Marjan Acou

PET with 18F-labelled choline-based tracers for tumour imaging: a review of the literature

PurposeTo give an up-to-date overview of the potential clinical utility of 18F-labelled choline derivatives for tumour imaging with positron emission tomography.MethodsA PubMed search for 18F-labelled choline analogues was performed. Review articles and reference lists were used to supplement the search findings.Results18F-labelled choline analogues have been investigated as oncological PET probes for many types of cancer on the basis of enhanced cell proliferation. To date, studies have focused on the evaluation of prostate cancer. Available studies have provided preliminary results for detecting local and metastatic disease. Experience with 18F-fluorocholine PET in other tumour types, including brain and liver tumours, is still limited. In the brain, excellent discrimination between tumour and normal tissue can be achieved due to the low physiological uptake of 18F-fluorocholine. In the liver, in which there is a moderate to high degree of physiological uptake in normal tissue, malignancy discrimination may be more challenging.ConclusionPET/CT with 18F-fluorocholine can be used to detect (recurrent) local prostate cancer, but seems to have limited value for T (tumour) and N (nodal) staging. In patients presenting with recurrent biochemical prostate cancer, it is a suitable single-step examination with the ability to exclude distant metastases when local salvage treatment is intended. In the brain, high-grade gliomas, metastases and benign lesions can be distinguished on the basis of 18F-fluorocholine uptake. Moreover, PET imaging is able to differentiate between radiation-induced injury and tumour recurrence. In the liver, 18F-fluorocholine PET/CT seems promising for the detection of hepatocellular carcinoma.

Age-related differences in metabolites in the posterior cingulate cortex and hippocampus of normal ageing brain: A 1H-MRS study

OBJECTIVE To study age-related metabolic changes in N-acetylaspartate (NAA), total creatine (tCr), choline (Cho) and myo-inositol (Ins). MATERIALS AND METHODS Proton magnetic resonance spectroscopy (1H-MRS) was performed in the posterior cingulate cortex (PCC) and the left hippocampus (HC) of 90 healthy subjects (42 women and 48 men aged 18-76 years, mean±SD, 48.4±16.8 years). Both metabolite ratios and absolute metabolite concentrations were evaluated. Analysis of covariance (ANCOVA) and linear regression were used for statistical analysis. RESULTS Metabolite ratios Ins/tCr and Ins/H2O were found significantly increased with age in the PCC (P<0.05 and P≤0.001, respectively), and in the HC (P<0.01 for both). An increased tCr/H2O was only observed in the PCC (P<0.01). Following absolute quantification based on the internal water signal, significantly increased concentrations of Ins and tCr in the PCC confirmed the relative findings (P<0.01 for both). CONCLUSION Age-related increases of tCr and Ins are found in the PCC, whereas this holds only true for Ins in the HC, indicating possible gliosis in the ageing brain. No age-dependent NAA decreases were observed in the PCC nor the HC. The 1H-MRS results in these specific brain regions can be important to differentiate normal ageing from age-related pathologies such as mild cognitive impairment (MCI) and Alzheimers disease.

Structural and metabolic features of two different variants of multiple sclerosis: a PET/MRI study.

Multimodality imaging such as proton magnetic resonance spectroscopy (MRS) and positron emission tomography (PET) have provided information specific to the underlying mechanisms of many brain diseases, including multiple sclerosis (MS).

Validation of 18F-FDG PET at conventional and delayed intervals for the discrimination of high-grade from low-grade gliomas: a stereotactic PET and MRI study.

Aim The aim of this study was to validate 18F-FDG PET imaging for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). Methods Twenty-one patients with gliomas undergoing a stereotactic biopsy underwent PET scanning at conventional and delayed intervals, diagnostic and stereotactic MR examinations. To calculate the uptake at the biopsy site, a 2-mm voxel was selected. Uptake in this voxel was expressed as a percentage of the average uptake per voxel in the normal brain. The difference in uptake between HGG and LGG at conventional and late intervals and the difference in uptake difference between HGG and LGG at both intervals were analyzed using t tests as well as a mixed-model analysis of variance. Results At conventional intervals, uptake in LGG was 67% of that in the normal brain. Between early and late intervals, a significant decrease in uptake of 11% (±2.5%) was noted (P = 0.001). Uptake in HGG at conventional intervals was 138% of that in the normal brain. Between early and late intervals, a significant increase in uptake of 43% (±11%) was noted (P = 0.005). The difference in uptake between HGG and LGG was significant both at conventional and delayed intervals (P < 0.001). Moreover, the difference in uptake between both groups was significantly greater (31%) at delayed than at conventional intervals (2%) (P < 0.001). Conclusions The results of this correlative study between tumor grade and 18F-FDG uptake both determined at the stereotactic biopsy site indicate that PET, particularly at delayed intervals, is valid for discriminating LGG from HGG.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization

BackgroundSegmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments.MethodsWe present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient’s dataset with a different set of random seeding points.ResultsUsing L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data.ConclusionsBased on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization

Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common initialization methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an initialization method, as it allows further enhancement of the sources during the NMF iterative procedure.

Progressive multifocal leukoencephalopathy (PML) mimicking high-grade glioma on delayed F-18 FDG PET imaging

The purpose of our study was to determine the increase in F-18 fluorodeoxyglucose (FDG) uptake in a patient with progressive multifocal leukoencephalopathy (PML) between early and late scan times using positron emission tomography (PET) imaging with F-18 FDG at conventional (60 minutes [min] after injection, PET(60)) and delayed (300 min after injection, PET(300)) intervals. PET(60) and PET(300) imaging was performed on a pathologically proven PML lesion. The PML lesion in the posterior fossa exhibited an increase in F-18 FDG uptake of 52% between early and late times, which was in the range of that in high-grade gliomas. Thus, dual-time-point PET with F-18 FDG may not be able to differentiate between infectious and malignant brain lesions.

Unforgettable - a pictorial essay on anatomy and pathology of the hippocampus.

The hippocampus is a small but complex anatomical structure that plays an important role in spatial and episodic memory. The hippocampus can be affected by a wide range of congenital variants and degenerative, inflammatory, vascular, tumoral and toxic-metabolic pathologies. Magnetic resonance imaging is the preferred imaging technique for evaluating the hippocampus. The main indications requiring tailored imaging sequences of the hippocampus are medically refractory epilepsy and dementia. The purpose of this pictorial review is threefold: (1) to review the normal anatomy of the hippocampus on MRI; (2) to discuss the optimal imaging strategy for the evaluation of the hippocampus; and (3) to present a pictorial overview of the most common anatomic variants and pathologic conditions affecting the hippocampus.Teaching points• Knowledge of normal hippocampal anatomy helps recognize anatomic variants and hippocampal pathology.• Refractory epilepsy and dementia are the main indications requiring dedicated hippocampal imaging.• Pathologic conditions centered in and around the hippocampus often have similar imaging features.• Clinical information is often necessary to come to a correct diagnosis or an apt differential.

A Semi-Automated Segmentation Framework for MRI Based Brain Tumor Segmentation Using Regularized Nonnegative Matrix Factorization

Segmentation plays an important role in the clinical management of brain tumors. Clinical practice would benefit from accurate and automated volumetric delineation of the tumor and its subcompartments. We present a semi-automated framework for brain tumor segmentation based on regularized nonnegative matrix factorization (NMF). L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to the BRATS 2013 Leaderboard dataset, consisting of publicly available multi-sequence MRI data of brain tumor patients. Our method performs well in comparison with state-of-the-art, in particular for the enhancing tumor region, for which we reach the highest Dice score among all participants.