Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.

PURPOSE: The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS: In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION: Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images.

INTRODUCTION: Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS: A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS: Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS: We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.

Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer.

PURPOSE: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus. METHODS: Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on patients treated with RT in our institution. Radiomic features were extracted from 3D dose maps considering Gy values as grey-levels in images. DVH and usual clinical factors were also considered. Three toxicity prediction models (clinical only, clinical + DVH and combined, i.e., including clinical + DVH + radiomics) were incrementally trained using a neural network on 70% of the patients for prediction of grade ≥2 acute and late pulmonary toxicities (APT/LPT) and grade ≥2 acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal cut-off values were determined based on the Youden Index. The trained models were then evaluated in the remaining 30% of patients using balanced accuracy (BAcc). RESULTS: 167 patients were treated from 2015 to 2018: 78% non small-cell lung cancers, 14% small-cell lung cancers and 8% other histology with a median age at treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT and AET. In the training set (n = 117), the corresponding BAcc for clinical only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72. CONCLUSION: In patients with a lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs for the prediction of APT and LPT.

Use of radiomics in the radiation oncology setting: Where do we stand and what do we need?

Radiomics is a field that has been growing rapidly for the past ten years in medical imaging and more particularly in oncology where the primary objective is to contribute to personalised and predictive medicine. This short review aimed at providing some insights regarding the potential value of radiomics for cancer patients treated with radiotherapy. Radiomics may contribute to each stage of the patients' management: diagnosis, planning, treatment monitoring and post-treatment follow-up (toxicity and response). However, its applicability in clinical routine is currently hindered by several factors, including lack of automation, standardisation and harmonisation. A major effort must be carried out to automate the workflow, standardise radiomics good practices and carry out large-scale studies before any transfer to daily clinical practice.

[Radiation-oncology horizon 2030: From microbiota to plasma laser]. / Oncologie-radiothérapie horizon 2030 : du microbiote au laser plasma.

Advances in physical, technological and biological fields have made radiation oncology a discipline in continual evolution. New current research areas could be implemented in the clinic in the near future. In this review in the form of several interviews, various promising themes for our specialty are described such as the gut microbiota, tumor organoids (or avatar), artificial intelligence, connected therapies, nanotechnologies and plasma laser. The individual prediction of the best therapeutic index combined with the integration of new technologies will ideally allow highly personalized treatment of patients receiving radiation therapy.

Harmonization strategies for multicenter radiomics investigations.

Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called 'batch-effects' in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.

Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies.

Multicenter studies are needed to demonstrate the clinical potential value of radiomics as a prognostic tool. However, variability in scanner models, acquisition protocols and reconstruction settings are unavoidable and radiomic features are notoriously sensitive to these factors, which hinders pooling them in a statistical analysis. A statistical harmonization method called ComBat was developed to deal with the "batch effect" in gene expression microarray data and was used in radiomics studies to deal with the "center-effect". Our goal was to evaluate modifications in ComBat allowing for more flexibility in choosing a reference and improving robustness of the estimation. Two modified ComBat versions were evaluated: M-ComBat allows to transform all features distributions to a chosen reference, instead of the overall mean, providing more flexibility. B-ComBat adds bootstrap and Monte Carlo for improved robustness in the estimation. BM-ComBat combines both modifications. The four versions were compared regarding their ability to harmonize features in a multicenter context in two different clinical datasets. The first contains 119 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging and positron emission tomography imaging. In that case ComBat was applied with 3 labels corresponding to each center. The second one contains 98 locally advanced laryngeal cancer patients from 5 centers with contrast-enhanced computed tomography. In that specific case, because imaging settings were highly heterogeneous even within each of the five centers, unsupervised clustering was used to determine two labels for applying ComBat. The impact of each harmonization was evaluated through three different machine learning pipelines for the modelling step in predicting the clinical outcomes, across two performance metrics (balanced accuracy and Matthews correlation coefficient). Before harmonization, almost all radiomic features had significantly different distributions between labels. These differences were successfully removed with all ComBat versions. The predictive ability of the radiomic models was always improved with harmonization and the improved ComBat provided the best results. This was observed consistently in both datasets, through all machine learning pipelines and performance metrics. The proposed modifications allow for more flexibility and robustness in the estimation. They also slightly but consistently improve the predictive power of resulting radiomic models.

[Prognostic value of the metabolically active tumour volume]. / Valeur pronostique du volume tumoral métabolique sur la TEP au ((18)F)-fluorodésoxyglucose préthérapeutique pour le cancer de l'œsophage localisé.

PURPOSE: The purpose of this study was to assess the prognostic value of different parameters on pretreatment fluorodeoxyglucose [((18)F)-FDG] positron emission tomography-computed tomography (PET-CT) in patients with localized oesophageal cancer. PATIENTS AND METHOD: We retrospectively reviewed 83 cases of localised oesophageal cancer treated in our institution. Patients were treated with curative intent and have received chemoradiotherapy alone or followed by surgery. Different prognostic parameters were correlated to survival: cancer-related factors, patient-related factors and parameters derived from PET-CT (maximum standardized uptake value [SUV max], metabolically active tumor volume either measured with an automatic segmentation software ["fuzzy locally adaptive bayesian": MATVFLAB] or with an adaptive threshold method [MATVseuil] and total lesion glycolysis [TLGFLAB and TLGseuil]). RESULTS: The median follow-up was 21.8 months (range: 0.16-104). The median overall survival was 22 months (95% confidence interval [95%CI]: 15.2-28.9). There were 67 deaths: 49 associated with cancer and 18 from intercurrent causes. None of the tested factors was significant on overall survival. In univariate analysis, the following three factors affected the specific survival: MATVFLAB (P=0.025), TLGFLAB (P=0.04) and TLGseuil (P=0.04). In multivariate analysis, only MATVFLAB had a significant impact on specific survival (P=0.049): MATVFLAB<18 cm(3): 31.2 months (95%CI: 21.7-not reached) and MATVFLAB>18 cm(3): 20 months (95%CI: 11.1-228.9). CONCLUSION: The metabolically active tumour volume measured with the automatic segmentation software FLAB on baseline PET-CT was a significant prognostic factor, which should be tested on a larger cohort.

Hypoxia imaging with [18F]-FMISO-PET for guided dose escalation with intensity-modulated radiotherapy in head-and-neck cancers.

BACKGROUND AND PURPOSE: Positron emission tomography (PET) with [(18)F]-fluoromisonidazole ([(18)F]-FMISO) provides a non-invasive assessment of hypoxia. The aim of this study is to assess the feasibility of a dose escalation with volumetric modulated arc therapy (VMAT) guided by [(18)F]-FMISO-PET for head-and-neck cancers (HNC). PATIENTS AND METHODS: Ten patients with inoperable stages III-IV HNC underwent [(18)F]-FMISO-PET before radiotherapy. Hypoxic target volumes (HTV) were segmented automatically by using the fuzzy locally adaptive Bayesian method. Retrospectively, two VMAT plans were generated delivering 70 Gy to the gross tumour volume (GTV) defined on computed tomography simulation or 79.8 Gy to the HTV. A dosimetric comparison was performed, based on calculations of tumour control probability (TCP), normal tissue complication probability (NTCP) for the parotid glands and uncomplicated tumour control probability (UTCP). RESULTS: The mean hypoxic fraction, defined as the ratio between the HTV and the GTV, was 0.18. The mean average dose for both parotids was 22.7 Gy and 25.5 Gy without and with dose escalation respectively. FMISO-guided dose escalation led to a mean increase of TCP, NTCP for both parotids and UTCP by 18.1, 4.6 and 8% respectively. CONCLUSION: A dose escalation up to 79.8 Gy guided by [(18)F]-FMISO-PET with VMAT seems feasible with improvement of TCP and without excessive increase of NTCP for parotids.

Potential of [18F]-fluoromisonidazole positron-emission tomography for radiotherapy planning in head and neck squamous cell carcinomas.

BACKGROUND AND PURPOSE: Positron-emission tomography (PET) with [(18)F]-fluoromisonidazole (FMISO) permits consideration of radiotherapy dose escalation to hypoxic volumes in head and neck cancers (HNC). However, the definition of FMISO volumes remains problematic. The aims of this study are to confirm that delayed acquisition at 4 h is most appropriate for FMISO-PET imaging and to assess different methods of volume segmentation. PATIENTS AND METHODS: A total of 15 HNC patients underwent several FMISO-PET/computed tomography (CT) acquisitions 2, 3 and 4 h after FMISO injection. Three automatic methods of PET image segmentation were tested: fixed threshold, adaptive threshold based on the ratio between tumour-derived and background activities (R(T/B)) and the fuzzy locally adaptive Bayesian (FLAB) method. The hypoxic fraction (HF), which is defined as the ratio between the FMISO and CT volumes, was also calculated. RESULTS: The R(T/B) for images acquired at 2, 3 and 4 h differed significantly, with mean values of 2.5 (1.7-2.9), 3 (2-4.5) and 3.4 (2.3-6.1), respectively. The mean tumour volume, as defined manually using CT images, was 39.1 ml (1.2-116 ml). After 4 h, the mean FMISO volumes were 18.9 (0.1-81), 9.5 (0.9-33.1) and 12.5 ml (0.9-38.4 ml) with fixed threshold, adaptive threshold and the FLAB method, respectively; median HF values were 0.47 (0.1-1.93), 0.25 (0.11-0.75) and 0.35 (0.14-1.05), respectively. FMISO volumes were significantly different. CONCLUSION: The best contrast is obtained at the 4-hour acquisition time. Large discrepancies were found between the three tested methods of volume segmentation.

HER2-overexpressing breast cancer: FDG uptake after two cycles of chemotherapy predicts the outcome of neoadjuvant treatment.

BACKGROUND: Pathologic complete response (pCR) to neoadjuvant treatment (NAT) is associated with improved survival of patients with HER2+ breast cancer. We investigated the ability of interim positron emission tomography (PET) regarding early prediction of pathology outcomes. METHODS: During 61 months, consecutive patients with locally advanced or large HER2+ breast cancer patients without distant metastases were included. All patients received NAT with four cycles of epirubicin+cyclophosphamide, followed by four cycles of docetaxel+trastuzumab. ¹8F-fluorodeoxyglucose (¹8F-FDG)-PET/computed tomography (CT) was performed at baseline (PET1) and after two cycles of chemotherapy (PET2). Maximum standardised uptake values were measured in the primary tumour as well as in the axillary lymph nodes. The correlation between pathologic response and SUV parameters (SUVmax at PET1, PET2 and ΔSUVmax) was examined with the t-test. The predictive performance regarding the identification of non-responders was evaluated using receiver operating characteristics (ROC) analysis. RESULTS: Thirty women were prospectively included and 60 PET/CT examination performed. At baseline, 22 patients had PET+ axilla and in nine of them ¹8F-FDG uptake was higher than in the primary tumour. At surgery, 14 patients (47%) showed residual tumour (non-pCR), whereas 16 (53%) reached pCR. Best prediction was obtained when considering the absolute residual SUVmax value at PET2 (AUC=0.91) vs 0.67 for SUVmax at PET1 and 0.86 for ΔSUVmax. The risk of non-pCR was 92.3% in patients with any site of residual uptake >3 at PET2, no matter whether in breast or axilla, vs 11.8% in patients with uptake ≤3 (P=0.0001). The sensitivity, specificity, PPV, NPV and overall accuracy of this cutoff were, respectively: 85.7%, 93.8%, 92.3%, 88.2% and 90%. CONCLUSION: The level of residual ¹8F-FDG uptake after two cycles of chemotherapy predicts residual disease at completion of NAT with chemotherapy+trastuzumab with high accuracy. Because many innovative therapeutic strategies are now available (e.g., addition of a second HER2-directed therapy or an antiangiogenic), early prediction of poor response is critical.

Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation.

Denoising of Positron Emission Tomography (PET) images is a challenging task due to the inherent low signal-to-noise ratio (SNR) of the acquired data. A pre-processing denoising step may facilitate and improve the results of further steps such as segmentation, quantification or textural features characterization. Different recent denoising techniques have been introduced and most state-of-the-art methods are based on filtering in the wavelet domain. However, the wavelet transform suffers from some limitations due to its non-optimal processing of edge discontinuities. More recently, a new multi scale geometric approach has been proposed, namely the curvelet transform. It extends the wavelet transform to account for directional properties in the image. In order to address the issue of resolution loss associated with standard denoising, we considered a strategy combining the complementary wavelet and curvelet transforms. We compared different figures of merit (e.g. SNR increase, noise decrease in homogeneous regions, resolution loss, and intensity bias) on simulated and clinical datasets with the proposed combined approach and the wavelet-only and curvelet-only filtering techniques. The three methods led to an increase of the SNR. Regarding the quantitative accuracy however, the wavelet and curvelet only denoising approaches led to larger biases in the intensity and the contrast than the proposed combined algorithm. This approach could become an alternative solution to filters currently used after image reconstruction in clinical systems such as the Gaussian filter.

[Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives]. / Méthodologies de définition automatique des volumes métaboliquement actifs en TEP : évaluation et perspectives.

PET imaging is now considered a gold standard tool in clinical oncology, especially for diagnosis purposes. More recent applications such as therapy follow-up or tumor targeting in radiotherapy require a fast, accurate and robust metabolically active tumor volumes delineation on emission images, which cannot be obtained through manual contouring. This clinical need has sprung a large number of methodological developments regarding automatic methods to define tumor volumes on PET images. This paper reviews most of the methodologies that have been recently proposed and discusses their framework and methodological and/or clinical validation. Perspectives regarding the future work to be done are also suggested.

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment.

In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications.

Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging.

PURPOSE: Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising. MATERIALS AND METHODS: Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods. RESULTS: Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images. CONCLUSION: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.

Contrast enhancement in emission tomography by way of synergistic PET/CT image combination.

UNLABELLED: The display of image fusion is well accepted as a powerful tool in visual image analysis and comparison. In clinical practice, this is a mandatory step when studying images from a dual PET/CT scanner. However, the display methods that are implemented on most workstations simply show both images side by side, in separate and synchronized windows. Sometimes images are presented superimposed in a single window, preventing the user from doing quantitative analysis. In this article a new image fusion scheme is presented, allowing performing quantitative analysis directly on the fused images. METHODS: The objective is to preserve the functional information provided by PET while incorporating details of higher resolution from the CT image. The process relies on a discrete wavelet-based image merging: both images are decomposed into successive details layers by using the "à trous" transform. This algorithm performs wavelet decomposition of images and provides coarser and coarser spatial resolution versions of them. The high-spatial frequencies of the CT, or details, can be easily obtained at any level of resolution. A simple model is then inferred to compute the lacking details of the PET scan from the high frequency detail layers of the CT. These details are then incorporated in the PET image on a voxel-to-voxel basis, giving the fused PET/CT image. RESULTS: Aside from the expected visual enhancement, quantitative comparison of initial PET and CT images with fused images was performed in 12 patients. The obtained results were in accordance with the objectives of the study, in the sense that the organs' mean intensity in PET was preserved in the fused image. CONCLUSION: This alternative approach to PET/CT fusion display should be of interest for people interested in a more quantitative aspect of image fusion. The proposed method is actually complementary to more classical visualization tools.

Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

A multiresolution image based approach for correction of partial volume effects in emission tomography.

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

[Infantile myofibromatosis--a case report]. / Die infantile Myofibromatose--Fallbericht.

A four months old child was referred for a rapidly growing recurrency of a tumor of the left eyebrow. The tumor was excised completely. The pathologic examination revealed an infantile myofibromatosis, most probably of the solitary type. The term "infantile myofibromatosis" summarizes a heterogenous group of rare fibromatoses in childhood, characterized by the proliferation of myofibroblasts. Isolated tumors have a fair prognosis after complete excision.

[Langerhans cell histiocytosis of the orbits]. / Langerhans-Zell-Histiocytose der Orbita.

Langerhans cell Histiocytosis is an infrequent disease of the orbit in little children. It is sometimes recognized in a rather late stage. We present three cases and discuss the manifestations and the treatment.