Fayçal Ben Bouallègue

The aim of this study was to compare predictive and post-treatment dosimetry and analyze the differences, investigating factors related to activity preparation and delivery, imaging modality used, and interventional radiology. Twenty-three HCC patients treated by selective internal radiation therapy with 90Y glass microspheres were included in this study. Predictive and post-treatment dosimetry were calculated at the voxel level based on 99mTc-MAA SPECT/CT and 90Y-microsphere PET/CT respectively. Dose distribution was analyzed through mean dose, metrics extracted from dose-volume histograms, and Dice similarity coefficients applied on isodoses. Reproducibility of the radiological gesture and its influence on dose deviation was evaluated. 90Y delivered activity was lower than expected in 67% (16/24) of the cases mainly due to the residual activity. A mean deviation of − 6 ± 11% was observed between the delivered activity and the 90Y PET’s FOV activity. In addition, a substantial difference of − 20 ± 8% was measured on 90Y PET images between the activity in the liver and in the whole FOV. After normalization, 99mTc-MAA SPECT dosimetry was highly correlated and concordant with 90Y-microsphere PET dosimetry for all dose metrics evaluated (ρ = 0.87, ρc = 0.86, P = 3.10−8 and ρ = 0.91, ρc = 0.90, P = 7.10−10 for tumor and normal liver mean dose respectively for example). Besides, mean tumor dose deviation was lower when the catheter position was identical than when it differed (16 Gy vs. 37 Gy, P = 0.007). Concordance between predictive and post-treatment dosimetry, evaluated with Dice similarity coefficients applied on isodoses, significantly correlated with the distance of the catheter position from artery bifurcation (P = 0.04, 0.0004, and 0.05, for 50 Gy, 100 Gy, and 150 Gy isodoses respectively). Discrepancies between planned activity and activity measured on 90Y PET images were observed and seemed to be mainly related to clinical hazards and equipment issues. Predictive vs. post-treatment comparison of relative dose distributions between tumor and normal liver showed a good correlation and no significant difference highlighting the predictive value of 99mTc MAA SPECT/CT-based dosimetry. Besides, the reproducibility of catheter tip position appears critical in the agreement between predictive and actual dose distribution.

Baseline ¹⁸F-FDG PET and MRI were performed in a patient with IgG4-related hypophysitis, showing a 15-mm hypervascular hypermetabolic lesion with sellar and suprasellar extension. Lack of response after 10 months of first-line corticosteroid therapy was demonstrated on both ¹⁸F-FDG PET and MRI. Three months later, after 2 injections of 1 g of rituximab associated with continued corticosteroid therapy, MRI showed substantial shrinkage of the pituitary lesion with minimal residual Gd enhancement, whereas ¹⁸F-FDG PET evidenced complete metabolic response. As such, joint ¹⁸F-FDG PET and MRI assessment during therapy may have a potential interest for treatment response evaluation in pituitary IgG4-related disease.

Purpose: Dynamic ¹⁸F‐FDG PET allows quantitative estimation of cerebral glucose metabolism both at the regional and local (voxel) level. Although sensitive to noise and highly computationally expensive, nonlinear least‐squares (NLS) optimization stands as the reference approach for the estimation of the kinetic model parameters. Nevertheless, faster techniques, including linear least‐squares (LLS) and Patlak graphical method, have been proposed to deal with high resolution noisy data, representing a more adaptable solution for routine clinical implementation. Former research investigating the relative performance of the available algorithms lack precise evaluation of kinetic parameter estimates under realistic acquisition conditions.Methods: The present study aims at the systematic comparison of the feasibility and pertinence of kinetic modeling of dynamic cerebral ¹⁸F‐FDG PET using NLS, LLS, and Patlak method, based on numerical simulations and patient data. Numerical simulations were used to study the bias and variance of K₁ and K_i parameters estimation under representative noise levels. Patient data allowed to assess the concordance between the three methods at the regional and voxel scale, and to evaluate the robustness of the estimations with respect to patient head motion.Results and Conclusions: Our findings indicate that at the regional level NLS and LLS provide kinetic parameter estimates (K₁ and K_i) with similar bias and variance characteristics (K₁ bias ± relative standard deviation [RSD] 0.0 ± 5.1% and 0.1% ± 4.9% for NLS and LLS respectively, K_i bias ± RSD 0.1% ± 4.5% and −0.7% ± 4.4% for NLS and LLS respectively). NLS estimates appear, however, to be slightly less sensitive to patient motion. At the voxel level, provided that patient motion is negligible or corrected, LLS offers an appealing alternative solution for local K₁ mapping. It yields K1 estimates that are highly correlated, with high correlation with NLS values (Pearson's r = 0.95 on actual data) within computations times less than two orders of magnitude lower. Last, Patlak method appears as the most robust and accurate technique for the estimation of K_i values at the regional and voxel scale, with or without head motion. It provides low bias/low variance K_i quantification (bias ± RSD −1.5 ± 9.5% and −4.1 ± 19.7% for Patlak and NLS respectively) as well as smooth parametric images suitable for visual assessment.

In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that relies on direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition.

Objective: We evaluated the relative performances of dipyridamole (Dip) and regadenoson (Reg) in a cohort of patients referred for coronary artery disease diagnosis or follow-up using myocardial perfusion imaging. Materials and Methods: We retrospectively included 515 consecutive patients referred for ^99mTc-sestamibi myocardial perfusion single-photon emission computerized tomography (SPECT) on a cadmium-zinc-telluride (CZT) camera after pharmacologic stress. About three quarters (n = 391, 76%) received Dip. Reg was administrated to patients with chronical respiratory disease or with body mass index (BMI) over 38 kg/m² (n = 124, 24%). Patients with an abnormal stress scan (92%) underwent a rest imaging on the same day. Qualitative interpretation of perfusion images was achieved using QPS software, and the ischemic area was assessed using the 17-segment model. In patients undergoing a stress-rest protocol, perfusion polar plots were postprocessed using automated in-house software to quantify the extension, intensity, and location of the reversible perfusion defect. Statistical comparison between groups was performed using univariate and multivariate analysis. Results: Qualitative analysis concluded to myocardial ischemia in 70% of the patients (69% in the Dip group, 76% in the Reg group, P = ns). In those patients, the number of involved segments (Dip 2.5 ± 1.6, Reg 2.7 ± 1.6, P = ns) and the proportion of patients with an ischemic area larger than two segments (Dip 30%, Reg 37%, P = ns) were comparable. Automated quantification of the reversible perfusion defect demonstrated similar defect extension, intensity, and severity in the two groups. Defect location was identical at the myocardial segment and vascular territory scales. Conclusions: Reg and Dip showed equal performances for ischemic burden characterization using myocardial CZT SPECT.

We investigated the feasibility of left ventricular (LV) and right ventricular (RV) volume and function estimation using a first-pass gated 15O-water PET. This prospective study included 19 patients addressed for myocardial perfusion reserve assessment using 15O-water PET. PET data were acquired at rest and after regadenoson stress, and gated first-pass images were reconstructed over the time range corresponding to tracer first-pass through the cardiac cavities and post-processed using TomPool software; LV and RV were segmented using a semi-automated 4D immersion algorithm. LV volumes were computed using a count-based model and a fixed threshold at 30% of the maximal activity. RV volumes were computed using a geometrical model and an adjustable threshold that was set so as to fit LV and RV stroke volumes. Ejection curves were fitted using a deformable reference curve model. LV results were compared to those obtained using 99mTc-sestamibi gated myocardial SPECT in terms of end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF). There was an excellent concordance between rest and stress PET in terms of EDV and ESV (Lin’s coefficient ~ 0.85–0.90), SV (~ 0.80), and EF (~ 0.75) for both ventricles. Correlation with myocardial SPECT was high for LV EDV (Pearson’s R = 0.89, p < 0.001) and ESV (R = 0.87, p < 0.001) and satisfying for LV SV (R = 0.67, p < 0.001) and EF (R = 0.67, p < 0.001). Minimal LV ESV overestimation (+ 4 mL, p = 0.03) and EF underestimation (− 4%, p = 0.01) were observed using PET. Biventricular volume and function assessment are achievable using the first-pass PET, and LV parameters correlate well with those derived from gated myocardial SPECT.

Baseline ¹⁸F-FDG PET was performed in a 74-year-old patient with relapsing upper mediastinum lymphoma. Left subclavian thrombosis was suspected on prior contrast-enhanced CT. Dynamic PET imaging was achieved during 3 min after IV injection of ¹⁸F-FDG to the left arm in order to further assess left subclavian vein permeability. The 20-s dynamic frame at 1 min after injection confirmed the absence of flow in the left subclavian vein and evidenced the derivation of ¹⁸F-FDG through left axillary, then superficial, then right internal mammary collaterals to the superior vena cava, hence confirming the subclavian thrombosis.

We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer’s disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer’s Disease Neuroimaging Initiative with available baseline ¹⁸F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p < 0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary’s index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted hazard ratio for AD conversion was 5.5 for the SVM model, compared with 4.0, 2.6, and 3.8 for cerebellar, pontine and composite SUVr (all p < 0.001), indicating that appropriate amyloid textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr.