion was applied to the phase images to obtain QSM for experimental and simulated data using the MEDI + 0 approach for background regularization. RESULTS: Across all scanners, QSM-values showed a linear increase with iron concentrations. The QSM-relaxivity was 0.231 ± 0.047 ppm/mM for free and 0.054 ± 0.013 ppm/mM for clustered iron, with adjusted determination coefficients (DoC) ≥ 0.87. Similarly, the simulations yielded linear increases (DoC ≥ 0.99). In both the experimental and digital phantoms, the estimated molar susceptibility was lower with clustered iron, because clustering led to highly localized field effects. CONCLUSION: Our iron phantom can be used to evaluate the capability of QSM to detect local variations in susceptibility across different field strengths, when using different MR-sequence protocols. The devised simulation method captures the effect of iron clustering in QSM as seen experimentally and could be used in the future to optimize QSM processing pipelines and achieve higher accuracy for local field effects, as also seen in Alzheimer's beta-amyloid plaques.

Prognostic value of imaging markers from 18FDG-PET/CT in paediatric patients with Hodgkin lymphoma.

OBJECTIVE: Identification of imaging prognostic parameters for early therapy personalisation to reduce treatment-related morbidity in paediatric Hodgkin lymphoma (HL). Our aim was to evaluate quantitative markers from baseline 2-[18F]fluoro-2-deoxy-d-glucose PET/CT as prognostic factors for treatment outcomes. Another goal was assessing the prognostic value of Deauville score at interim PET/CT. METHODS: Twenty-one patients were prospectively enrolled. Median age was 12 years (range 6-17); 13 were female. Patients underwent PET/CT for disease staging (bPET), at the end of two cycles of chemotherapy (iPET) and after chemotherapy. A total of 173 lesions were segmented from bPET. We calculated 51 texture features for each lesion. Total metabolic tumour volume and total lesion glycolysis from bPET were calculated for response prediction at iPET. Univariate and multivariate analyses were used for optimal cut-off values to separate responders at iPET according to the Deauville score. RESULTS: We identified four texture features as possible independent predictors of treatment outcomes at iPET. The areas under the ROC for univariate analysis were 0.89 (95% CI, 0.75-1), 0.82 (95% CI, 0.64-1), 0.79 (95% CI, 0.59-0.99) and 0.89 (95% CI, 0.75-1). The survival curves for patients assigned Deauville scores 1, 2, 3 and X were different from those assigned a score 4, with 4-year progression free-survival (PFS) rates of 85 versus 29%, respectively (P = 0.05). CONCLUSIONS: We found four textural features as candidates for predicting early response to chemotherapy in paediatric patients with HL. The Deauville score at iPET was useful for differentiating PFS rates.

Quantitative comparison between single-photon emission computed tomography and positron emission tomography imaging of lung ventilation with 99mTc-technegas and 68Ga-gallgas in patients with chronic obstructive pulmonary disease: A pilot study.

The aim of this study was quantitative comparison between 68Ga-Gallgas positron emission tomography (PET) and 99mTc-Technegas single photon emission computed tomography (SPECT) for lung ventilation function assessment in patients with moderate-to-severe obstructive pulmonary disease and to identify image-derived texture features correlating to the physiologic parameters. Five patients with moderate-to-severe chronic obstructive pulmonary disease with PET and SPECT lung ventilation scans were selected for this study. Threshold-based segmentations were used to compare ventilated regions between both imaging techniques. Histograms of both scans were compared to reveal main differences in distributions of radiotracers. Volumes of segmentation as well as 50 textural features measured in the pulmonary region were correlated to the forced expiratory volume in 1 s (FEV1) as the relevant physiological variable. A better peripheral distribution of the radiotracer was observed in PET scans for three out of five patients. A segmentation threshold of 27% and 31% for normalized scans, for PET and SPECT respectively, was found optimal for volume correlation with FEV1. A high correlation (Pearson correlation coefficient >0.9) was found between 16 texture features measured from SPECT and 7 features measured from PET and FEV1. Quantitative measurements revealed different tracer distribution in both techniques. These results suggest that tracer distribution patterns may depend on the cause of the pulmonary obstruction. We found several texture features measured from SPECT to correlate to FEV1.

Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values.

The statistical analysis of positron emission tomography (PET) standardized uptake value (SUV) measurements is challenging due to the skewed nature of SUV distributions. This limits utilization of powerful parametric statistical models for analyzing SUV measurements. An ad-hoc approach, which is frequently used in practice, is to blindly use a log transformation, which may or may not result in normal SUV distributions. This study sought to identify optimal transformations leading to normally distributed PET SUVs extracted from tumors and assess the effects of therapy on the optimal transformations. METHODS: The optimal transformation for producing normal distributions of tumor SUVs was identified by iterating the Box-Cox transformation parameter (λ) and selecting the parameter that maximized the Shapiro-Wilk P-value. Optimal transformations were identified for tumor SUVmax distributions at both pre and post treatment. This study included 57 patients that underwent 18F-fluorodeoxyglucose (18F-FDG) PET scans (publically available dataset). In addition, to test the generality of our transformation methodology, we included analysis of 27 patients that underwent 18F-Fluorothymidine (18F-FLT) PET scans at our institution. RESULTS: After applying the optimal Box-Cox transformations, neither the pre nor the post treatment 18F-FDG SUV distributions deviated significantly from normality (P > 0.10). Similar results were found for 18F-FLT PET SUV distributions (P > 0.10). For both 18F-FDG and 18F-FLT SUV distributions, the skewness and kurtosis increased from pre to post treatment, leading to a decrease in the optimal Box-Cox transformation parameter from pre to post treatment. There were types of distributions encountered for both 18F-FDG and 18F-FLT where a log transformation was not optimal for providing normal SUV distributions. CONCLUSION: Optimization of the Box-Cox transformation, offers a solution for identifying normal SUV transformations for when the log transformation is insufficient. The log transformation is not always the appropriate transformation for producing normally distributed PET SUVs.

Nanodoublers as deep imaging markers for multi-photon microscopy.

We demonstrate the possibility to excite second-harmonic (SH) active Fe(IO(3))(3) nanocrystals with two distinct laser sources at 800 and 1550 nm, and we show, by a complementary experimental and numerical study, how the wavelength flexibility inherent to non-phase-matched SH nanoparticles can be efficiently exploited to increase imaging penetration depth of markers embedded in biological samples.