Randomized phase III trial of metabolic imaging-guided dose escalation of radio-chemotherapy in patients with newly diagnosed glioblastoma (SPECTRO GLIO trial).

BACKGROUND: Glioblastoma (GBM) systematically recurs after a standard 60 Gy radio-chemotherapy regimen. Since magnetic resonance spectroscopic imaging (MRSI) has been shown to predict the site of relapse, we analyzed the effect of MRSI-guided dose escalation on overall survival (OS) of patients with newly diagnosed GBM. METHODS: In this multicentric prospective phase III trial, patients who had undergone biopsy or surgery for a GBM were randomly assigned to a standard dose (SD) of 60 Gy or a high dose (HD) of 60 Gy with an additional simultaneous integrated boost totaling 72 Gy to MRSI metabolic abnormalities, the tumor bed and residual contrast enhancements. Temozolomide was administered concomitantly and maintained for 6 months thereafter. RESULTS: One hundred and eighty patients were included in the study between March 2011 and March 2018. After a median follow-up of 43.9 months (95% CI [42.5; 45.5]), median OS was 22.6 months (95% CI [18.9; 25.4]) versus 22.2 months (95% CI [18.3; 27.8]) for HD, and median progression-free survival was 8.6 (95% CI [6.8; 10.8]) versus 7.8 months (95% CI [6.3; 8.6]), in SD versus HD, respectively. No increase in toxicity rate was observed in the study arm. The pseudoprogression rate was similar across the SD (14.4%) and HD (16.7%) groups. For O(6)-methylguanine-DNA methyltransferase (MGMT) methylated patients, the median OS was 38 months (95% CI [23.2; NR]) for HD patients versus 28.5 months (95% CI [21.1; 35.7]) for SD patients. CONCLUSION: The additional MRSI-guided irradiation dose totaling 72 Gy was well tolerated but did not improve OS in newly diagnosed GBM. TRIAL REGISTRATION: NCT01507506; registration date: December 20, 2011. https://clinicaltrials.gov/ct2/show/NCT01507506?cond=NCT01507506&rank=1.

Hypofractionated Stereotactic Re-irradiation and Anti-PDL1 Durvalumab Combination in Recurrent Glioblastoma: STERIMGLI Phase I Results.

BACKGROUND: Hypofractionated stereotactic radiotherapy (hFSRT) is a salvage option for recurrent glioblastoma (GB) which may synergize anti-PDL1 treatment. This phase I study evaluated the safety and the recommended phase II dose of anti-PDL1 durvalumab combined with hFSRT in patients with recurrent GB. METHODS: Patients were treated with 24 Gy, 8 Gy per fraction on days 1, 3, and 5 combined with the first 1500 mg Durvalumab dose on day 5, followed by infusions q4weeks until progression or for a maximum of 12 months. A standard 3 + 3 Durvalumab dose de-escalation design was used. Longitudinal lymphocytes count, cytokines analyses on plasma samples, and magnetic resonance imaging (MRI) were collected. RESULTS: Six patients were included. One dose limiting toxicity, an immune-related grade 3 vestibular neuritis related to Durvalumab, was reported. Median progression-free interval (PFI) and overall survival (OS) were 2.3 and 16.7 months, respectively. Multi-modal deep learning-based analysis including MRI, cytokines, and lymphocytes/neutrophil ratio isolated the patients presenting pseudoprogression, the longest PFI and those with the longest OS, but statistical significance cannot be established considering phase I data only. CONCLUSION: Combination of hFSRT and Durvalumab in recurrent GB was well tolerated in this phase I study. These encouraging results led to an ongoing randomized phase II. (ClinicalTrials.gov Identifier: NCT02866747).

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

BACKGROUND AND PURPOSE: All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). MATERIALS AND METHODS: MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. RESULTS: Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. CONCLUSION: Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis.

BACKGROUND AND PURPOSE: To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. MATERIALS AND METHODS: Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction. RESULTS: Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). CONCLUSION: Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.

Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI.

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

Quality control of 3D MRSI data in glioblastoma: Can we do without the experts?

PURPOSE: Proton magnetic resonance spectroscopic imaging (1H MRSI) is a noninvasive technique for assessing tumor metabolism. Manual inspection is still the gold standard for quality control (QC) of spectra, but it is both time-consuming and subjective. The aim of the present study was to assess automatic QC of glioblastoma MRSI data using random forest analysis. METHODS: Data for 25 patients, acquired prospectively in a preradiotherapy examination, were submitted to postprocessing with syngo.MR Spectro (VB40A; Siemens) or Java-based magnetic resonance user interface (jMRUI) software. A total of 28 features were extracted from each spectrum for the automatic QC. Three spectroscopists also performed manual inspections, labeling each spectrum as good or poor quality. All statistical analyses, with addressing unbalanced data, were conducted with R 3.6.1 (R Foundation for Statistical Computing; https://www.r-project.org). RESULTS: The random forest method classified the spectra with an area under the curve of 95.5%, sensitivity of 95.8%, and specificity of 81.7%. The most important feature for the classification was Residuum_Lipids_Versus_Fit, obtained with syngo.MR Spectro. CONCLUSION: The automatic QC method was able to distinguish between good- and poor-quality spectra, and can be used by radiation oncologists who are not spectroscopy experts. This study revealed a novel set of MRSI signal features that are closely correlated with spectral quality.

Determination of paramagnetic ferrous gel sensitivity in low energy x-ray beam produced by a miniature accelerator.

The INTRABEAM Carl Zeiss Surgical system (Oberkochen, Germany) is a miniature accelerator producing low energy photons (50 keV maximum). The published dosimetric characterization of the INTRABEAM was based on detectors (radiochromic films or ionization chambers) not allowing measuring the absorbed dose in the first millimeters of the irradiated medium, where the dose is actually prescribed. This study aims at determining with Magnetic Resonance Imaging (MRI) the sensitivity of a paramagnetic gel in order to measure the dose deposit produced with the INTRABEAM from 0 to 20 mm. Although spherical applicators are mostly used with the INTRABEAM system for breast applications, this study focuses on surface applicators that are of interest for cutaneous carcinomas. The irradiations at 12 different dose levels (between 2 Gy and 50 Gy at the gel surface) were performed with the INTRABEAM and a 4 cm surface applicator. The gel used in this study is a new « sensitive ¼ material. In order to compare gel sensitivity at low energy with high energy, gels were irradiated by an 18 MV photon beam produced by a Varian Clinac 2100 CD. T2 weighted multi echo MRI sequences were performed with 16 echo times. The T2 signal versus echo times was fitted with a mono-exponential function with 95% confidence interval. The calibration curve determined at low energy is a linear function (r2 = 0.9893) with a sensitivity of 0.0381 s-1.Gy-1, a similar linear function was obtained at high energy (0.0372 s-1.Gy-1 with r2 = 0.9662). The calibration curve at low energy was used to draw isodose maps from the MR images. The PDD (Percent Depth Dose) determined in the gel is within 5%-1mm of the ionization chamber PDD except for one point. The dosimetric sensitivity of this new paramagnetic ferrous gel was determined with MRI measurements. It allowed measuring the dose distribution specifically in the first millimeters for an irradiation with the INTRABEAM miniature accelerator equipped with a surface applicator.

Semi-automatic segmentation of whole-body images in longitudinal studies.

We propose a semi-automatic segmentation pipeline designed for longitudinal studies considering structures with large anatomical variability, where expert interactions are required for relevant segmentations. Our pipeline builds on the regularized Fast Marching (rFM) segmentation approach by Risseret al(2018). It consists in transporting baseline multi-label FM seeds on follow-up images, selecting the relevant ones and finally performing the rFM approach. It showed increased, robust and faster results compared to clinical manual segmentation. Our method was evaluated on 3D synthetic images and patients' whole-body MRI. It allowed a robust and flexible handling of organs longitudinal deformations while considerably reducing manual interventions.

Dose-painting multicenter phase III trial in newly diagnosed glioblastoma: the SPECTRO-GLIO trial comparing arm A standard radiochemotherapy to arm B radiochemotherapy with simultaneous integrated boost guided by MR spectroscopic imaging.

BACKGROUND: Glioblastoma, a high-grade glial infiltrating tumor, is the most frequent malignant brain tumor in adults and carries a dismal prognosis. External beam radiotherapy (EBRT) increases overall survival but this is still low due to local relapses, mostly occurring in the irradiation field. As the ratio of spectra of choline/N acetyl aspartate> 2 (CNR2) on MR spectroscopic imaging has been described as predictive for the site of local relapse, we hypothesized that dose escalation on these regions would increase local control and hence global survival. METHODS/DESIGN: In this multicenter prospective phase III trial for newly diagnosed glioblastoma, 220 patients having undergone biopsy or surgery are planned for randomization to two arms. Arm A is the Stupp protocol (EBRT 60 Gy on contrast enhancement + 2 cm margin with concomitant temozolomide (TMZ) and 6 months of TMZ maintenance); Arm B is the same treatment with an additional simultaneous integrated boost of intensity-modulated radiotherapy (IMRT) of 72Gy/2.4Gy delivered on the MR spectroscopic imaging metabolic volumes of CHO/NAA > 2 and contrast-enhancing lesions or resection cavity. Stratification is performed on surgical and MGMT status. DISCUSSION: This is a dose-painting trial, i.e. delivery of heterogeneous dose guided by metabolic imaging. The principal endpoint is overall survival. An online prospective quality control of volumes and dose is performed in the experimental arm. The study will yield a large amount of longitudinal multimodal MR imaging data including planning CT, radiotherapy dosimetry, MR spectroscopic, diffusion and perfusion imaging. TRIAL REGISTRATION: NCT01507506 , registration date December 20, 2011.

New approach of ultra-focal brachytherapy for low- and intermediate-risk prostate cancer with custom-linked I-125 seeds: A feasibility study of optimal dose coverage.

PURPOSE: To present the feasibility study of optimal dose coverage in ultra-focal brachytherapy (UFB) with multiparametric MRI for low- and intermediate-risk prostate cancer. METHODS AND MATERIALS: UFB provisional dose plans for small target volumes (<7 cc) were calculated on a prostate training phantom to optimize the seeds number and strength. Clinical UFB consisted in a contour-based nonrigid registration (MRI/Ultrasound) to implant a fiducial marker at the location of the tumor focus. Dosimetry was performed with iodine-125 seeds and a prescribed dose of 160 Gy. On CT scans acquired at 1 month, dose coverage of 152 Gy to the ultra-focal gross tumor volume was evaluated. Registrations between magnetic resonance and CT scans were assessed on the first 8 patients with three software solutions: VariSeed, 3D Slicer, and Mirada, and quantitative evaluations of the registrations were performed. Impact of these registrations on the initial dose matrix was performed. RESULTS: Mean differences between simulated dose plans and extrapolated Bard nomogram for UFB volumes were 36.3% (26-56) for the total activity, 18.3% (10-30) for seed strength, and 22.5% (16-38) for number of seeds. Registration method implemented in Mirada performed significantly better than VariSeed and 3D Slicer (p = 0.0117 and p = 0.0357, respectively). For dose plan evaluation between Mirada and VariSeed, D100% (Gy) for ultra-focal gross tumor volume had a mean difference of 28.06 Gy, mean values being still above the objective of 152 Gy. D90% for the prostate had a mean difference of 1.17 Gy. For urethra and rectum, dose limits were far below the recommendations. CONCLUSIONS: This UFB study confirmed the possibility to treat with optimal dose coverage target volumes smaller than 7 cc.

Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification.

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

PURPOSE: To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). METHODS: SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. RESULTS: Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. CONCLUSION: Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

Virtual bolus for total body irradiation treated with helical tomotherapy.

Intensity-modulated radiation therapy (IMRT) for total body irradiation (TBI) is practiced in several centers using the TomoTherapy System. In this context the planning target volume (PTV) is the entire body including the skin. A safety margin in the air surrounding the body should be added to take into account setup errors. But using inverse planning, over-fluence peak could be generated in the skin region to insure dose homogeneity. This work proposes to study the performance of the use of a virtual bolus (VB). A VB is a material placed on the skin surface during planning, but absent for the real treatment. The optimal VB that compensates large setup errors without introducing a high-dose increase or hot spots for small setup errors was determined. For two cylindrical phantoms, 20VBs with different densities, thicknesses or designs were tested. Dose coverage of the PTV (V95%) in the presence of simulated setup errors was computed to assess the VB performance. A measure of the dose increase in the phantom center due to the absence of the VB during treatment was also achieved. Finally, the fluence peak at the phantom edge was measured in complete buildup conditions using a large phantom and a detector matrix. Using these VBs, simulated setup errors were compensated to a minimum value of 2.6 and 2.1 cm for small and large phantom, respectively (and only 1.2 and 1.7 cm with no VB). An optimal double-layer VB was found with a density of 0.4 kg.m(-3) and a total thickness of 8mm; an inner layer of 5 mm was declared as the target for the treatment planning system and an additional layer of 3 mm was added to avoid the over-fluence peak. Using this VB, setup errors were compensated up to 2.9 cm. The dose increase was measured to be only +1.5% at the phantom center and over-fluence peak was strongly decreased.

Voxel-based evidence of perfusion normalization in glioblastoma patients included in a phase I-II trial of radiotherapy/tipifarnib combination.

We previously showed that the farnesyl transferase inihibitor, Tipifarnib induced vascularization normalization, oxygenation and radiosensitization in a pre-clinical glioblastoma (GBM) model. The aim of this study was to assess by dynamic-susceptibility-contrast MRI (DSC-MRI) the effect of radiotherapy (RT) and Tipifarnib combination on tumor perfusion in GBM patients. Eighteen patients with newly diagnosed GBM, enrolled in a phase I-II clinical trial associating RT with Tipifarnib, underwent anatomical MR imaging and DSC-MRI before (M0) and two months after treatment (M2). Anatomic volumes of interest (VOIs) were delineated according to contrast-enhanced and hyper-intense signal areas on T1-Gd and T2 images, respectively. Perfusion variations between M0 and M2 were assessed with median relative cerebral blood volume (rCBV) inside these VOIs. Another voxel by voxel analysis of CBV values classified 405,117 tumor voxels into High_, Normal_ and Low_CBVTUMOR according to the distribution of CBV in the contralateral normal tissue. These three categories of CBVTUMOR voxels were color-coded over anatomical MRI. Variations of median rCBV were significantly different for two groups of patients (P < 0.013): rCBV decreased when initial rCBV was ≥ 1.0 (Group_rCBV_M0 > 1) and rCBV increased when initial rCBV was < 1.0 (Group_rCBV_M0 < 1). Mapping of color-coded voxels provided additional spatial and quantitative information about tumor perfusion: Group_rCBV_M0 > 1 presented a significant decrease of High_CBVTUMOR volume (P = 0.015) simultaneously with a significant increase of Normal_CBVTUMOR volume (P = 0.009) after treatment. Group_rCBV_M0 < 1 presented a decrease of Low_CBVTUMOR volume with an increase of Normal_ and High_CBV TUMOR volume after treatment. Pre and post-treatment CBV measurements with DSC-MRI characterized tumor perfusion evolution in GBM patients treated with RT combined to Tipifarnib; showing variations in favour of tumor perfusion normalization in agreement with our pre-clinical results of vascular normalization.

Evaluation of the lactate-to-N-acetyl-aspartate ratio defined with magnetic resonance spectroscopic imaging before radiation therapy as a new predictive marker of the site of relapse in patients with glioblastoma multiforme.

PURPOSE: Because lactate accumulation is considered a surrogate for hypoxia and tumor radiation resistance, we studied the spatial distribution of the lactate-to-N-acetyl-aspartate ratio (LNR) before radiation therapy (RT) with 3D proton magnetic resonance spectroscopic imaging (3D-(1)H-MRSI) and assessed its impact on local tumor control in glioblastoma (GBM). METHODS AND MATERIALS: Fourteen patients with newly diagnosed GBM included in a phase 2 chemoradiation therapy trial constituted our database. Magnetic resonance imaging (MRI) and MRSI data before RT were evaluated and correlated to MRI data at relapse. The optimal threshold for tumor-associated LNR was determined with receiver-operating-characteristic (ROC) curve analysis of the pre-RT LNR values and MRI characteristics of the tumor. This threshold was used to segment pre-RT normalized LNR maps. Two spatial analyses were performed: (1) a pre-RT volumetric comparison of abnormal LNR areas with regions of MRI-defined lesions and a choline (Cho)-to- N-acetyl-aspartate (NAA) ratio ≥ 2 (CNR2); and (2) a voxel-by-voxel spatial analysis of 4,186,185 voxels with the intention of evaluating whether pre-RT abnormal LNR areas were predictive of the site of local recurrence. RESULTS: A LNR of ≥ 0.4 (LNR-0.4) discriminated between tumor-associated and normal LNR values with 88.8% sensitivity and 97.6% specificity. LNR-0.4 voxels were spatially different from those of MRI-defined lesions, representing 44% of contrast enhancement, 64% of central necrosis, and 26% of fluid-attenuated inversion recovery (FLAIR) abnormality volumes before RT. They extended beyond the overlap with CNR2 for most patients (median: 20 cm(3); range: 6-49 cm(3)). LNR-0.4 voxels were significantly predictive of local recurrence, regarded as contrast enhancement at relapse: 71% of voxels with a LNR-0.4 before RT were contrast enhanced at relapse versus 10% of voxels with a normal LNR (P<.01). CONCLUSIONS: Pre-RT LNR-0.4 in GBM indicates tumor areas that are likely to relapse. Further investigations are needed to confirm lactate imaging as a tool to define additional biological target volumes for dose painting.

Hybrid sparse regularization for magnetic resonance spectroscopy.

Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from poor signal-to-noise ratio limiting its clinical use for treatment purposes. This is due to the combination of a weak MR signal and low metabolite concentrations, in addition to the acquisition noise. We propose a new method that handles this challenge by efficiently denoising MRSI signals without constraining the spectral or spatial profiles. The proposed denoising approach is based on wavelet transforms and exploits the sparsity of the MRSI signals both in the spatial and frequency domains. A fast proximal optimization algorithm is then used to recover the optimal solution. Experiments on synthetic and real MRSI data showed that the proposed scheme achieves superior noise suppression (SNR increase up to 60%). In addition, this method is computationally efficient and preserves data features better than existing methods.

Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost.

BACKGROUND: To integrate 3D MR spectroscopy imaging (MRSI) in the treatment planning system (TPS) for glioblastoma dose painting to guide simultaneous integrated boost (SIB) in intensity-modulated radiation therapy (IMRT). METHODS: For sixteen glioblastoma patients, we have simulated three types of dosimetry plans, one conventional plan of 60-Gy in 3D conformational radiotherapy (3D-CRT), one 60-Gy plan in IMRT and one 72-Gy plan in SIB-IMRT. All sixteen MRSI metabolic maps were integrated into TPS, using normalization with color-space conversion and threshold-based segmentation. The fusion between the metabolic maps and the planning CT scans were assessed. Dosimetry comparisons were performed between the different plans of 60-Gy 3D-CRT, 60-Gy IMRT and 72-Gy SIB-IMRT, the last plan was targeted on MRSI abnormalities and contrast enhancement (CE). RESULTS: Fusion assessment was performed for 160 transformations. It resulted in maximum differences <1.00 mm for translation parameters and ≤1.15° for rotation. Dosimetry plans of 72-Gy SIB-IMRT and 60-Gy IMRT showed a significantly decreased maximum dose to the brainstem (44.00 and 44.30 vs. 57.01 Gy) and decreased high dose-volumes to normal brain (19 and 20 vs. 23% and 7 and 7 vs. 12%) compared to 60-Gy 3D-CRT (p < 0.05). CONCLUSIONS: Delivering standard doses to conventional target and higher doses to new target volumes characterized by MRSI and CE is now possible and does not increase dose to organs at risk. MRSI and CE abnormalities are now integrated for glioblastoma SIB-IMRT, concomitant with temozolomide, in an ongoing multi-institutional phase-III clinical trial. Our method of MR spectroscopy maps integration to TPS is robust and reliable; integration to neuronavigation systems with this method could also improve glioblastoma resection or guide biopsies.

Identification of activated regions during a language task.

Functional magnetic resonance imaging (fMRI) techniques are based on the assumption that changes in neural activity are accompanied by modulation in the blood-oxygenation-level-dependent (BOLD) signal. In addition to conventional increases in BOLD signals, sustained negative BOLD signal changes are occasionally observed in many fMRI experiments, which show regions of cortex that seem to respond in antiphase with primary stimulus. The existence of this so-called negative BOLD response (NBR) has been observed and investigated in many functional studies. Several theoretical mechanisms have been proposed to account for it, but its origin has never been fully explained. In this study, the variability of fMRI activation, including the sources of the negative BOLD signal, during phonological and semantic language tasks, was investigated in six right-handed healthy subjects. We found significant activations in the brain regions, mainly in the left hemisphere, involved in the language stimuli [prominent in the inferior frontal gyrus, approximately Brodmann Areas (BA)7, BA44, BA45 and BA47, and in the precuneus]. Moreover, we observed activations in motor regions [precentral gyrus and supplementary motor area (SMA)], a result that suggests a specific role of these areas (particularly the SMA) in language processing. Functional analysis have also shown that certain brain regions, including the posterior cingulate cortex and the anterior cingulate cortex, have consistently greater activity during resting states compared to states of performing cognitive tasks. In our study, we observed diffuse NBR at the cortical level and a stronger negative response in correspondence to the main sinuses. These phenomena seem to be unrelated to a specific neural activity, appearing to be expressions of a mechanical variation in hemodynamics. We discussed about the importance of these responses that are anticorrelated with the stimulus. Our data suggest that particular care must be considered in the interpretation of fMRI findings, especially in the case of presurgical studies.

Quantitative evaluation for brain CT/MRI coregistration based on maximization of mutual information in patients with focal epilepsy investigated with subdural electrodes.

Patients with drug-resistant focal epilepsy may require intracranial investigations with subdural electrodes. These must be correctly localized with respect to the brain cortical surface and require appropriate monitoring. For this purpose, coregistration techniques, which fuse preimplantation 3D magnetic resonance imaging scans with postimplantation computed tomography scans, have been implemented. In order to reduce localization errors due to the fusion process, we used a coregistration method based on the maximization of mutual information (MI) in 11 patients with extratemporal epilepsy who were invasively investigated. Our registration method is based on three processing steps: rigid-body transformation for coregistration, computation of MI as a similarity measure and the use of the Downhill Simplex optimization method. After consistency analysis, the shift of the registration method reached 0.14+/-0.27 mm in translation and 0.03+/-0.14 degrees in rotation, and the accuracies assessed on voxels of skull surface and voxels of the center of the brain volume were 1.42+/-0.61 and 1.15+/-0.53 mm, respectively. The accuracy of the fusion process reached submillimeter range, and results were considered reliable for surgical planning in all studied patients.

BOLD signal and vessel dynamics: a hierarchical cluster analysis.

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (fMRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites.