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Purpose Characterizing vessel territories can provide crucial information for evaluation of cerebrovascular disorders. In this study, we present a novel postprocessing pipeline for vascular territorial imaging of cerebral arteries based on a noncontrast enhanced time-resolved 4D magnetic resonance angiography (MRA). Methods Eight healthy participants, 1 Moyamoya patient, and 1 arteriovenous malformations patient were recruited. Territorial segmentation and relative blood flow rate calculations of cerebral arteries including left and right middle cerebral arteries and left and right posterior cerebral arteries were carried out based on the 4D MRA-derived arterial arrival time maps of intracranial vessels. Results Among healthy young subjects, the average relative blood flow rate values corresponding to left and right middle cerebral arteries and left and right posterior cerebral arteries were 35.9 ± 5.9%, 32.9 ± 7.5%, 15.4 ± 3.8%, and 15.9 ± 2.5%, respectively. Excellent agreement was observed between relative blood flow rate values obtained from the proposed 4D MRA-based method and reference 2D phase contrast MRI. Abnormal cerebral circulations were visualized and quantified on both patients using the developed technique. Conclusion The vascular territorial imaging technique developed in this study allowed for the generation of territorial maps with user-defined level of details within a clinically feasible scan time, and as such may provide useful information to assess cerebral circulation balance in different pathologies.

Purpose Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. Methods A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering. Results The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively. Conclusion This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice., 15 pages, 8 figures, presented in CARS 2020, submitted to IJCARS

Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; pp Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

Contains fulltext : 181510.pdf (Publisher’s version ) (Open Access) Atypical brain development and early brain injury have profound and long lasting impact on the development, skill acquisition, and subsequent independence of a child. Heterogeneity is present at the brain level and at the motor level; particularly with respect to phenomena of bilateral activation and mirrored movements (MMs). In this multiple case study we consider the feasibility of using several modalities to explore the relationship between brain structure and/or activity and hand function: Electroencephalography (EEG), both structural and functional Magnetic Resonance Imaging (sMRI, fMRI), diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), Electromyography (EMG) and hand function assessments. Methods : 15 children with unilateral CP (ages: 9.4±2.5 years) undertook hand function assessments and at least two additional neuroimaging and/or neurophysiological procedures: MRI/DTI/fMRI (n=13), TMS (n=11), and/or EEG/EMG (n=8). During the fMRI scans and EEG measurements, a motor task was performed to study cortical motor control activity during simple hand movements. DTI tractography analysis was used to study the corpus-callosum (CC) and cortico-spinal tracts (CST). TMS was used to study cortico-spinal connectivity pattern. Results: Type and range of severity of brain injury was evident across all levels of manual ability with the highest radiological scores corresponded to children poorer manual ability. Evidence of MMs was found in 7 children, mostly detected when moving the affected hand, and not necessarily corresponding to bilateral brain activation. When moving the affected hand, bilateral brain activation was seen in 6/11 children while 3/11 demonstrated unilateral activation in the contralateral hemisphere, and one child demonstrated motor activation predominantly in the supplementary motor area (SMA). TMS revealed three types of connectivity patterns from the cortex to the affected hand: a contralateral (n=3), an ipsilateral (n=4) and a mixed (n=1) connectivity pattern; again without clear association with MMs. No differences were found between children with and without MMs in lesion scores, motor fMRI laterality indices, CST diffusivity values, and upper limb function. In the genu, midbody, and splenium of the CC, higher fractional anisotropy values were found in children with MMs compared to children without MMs. The EEG data indicated a stronger mu-restoration above the contralateral hemisphere in 6/8 children and above the ipsilateral hemisphere in 2/8 children. Conclusion: The current results demonstrate benefits from the use of different modalities when studying upper-limb function in children with CP; not least to accommodate to the variations in tolerance and feasibility of implementation of the differing methods. These exposed multiple individual brain-reorganization patterns corresponding to different functional motor abilities. Additional research is warranted to understand the transactional influences of early brain injury, neuroplasticity and developmental and environmental factors on hand function in order to develop targeted interventions. 12 p.

Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts. Preoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images, $$\hbox {T}_{2}$$ -weighted images (WI) and post-contrast $$\hbox {T}_{1}\hbox {WI}$$ . Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis. Radiomic analysis differentiated tumors with 1p/19q intact ( $$n=21$$ ; astrocytomas) from those with 1p/19q codeleted ( $$n=26$$ ; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity $$=$$ 92%, specificity $$=$$ 83% and accuracy $$=$$ 87%, and with area under the curve $$=$$ 0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted ( $$46.2\pm 30.0$$ vs. $$30.8\pm 16.8$$ cc, respectively; $$p=0.03$$ ) and predominantly located to the left insula ( $$p=0.04$$ ). The proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.

Background: Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. Subjects and Methods: A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). Results: The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart. Discussion: The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice.

Introduction Understanding regarding the whole placental vascular network structure is limited. Our aim was to quantitatively characterize the human placental vascular tree ex-vivo using high-resolution MRI. Methods 34 normal placentas were rinsed and injected with a solution of gelatin and contrast agent through the umbilical vessels. A sample of six placentas taken from pregnancies with intrauterine-growth-restriction (IUGR) was used to demonstrate the potential application to cases with placental insufficiency. Structural ex-vivo MR scans of the placenta were performed using high resolution T1 weighted images. A semi-automatic method was developed to segment and characterize the placental vascular architecture: placental volume and cord insertion location; number of bifurcations, generations and vessels diameters. Results Different vascular patterns were found in placentas with central versus marginal cord-insertion. Based on the placental volume and number of bifurcations we were able to predict birth weight. Furthermore, preliminary results on IUGR sample demonstrated the potential of this method to differentiate between small newborns with suspected IUGR from small normal newborns who reached their full growth potential. Results obtained using the automatic method were validated against manual values demonstrating no significant differences or bias. Histopathology supported the imaging findings. Discussion: This is the first study to quantitatively characterize the human placental vascular architecture using high resolution ex-vivo MRI. Different patterns of vascular architecture may be related to different functioning of the placenta and affect fetal development. This method is simple, relatively fast, provides detailed information of the placental vascular architecture, and may have important clinical applications.

The interstitium-to-plasma rate constant (kep), extracted from dynamic contrast enhancement (DCE-MRI) MRI data, seems to have an important role in the assessment of patients with brain tumors. This parameter is affected by the slow behavior of the system, and thus is expected to be highly dependent on acquisition duration. The aim of this study was to optimize the scan duration and protocol of DCE-MRI for accurate estimation of the kep parameter in patients with high grade brain tumors. The effects of DCE-MRI scan duration and protocol design (continuous vs integrated scanning) on the estimated pharmacokinetic (PK) parameters and on model selection, were studied using both simulated and patient data. Scan duration varied, up to 60min for simulated data, and up to 25min in 25 MRI scans obtained from patients with high grade brain tumors, with continuous and integrated scanning protocols. Converging results were obtained from simulated and real data. Significant effect of scan duration was detected on kep. Scan duration of 9min, with integrated protocol in which the data are acquired continuously for 5min, and additional volumes at 7 and 9min, was sufficient for accurate estimation of even low kep values, with an average error of 3%. Over-estimation of the PK parameters was detected for scan duration 12min. An integrated protocol of 9min is suggested as optimal for clinical use in patients with high grade brain tumors. Lower acquisition time may result in over-estimation of kep when using ETM, and therefore care should be taken using model selection.

PURPOSE To optimize the analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) under the two-compartment-exchange-model (2CXM) and to incorporate voxelwise bolus-arrival-time (BAT). MATERIALS AND METHODS The accuracy of the pharmacokinetic (PK) parameters, extracted from 3T DCE-MRI using 2CXM, was tested under several conditions: eight algorithms for data estimation; correction for BAT; using model selection; different temporal resolution and scan duration. Comparisons were performed on simulated data. The best algorithm was applied to seven patients with brain tumors or following stroke. The extracted perfusion parameters were compared to those of dynamic susceptibility contrast MRI (DSC-MRI). RESULTS ACoPeD (AIF-corrected-perfusion-DCE-MRI), an analysis using a 2nd derivative regularized-spline and incorporating BAT, achieved the most accurate estimation in simulated data, mean-relative-error: Fp , F, vp , ve : 24.8%, 41.7%, 26.4%, 27.2% vs. 76.5%, 190.8%, 78.8%, 82.39% of the direct four parameters estimation (one-sided two-sample t-test, P < 0.001). Correction for BAT increased the estimation accuracy of the PK parameters by more than 30% and provided a supertemporal resolution estimation of the BAT (higher than the acquired resolution, mean-absolute-error 0.2 sec). High temporal resolution (∼2 sec) is required to avoid biased estimation of PK parameters, and long scan duration (∼20 min) is important for reliable permeability but not for perfusion estimations, mean-error-reduction: E: ∼12%, ve : ∼6%. Using ACoPeD, PK values from normal-appearing white matter, gray matter, and lesion were extracted from patients. Preliminary results showed significant voxelwise correlations to DSC-MRI, between flow values in a patient following stroke (r = 0.49, P < 0.001), and blood volume in a patient with a brain tumor (r = 0.62, P < 0.001). CONCLUSION This study proposes an optimized analysis method, ACoPeD, for tissue perfusion and permeability estimation using DCE-MRI, to be used in clinical settings. LEVEL OF EVIDENCE 1 J. Magn. Reson. Imaging 2017;45:237-249.

Knowledge regarding the association between functional connectivity and white-matter (WM) maturation of motor and visual networks in preterm infants at term equivalent age (TEA) and their association with behavioral outcome is currently limited. Thirty-two preterm infants born

To evaluate the interrelationship among impaired renal function, brain pathology on imaging, and cognitive decline in a longitudinal poststroke cohort.The Tel Aviv Brain Acute Stroke Cohort study is a prospective cohort of mild-moderate ischemic stroke/TIA survivors without dementia who underwent a 3T MRI and were cognitively assessed at admission and for 24 months following stroke. Renal function was evaluated at admission by creatinine clearance (CCl) estimation. The volumes of ischemic lesions and preexisting white matter hyperintensities (WMH), brain atrophy, and microstructural changes of the normal-appearing white matter tissue were measured using previously validated methods.Baseline data were available for 431 participants. Participants with a CCl60 mL/min at baseline performed significantly worse in all cognitive tests over time (p = 0.001) than those with a CCl ≥60 mL/min and had larger WMH volume and cortical atrophy and smaller hippocampal volume (all p0.001). After 2 years, 15.5% of the participants were diagnosed with cognitive impairment. Multiple logistic regression analysis, controlling for traditional risk factors, suggested CCl60 mL/min at baseline as a significant predictor for the development of cognitive impairment 2 years after the index stroke (odds ratio 2.01 [95% confidence interval 1.03-3.92], p = 0.041).Impaired renal function is associated with increased WMH volume and cortical atrophy, known biomarkers of the aging brain, and is a predictor for cognitive decline 2 years after stroke/TIA. Decreased renal function may be associated with cerebral small vessel disease underlying poststroke cognitive decline, suggesting a new target for early intervention.

INTRODUCTION: Peripheral neurofibromas (NF) represent one of the most common clinical presentations of the NF1. About 10% of patients with NF1 develop malignant peripheral nerve-sheath tumours (MPNST), which is a major cause of morbidity in NF1 patients. Improved prognosis can be achieved if MPNST are diagnosed at an early stage permitting ablative surgery with wide resection margins. Better understanding regarding the natural history and biology of MPNST has been obtained in the last decade, however existing imaging modalities are still imperfect for diagnosis of MPNST. The aim of this study was to use a machine learning method in order to differentiate between benign plexiform neurofibromas from MPNST in patients with NF1 based on conventional MRI methods. METHODS: Among 23 NF1 patients with pathologically-defined MPNST treated in the last decade in TASMC, seven patients with available preoperative MRI studies were included in the study. For comparison, 7 patients with benign plexiform neurofibromas were chosen from the database of the Gilbert Israeli Neurofibromatosis Center. Regions of interest at tumor areas were extracted from the post contrast 2D T(1)-weighted spin-echo images acquired on 1.5/3T MRI systems. For each patient, speeded-up-robust (SURF) features were extracted from lesion areas using Matlab bagOfFeatures algorithm. The algorithm was trained with number of visual words =500, grid point selection method, strongest features=0.8 and grid step size (in pixels)=[5 5]. The algorithm was tested with 100 iterations, with the data being randomly split into training (80%) and testing (20%) datasets. RESULTS: A code-book of lesion representations was generated for each group and was given as input to the support-vector-machine (SVM) classifier. Classification results achieved mean accuracy=80%, mean sensitivity=72% and mean specificity=87%. CONCLUSION: Our preliminary results suggest that a quantitative image representation method based on Bag-of-Features may assist in the classification between benign plexiform neurofibromas and MPNST in NF1.

We report on a series of nine patients with high grade glioma (HGG) who developed punctate contrast-enhancing foci on MRI, several years after tumor treatment. The lesions were considered suspicious for tumor progression at their onset, but were proven over time to be benign in nature. Five of the patients were treated for glioblastoma; 3 for anaplastic astrocytoma, and one for anaplastic oligodendroglioma. Median age of the patients at diagnosis was 48 years (31–62 years). The mean time of onset of “suspicious spots” following treatment was 51 months after diagnosis (6–170 months). Of note, median overall survival of the group is 96 months; with none of the nine patients deceased, and only 2 patients known to have progression. The remainder of the 7 patients are free of active tumor and being followed off treatment. No apparent clinical manifestations of the lesions have been observed. The majority of the punctate lesions were of a dynamic nature; they appeared and spontaneously disappeared over time, without intervention. All lesions developed within the field of radiation treatment; some were contralateral to the tumor. Radiological characteristics of the lesions including analysis of SWI, DWI, and DCE magnetic resonance imaging sequences will be reported. Punctate enhancing foci in the region of radiation treatment are phenomena which may be manifestation of delayed treatment sequelae in HGG. Patients with these lesions are on average of younger age and have a particularly long survival. These lesions appeared several years following oncological treatment. The foci should be initially followed more closely until shown to be of a non-progressive nature; they should not be presumed to be indicative of tumor recurrence. Possible etiology of the lesions may be related to vascular changes induced by radiation.

Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI.To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast TRetrospective.Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others).Post-contrast 3D TAnalysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first- and second-order statistical, morphological, wavelet features, and bag-of-features. Following dimension reduction, classification was performed using various machine-learning algorithms including support-vector machine (SVM), k-nearest neighbor, decision trees, and ensemble classifiers.For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively.Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519-528.

To study the repeatability of plasma volume (vTwenty eight healthy subjects, of which eleven scanned twice, were used to assess the repeatability of vThe threshold values for significant changes were: LDD = 0.0024 (ml/100 ml, 21%) for mean vThis study shows high repeatability of v

The hippocampus is known to play a vital role in learning and memory and was demonstrated as an early imaging marker for Alzheimer's disease (AD). However, its role as a predictor for mild cognitive impairment and dementia following stroke is unclear. The main purpose of this study was to examine the associations between hippocampal volume, mean diffusivity (MD) and connectivity and cognitive state following stroke. Eighty three consecutive first ever mild to moderate stroke or transient ischemic attack (TIA) survivors from our ongoing prospective TABASCO (Tel Aviv Brain Acute Stroke Cohort) study underwent magnetic resonance imaging scans within 7 days of stroke onset. Hippocampal volume was measured from T1 weighted images, hippocampal mean diffusivity was calculated from diffusion tensor imaging and connectivity was calculated from resting state fMRI. Global cognitive assessments were evaluated during hospitalization and 6 and 12 months later using a computerized neuropsychological battery. Multiple linear regression analysis was used to test which of the hippocampi measurements best predict cognitive state. All three imaging parameters were significantly correlated to each other (|r's| >0.3, P's

Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. Results: The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. Conclusions: The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Cerebral blood volume (CBV) is an important parameter for the assessment of brain tumors, usually obtained using dynamic susceptibility contrast (DSC) MRI. However, this method often suffers from low spatial resolution and high sensitivity to susceptibility artifacts and usually does not take into account the effect of tissue permeability. The plasma volume (v p) can also be extracted from dynamic contrast enhancement (DCE) MRI. The aim of this study was to investigate whether DCE can be used for the measurement of cerebral blood volume in place of DSC for the assessment of patients with brain tumors. Twenty-eight subjects (17 healthy subjects and 11 patients with glioblastoma) were scanned using DCE and DSC. v p and CBV values were measured and compared in different brain components in healthy subjects and in the tumor area in patients. Significant high correlations were detected between v p and CBV in healthy subjects in the different brain components; white matter, gray matter, and arteries, correlating with the known increased tissue vascularity, and within the tumor area in patients. This work proposes the use of DCE as an alternative method to DSC for the assessment of blood volume, given the advantages of its higher spatial resolution, its lower sensitivity to susceptibility artifacts, and its ability to provide additional information regarding tissue permeability.

BACKGROUND White matter hyperintensities (WMH) were shown to predict cognitive decline following stroke or transient ischemic attack (TIA). However, WMH are only one among other radiological markers of cerebral small vessel disease (SVD). OBJECTIVE The aim of this study was to determine whether adding other SVD markers to WMH improves prediction of post-stroke cognitive performances. METHODS Consecutive first-ever stroke or TIA patients (n = 266) from the Tel Aviv Acute Brain Stroke Cohort (TABASCO) study were enrolled. MRI scans were performed within seven days of stroke onset. We evaluated the relationship between cognitive performances one year following stroke, and previously suggested total SVD burden score including WMH, lacunes, cerebral microbleeds (CMB), and perivascular spaces (PVS). RESULTS Significant negative associations were found between WMH and cognition (p

This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.

Background: Patients with Parkinson’s disease have reduced gray matter volume and fractional anisotropy in both cortical and sub-cortical structures, yet changes in the pre-motor phase of the disease are unknown. Methods: A comprehensive imaging study using voxelbased morphometry and diffusion tensor imaging tract-based spatial statistics analysis was performed on 64 Ashkenazi Jewish asymptomatic first degree relatives of patients with Parkinson’s disease (30 mutation carriers), who carry the G2019S mutation in the leucine-rich repeat kinase 2 (LRRK2) gene. Results: No between-group differences in gray matter volume could be noted in either whole-brain or volume-of-interest analysis. Diffusion tensor imaging analysis did not identify group differences in white matter areas, and volume-of-interest analysis identified no differences in diffusivity parameters in Parkinson’s disease-related structures. Conclusions: G2019S carriers do not manifest changes in gray matter volume or diffusivity parameters in Parkinson’s disease-related structures prior to the appearance of motor symptoms. V C 2014 Interna

Volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require the delineation of the PN boundaries, which is mostly impractical in the daily clinical setup. Accurate volumetric measurements are seldom performed for these tumors mainly due to their great dispersion, size and multiple locations. This paper presents a semiautomatic method for segmentation of PN from STIR MRI scans.Plexiform neurofibroma interactive segmentation tool (PNist) is a new tool to segment PNs in STIR MRI scans. The method is based on histogram tumor models computed from a training set.Experimental results from 28 datasets show an average absolute volume difference of 6.8 % with an average user time of approximately 7 min versus more than 13 min with manual delineation. In complex cases, the PNist user time is less than half in compared to state-of-the-art tools.PNist is a new method for the semiautomatic segmentation of PN lesions. Its simplicity and reliability make it unique among other state-of-the-art methods. It has the potential to become a clinical tool that allows the reliable evaluation of PN burden and progression.

a b s t r a c t Background: Lesion size in fluid attenuation inversion recovery (FLAIR) images is an important clinical parameter for patient assessment and follow-up. Although manual delineation of lesion areas consid- ered as ground truth, it is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this study, an automatic methodology for FLAIR lesion segmentation is proposed, and its application in patients with brain tumors undergoing therapy; and in patients following stroke is demonstrated. Materials and methods: FLAIR lesion segmentation was performed in 57 magnetic resonance imaging (MRI) data sets obtained from 44 patients: 28 patients with primary brain tumors; 5 patients with recurrent- progressive glioblastoma (rGB) who were scanned longitudinally during anti-angiogenic therapy (18 MRI scans); and 11 patients following ischemic stroke. Results: FLAIR lesion segmentation was obtained in all patients. When compared to manual delineation, a high visual similarity was observed, with an absolute relative volume difference of 16.80% and 20.96% and a volumetric overlap error of 24.87% and 27.50% obtained for two raters: accepted values for automatic methods. Quantitative measurements of the segmented lesion volumes were in line with qualitative radiological assessment in four patients who received anti-anogiogenic drugs. In stroke patients the proposed methodology enabled identification of the ischemic lesion and differentiation from other FLAIR hyperintense areas, such as pre-existing disease. Conclusion: This study proposed a replicable methodology for FLAIR lesion detection and quantification and for discrimination between lesion of interest and pre-existing disease. Results from this study show the wide clinical applications of this methodology in research and clinical practice. © 2013 Published by Elsevier Ireland Ltd.

The goal of the present study was to investigate whether spontaneous functional recovery following insult to the language-dominant hemisphere continues in the so-called “chronic stage,” and if so, to examine its neuro-functional correlates. We used a longitudinal functional magnetic resonance imaging (fMRI) block design, where each young patient served as his/her own control. Specifically, we examined whether language functions differed significantly in two monitoring sessions conducted years apart, both in the chronic stage, where almost no functional changes are expected. We focused on a unique cohort of young brain damaged patients with aphasiogenic lesions occurring after normal language acquisition, in order to maximize the potential of plasticity for language reorganization following brain damage. The most striking finding was that the linguistic recovery of our patients was significant not just relative to their linguistic scores on initial testing (T1), but also in absolute terms, relative to the respective age-matched normal population. Such improvement, therefore, cannot be simply attributed to the natural process of development. Overall, we found that right hemisphere (RH) activation was associated with better recovery in the chronic stage. Our longitudinal findings may challenge the view of recovery as ending within the first year following onset, suggesting that the RH may provide the substrate for ongoing plasticity in the damaged brain.

Normal brain cells depend on glucose metabolism, yet they have the flexibility to switch to the usage of ketone bodies during caloric restriction. In contrast, tumor cells lack genomic and metabolic flexibility and are largely dependent on glucose. Ketogenic-diet (KD) was suggested as a therapeutic option for malignant brain cancer. This study aimed to detect metabolic brain changes in patients with malignant brain gliomas on KD using proton magnetic-resonance-spectroscopy (

Optic pathway gliomas (OPGs) are diagnosed based on typical MR features and require careful monitoring with serial MRI. Reliable, serial radiological comparison of OPGs is a difficult task, where accuracy becomes very important for clinical decisions on treatment initiation and results. Current radiological methodology usually includes linear measurements that are limited in terms of precision and reproducibility.We present a method that enables semiautomated segmentation and internal classification of OPGs using a novel algorithm. Our method begins with co-registration of the different sequences of an MR study so that T1 and T2 slices are realigned. The follow-up studies are then re-sliced according to the baseline study. The baseline tumor is segmented, with internal components classified into solid non-enhancing, solid-enhancing, and cystic components, and the volume is calculated. Tumor demarcation is then transferred onto the next study and the process repeated. Numerical values are correlated with clinical data such as treatment and visual ability.We have retrospectively implemented our method on 24 MR studies of three OPG patients. Clinical case reviews are presented here. The volumetric results have been correlated with clinical data and their implications are also discussed.The heterogeneity of OPGs, the long course, and the young age of the patients are all driving the demand for more efficient and accurate means of tumor follow-up. This method may allow better understanding of the natural history of the tumor and provide a more advanced means of treatment evaluation.

Characterizing and mapping the relationship between neuronal reorganization and functional recovery are essential to the understanding of cerebral plasticity and the dynamic processes which occur following brain damage. The neuronal mechanisms underlying linguistic recovery following left hemisphere (LH) lesions are still unknown. Using functional magnetic resonance imaging (fMRI), we investigated whether the extent of brain lateralization of linguistic functioning in specific regions of interest (ROIs) is correlated with the level of linguistic performance following recovery from acquired childhood aphasia. The study focused on a rare group of children in whom lesions occurred after normal language acquisition, but prior to complete maturation of the brain. During fMRI scanning, rhyming, comprehension and verb generation activation tasks were monitored. The imaging data were evaluated with reference to linguistic performance measured behaviorally during imaging, as well as outside the scanner. Compared with normal controls, we found greater right hemisphere (RH) lateralization in patients. However, correlations with linguistic performance showed that increased proficiency in linguistic tasks was associated with greater lateralization to the LH. These results were replicated in a longitudinal case study of a patient scanned twice, 3 years apart. Additional improvement in linguistic performance of the patient was accompanied by increasing lateralization to the LH in the anterior language region. This, however, was the result of a decreased involvement of the RH. These findings suggest that recovery is a dynamic, ongoing process, which may last for years after onset. The role of each hemisphere in the recovery process may continuously change within the chronic stage.

INTRODUCTION: Pituitary adenomas are the third most common primary brain tumor in adults, with an incidence of 73–94/100000 cases. Most adenomas can be effectively resected using endoscopic endonasal approach, yet in about 10% of cases the tumor may be fibrous requiring a more complex procedure. Preoperative assessment of pituitary adenoma consistency and vascularity is thus important for surgical planning and risk assessment. The aim of this study was to assess pituitary macroadenoma consistency and vascularity using texture analysis of conventional MRI. METHODS: Retrospective analysis of pre-operative MRI data of 109 patients with pituitary adenoma was performed. Tumor consistency (hard vs soft) and vascularity (vascular vs avascular) were determined intraoperatively by the surgeon. Radiomics analysis was performed on T(2)-weighted (T(2)W) and post contrast T(1)-weighted (T(1)W+C) images, and apparent diffusion coefficient (ADC) maps (available in 57 patients). Fifteen features were extracted from the lesion area for each modality, including: mean, minimum, maximum, median, percentile 10,25,75,90, standard deviation, kurtosis, skewness, entropy, root mean square (RMS), and energy. Between group differences were tested using independent samples T-Test. RESULTS: Consistency: Significant group differences (p

Dynamic contrast enhanced (DCE) MRI using Tofts' model for estimating vascular permeability is widely accepted, yet inter-tissue differences in bolus arrival time (BAT) are generally ignored. In this work we propose a method, incorporating the BAT in the analysis, demonstrating its applicability and advantages in healthy subjects and patients. A method for DCE Up Sampled TEmporal Resolution (DUSTER) analysis is proposed which includes: baseline T1 map using DESPOT1 analyzed with flip angle (FA) correction; preprocessing; raw-signal-to-T1-to-concentration time curves (CTC) conversion; automatic arterial input function (AIF) extraction at temporal super-resolution; model fitting with model selection while incorporating BAT in the pharmacokinetic (PK) model, and fits contrast agent CTC while using exhaustive search in the BAT dimension in super-resolution. The method was applied to simulated data and to human data from 17 healthy subjects, six patients with glioblastoma, and two patients following stroke. BAT values were compared to time-to-peak (TTP) values extracted from dynamic susceptibility contrast imaging. Results show that the method improved the AIF estimation and allowed extraction of the BAT with a resolution of 0.8 s. In simulations, lower mean relative errors were detected for all PK parameters extracted using DUSTER compared to analysis without BAT correction (vp:5% vs. 20%, Ktrans: 9% vs. 24% and Kep: 8% vs. 17%, respectively), and BAT estimates demonstrated high correlations (r = 0.94, p < 1e− 10) with true values. In real data, high correlations between BAT values were detected when extracted from data acquired with high temporal resolution (2 s) and sub-sampled standard resolution data (6 s) (mean r = 0.85,p < 1e− 10). BAT and TTP values were significantly correlated in the different brain regions in healthy subjects (mean r = 0.72,p = < 1e− 3), as were voxel-wise comparisons in patients (mean r = 0.89, p < 1e− 10). In conclusion, incorporating BAT in DCE analysis improves estimation accuracy for the AIF and the PK parameters while providing an additional clinically important parameter.

Differentiation between treatment-related changes and progressive disease (PD) remains a major clinical challenge in the follow-up of patients with high grade brain tumors. The aim of this study was to differentiate between treatment-related changes and PD using dynamic contrast enhanced (DCE) MRI. Twenty patients were scanned using conventional, DCE-MRI and MR spectroscopy (total of 44 MR scans). The enhanced lesion area was extracted using independent components analysis of the DCE data. Pharmacokinetic parameters were estimated from the DCE data based on the Extended-Tofts-Model. Voxel based classification for treatment-related changes versus PD was performed in a patient-wise leave-one-out manner, using a support vector machine classifier. DCE parameters, K (trans), v e, k ep and v p, significantly differentiated between the tissue types. Classification results were validated using spectroscopy data showing significantly higher choline/creatine values in the extracted PD component compared to areas with treatment-related changes and normal appearing white matter, and high correlation between choline/creatine values and the percentage of the identified PD component within the lesion area (r = 0.77, p

Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans.The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors.The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI.The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Neuroplasticity studies examining children with hemiparesis (CH) have focused predominantly on unilateral interventions. CH also have bimanual coordination impairments with bimanual interventions showing benefits. We explored neuroplasticity following hand-arm bimanual intensive therapy (HABIT) of 60 hours in twelve CH (6 females, mean age 11 ± 3.6 y). Serial behavioral evaluations and MR imaging including diffusion tensor (DTI) and functional (fMRI) imaging were performed before, immediately after, and at 6-week follow-up. Manual skills were assessed repeatedly with the Assisting Hand Assessment, Children’s Hand Experience Questionnaire, and Jebsen-Taylor Test of Hand Function. Beta values, indicating the level of activation, and lateralization index (LI), indicating the pattern of brain activation, were computed from fMRI. White matter integrity of major fibers was assessed using DTI. 11/12 children showed improvement after intervention in at least one measure, with 8/12 improving on two or more tests. Changes were retained in 6/8 children at follow-up. Beta activation in the affected hemisphere increased at follow-up, and LI increased both after intervention and at follow-up. Correlations between LI and motor function emerged after intervention. Increased white matter integrity was detected in the corpus callosum and corticospinal tract after intervention in about half of the participants. Results provide first evidence for neuroplasticity changes following bimanual intervention in CH. This project was funded by grants from Guy’s and St Thomas’ Charity, Marnie Kimelman Trust and ILAN, the Israeli Association for Disabled children. Beit Issie Shapiro funded and provided the camp venue. D. Green was supported by a grant from the Department of Immigration and Absorption during 2010-2011.

Purpose: To evaluate the sensitivity of high b value diffusion weight magnetic resonance imaging (DWI) in detecting normal white matter maturation, compare it to conventional diffusion tensor imaging (DTI), and to obtain normative quantitative data using this method. Materials and Methods: High b value DWI (bmax 6000 sec/mm 2 ) using q-space analysis and conventional DTI (b 1000 sec/mm 2 ) were performed on 36 healthy subjects aged 4 months to 23 years. Fractional-anisotropy (FA), apparent-displacement, and apparent-probability values were measured in all slices and in six regions of interest (ROIs) of large fiber tracks. Values were correlated with each other and with age using regression analysis. Results: FA, displacement, and probability indices from all slices were highly correlated with each other (r 0.87, P 0.0001) and with age (r 0.82, P 0.0001). All age-related changes in the six pre-determined ROIs were best fitted by mono-exponential functions. Changes in the splenium extended to a later age when compared with the genu of the corpus-callosum, while the centrum semi-ovale demonstrated the latest changes with age. Conclusions: High b-value DWI and DTI showed changes in white matter from infancy through adulthood. However, high b-value detects a signal that is likely to originate mainly from the intra-axonal water population, and thus may represent different aspects of development and different sensitivity to pathology.

Object. The role of functional magnetic resonance (fMR) imaging has become increasingly important in the presurgical mapping of gray matter. Neurosurgical interventions often involve fiber bundles that connect critical functional areas. Recently, diffusion-tensor (DT) imaging has enabled the visualization of fiber bundle direction and integrity, thus providing the ability to delineate clearly white matter from gray matter tissue. The main objective of this study was to improve the presurgical assessment of critical functionality in the vicinity of brain lesions by combining DT and fMR imaging methodologies. Methods. Twenty patients with various space-occupying brain lesions underwent imaging for presurgical evaluation of motor and/or somatosensory functions. The authors focus on five patients with diverse space-occupying brain lesions. Diffusion tensor—based fiber tracking and fMR imaging activation maps were superimposed in three dimensions to visualize pyramidal tracts corresponding to motor and somatosensory regional activation. Conclusions. The combination of DT and fMR imaging for presurgical functional brain mapping provides valuable information that cannot be extracted using either method alone. The validity and sensitivity of noninvasive functional mapping for surgical guidance could be improved by considering results obtained with both methods. Furthermore, the use of three-dimensional visualization seems crucial and unique for viewing and understanding the complicated spatial relationship among the lesion, gray matter activation, and white matter fiber bundles.

The functional anatomy of syntactic transformations, a major computational operation invoked in sentence processing, was identified through a functional magnetic resonance imaging investigation. A grammaticality judgment task was used, presented through a novel hidden-blocks design. Subjects listened to transformational and non-transformational sentences in which a host of other complexity generators (number of words, prepositions, embeddings, etc.) were kept constant. A series of analyses revealed that the neural processing of transformations is localizable, evoking a highly lateralized and localized activation in the left inferior frontal gyrus (Broca's region) and bilateral activation in the posterior superior temporal sulcus. The pattern of activation associated with transformational analysis was distinct from the one observed in neighboring regions, and anatomically separable from the effects of verb complexity, which yielded significant activation in the left posterior superior temporal sulcus. Taken together with neuropsychological evidence, these results uncover the neural reality of syntactic transformations.

To what extent does emotional traumatic context affect sensory processing in the brain? A striking example of emotional impact on sensation is manifested in posttraumatic stress disorder (PTSD), in which a severe emotional trauma produces recurrent and vivid unpleasant sensory recollections. Here we report on an fMRI study exploring the sensory processing of trauma-related pictures in the visual cortex and amygdala in respect to PTSD. The impact of traumatic experience on brain responses was tested in relation to stimuli content and its level of recognition in a parametric factorial design. Twenty combat veterans, 10 with and 10 without PTSD, viewed backward-masked images of combat and noncombat content, presented at below, near, and above recognition thresholds. The response to combat content evoked more activation in the visual cortex in PTSD subjects than in non-PTSD subjects, only when images were presented at below recognition threshold. By contrast, the amygdala demonstrated increased activation in PTSD subjects irrespective of content and recognition threshold of the images. These intriguing findings are compatible with the notion that in PTSD, emotional traumatic experience could modify visual processing already at the preattentive level. On the other hand, lack of content specificity in the amygdala point to a possible predisposed mechanism for pathological processing of traumatic experience. The differential sensitivity of the amygdala and visual cortex to traumatic context implies distinct roles of limbic and sensory regions in the registration and recollection of emotional experience in the brain.

High b value diffusion weighted magnetic resonance imaging (high-b DWI) was used to characterize white matter changes in the brain of patients with vascular dementia (VaD). Hyperintense white matter areas detected by T2-weighted magnetic resonance imaging (MRI) represent lesions, also termed leukoaraiosis that are very common in VaD as well as in other types of dementia. Therefore, the role of white matter changes in the cognitive and memory decline that occurs in VaD patients is still under debate. High-b DWI, analyzed using the q-space approach, is a more sensitive MRI method for detection of white matter changes. High-b DWI revealed massive white matter loss in VaD patients that surpassed the boundaries of T2 hyperintensities. This technique, therefore, might serve as a better indication for the extensive nerve fiber loss in the white matter that is caused by vascular disease.

An important characteristic of visual perception is the fact that object recognition is largely immune to changes in viewing conditions. This invariance is obtained within a sequence of ventral stream visual areas beginning in area V1 and ending in high order occipito-temporal object areas (the lateral occipital complex, LOC). Here we studied whether this transformation could be observed in the contrast response of these areas. Subjects were presented with line drawings of common objects and faces in five different contrast levels (0, 4, 6, 10, and 100%). Our results show that indeed there was a gradual trend of increasing contrast invariance moving from area V1, which manifested high sensitivity to contrast changes, to the LOC, which showed a significantly higher degree of invariance at suprathreshold contrasts (from 10 to 100%). The trend toward increased invariance could be observed for both face and object images; however, it was more complete for the face images, while object images still manifested substantial sensitivity to contrast changes. Control experiments ruled out the involvement of attention effects or hemodynamic “ceiling” in producing the contrast invariance. The transition from V1 to LOC was gradual with areas along the ventral stream becoming increasingly contrast-invariant. These results further stress the hierarchical and gradual nature of the transition from early retinotopic areas to high order ones, in the build-up of abstract object representations.

Identification of early changes in Dopamine-Transporter (DaT) SPECT imaging expected in the prodromal phase of Parkinson’s disease (PD), are usually overlooked. Carriers of the G2019S LRRK2 mutation are known to be at high risk for developing PD, compared to non-carriers. In this work we aimed to study early changes in Dopamine uptake in non-manifesting PD carriers (NMC) of the G2019S LRRK2 mutation using quantitative DaT-SPECT analysis and to examine the potential for early prediction of PD. Eighty Ashkenazi-Jewish subjects were included in this study: eighteen patients with PD; thirty-one NMC and thirty-one non-manifesting non-carriers (NMNC). All subjects underwent a through clinical assessment including evaluation of motor, olfactory, affective and non-motor symptoms and DaT-SPECT imaging. A population based DaT-SPECT template was created based on the NMNC cohort, and data driven volumes-of-interest (VOIs) were defined. Comparisons between groups were performed based on VOIs and voxel-wise analysis. The striatum area of all three cohorts was segmented into four VOIs, corresponding to the right/left dorsal and ventral striatum. Significant differences in clinical measures were found between patients with PD and non-manifesting subjects with no differences between NMC and NMNC. Significantly lower uptake (p

Gamma knife radiosurgery (GK-RS) is a technique applied in selected cases of mesial-temporal epilepsy, although still limited to centres with adequate instrumentation and expertise. Here, we report a case of radio surgery targeted with the aid of electrical source imaging that localizes the cortical area generating the scalp epileptic discharges. The patient, a 39-year-old male, presented with a right mesio-temporal lesion; electrical source imaging localization partially overlapped with the lesional area but showed an important activation of the omolateral frontal area, concordant with the epileptic network. The patient underwent GK-RS, with good neurosurgical and clinical results. A radiosurgical ellipsoidal treatment volume area of 2 × 2 × 2 cm³, located over the right temporo-mesial region within a centre showing abnormal signal intensity, was considered. Seven months after treatment, the patient developed brain oedema that gradually resolved after one year. After three years of follow-up, the patient was seizure-free (Engel class I). Our very preliminary experience suggests that electrical source imaging appears to be a useful supporting tool for the definition of the radiosurgical treatment volume in selected patients with temporo-mesial lesional epilepsy.

Due to fundamental characteristics of MRI that limit scan speedup, sub-sampling techniques such as compressed sensing (CS) have been developed for rapid MRI. Current CS MRI approaches utilize sparsity of the image in the wavelet or other transform domains to speed-up acquisition. Another drawback of MRI is its poor signal-to-noise ratio (SNR), which is proportional to the image slice thickness. In this paper, we use the difference between adjacent slices as the sparse domain for CS MRI. We propose to acquire thick MRI slices and to reconstruct the thin slices from the thick slices' data, utilizing the similarity between adjacent thin slices. The acquisition of thick slices, instead of thin ones, improves the total SNR of the reconstructed image. Experimental results show that the image reconstruction quality of the proposed method outperforms existing CS MRI methods using the same number of measurements.

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) which affects nearly one million people worldwide, leading to a progressive decline of motor and sensory functions, and permanent disability. High b-value diffusion-weighted MR images (b of up to 14000 s/mm 2 ) were acquired from the brains of controls and MS patients. These diffusion MR images, in which signal decay is not monoexponential, were analyzed using the q-space approach that emphasizes the diffusion characteristics of the slow-diffusing component. From this analysis, displacement and probability maps were constructed. The computed q-space analyzed MR images that were compared with conventional T1, T2 (fluid attenuated inversion recovery (FLAIR)), and diffusion tensor imaging (DTI) images were found to be sensitive to the pathophysiological state of white matter. The indices used to construct this qspace analyzed MR maps, provided a pronounced differentiation between normal tissue and tissues classified as MS plaques by the FLAIR images. More importantly, a pronounced differentiation was also observed between tissues classified by the FLAIR MR images as normal-appearing white matter (NAWM) in the MS brains, which are known to be abnormal, and the respective control tissues. The potential diagnostic capacity of high b-value diffusion q-space analyzed MR images is discussed, and experimental data that explains the consequences of using the q-space approach once the short pulse gradient approximation is violated are presented. Magn Reson Med 47:115‐126, 2002. © 2002 Wiley-Liss, Inc.

How are objects represented in the human visual cortex? Two conflicting theories suggest either a holistic representation, in which objects are represented by a collection of object templates, or a part-based representation, in which objects are represented as collections of features or object parts. We studied this question using a gradual object-scrambling paradigm in which pictures of objects (faces and cars) were broken in a stepwise manner into an increasing number of blocks. Our results reveal a hierarchical axis oriented anterior--posteriorly in the organization of ventral object-areas. Along this axis, representations are arranged in bands of increasing sensitivity to image scrambling. The axis starts in early visual areas through retinotopic areas V4/V8 and continues into the lateral-occipital sulcus dorsally and the posterior fusiform girus ventrally, corresponding together to the previously described object-related lateral occipital complex (LOC). Regions showing the highest sensitivity to scrambling tended to be located at the most anterior-lateral regions of the complex. In these more anterior regions, breaking the images into 16 parts produced a significant reduction in activation. Interestingly, activation was not affected when images were cut in two halves, either horizontally or vertically. Car images generally produced a weaker activation compared to faces in the lateral occipital complex but showed the same tendency of increased scrambling sensitivity along the anterior--posterior axis. These results suggest the existence of a hierarchical axis along ventral occipito-temporal object-areas, in which the neuronal properties shift from sensitivity to local object features to a more global and holistic representation.

Background Treatment with bevacizumab is associated with substantial radiologic response in patients with glioblastoma (GB). However, following this initial response, changes in T 2 -weighted MRI signal may develop, suggesting an infiltrative pattern of tumor progression. The aim of this study was to differentiate between vasogenic-edema versus tumor-infiltrative area in GB patients. Methods and materials Fourteen patients with GB were longitudinally scanned, before and during intravenous bevacizumab therapy (5/10mg/kg every 2-weeks). A total of 40 MR scans including conventional, diffusion, dynamic susceptibility contrast, dynamic contrast enhancement imaging, and MR-spectroscopy (MRS) were analyzed. Classification of non-enhancing fluid-attenuation-inversion-recovery (FLAIR) area was performed based on mean diffusivity, cerebral blood volume and flow maps, and further characterized using multiple MRI parameters. Results The non-enhancing FLAIR lesion area was classified into: vasogenic-edema , characterized by reduced perfusion and increased FLAIR values; or tumor-infiltrative area, characterized by increased perfusion. Tumor-infiltrative area demonstrated a higher malignant pattern on MRS compared to areas of vasogenic-edema. Substantial reductions of the enhanced T 1 -weighted (58±10%) and hyperintense FLAIR (53±9%) lesion volumes were detected mainly during the first weeks of therapy, with a shift to an infiltrative pattern of tumor progression thereafter, as detected by an increase in tumor-infiltrative area in the majority of patients, which correlated with progression-free survival (week 8: r =−0.86, p =0.003, week 16: r =−0.99, p =0.001). Conclusion Characterization of non-enhancing hyperintense FLAIR lesion area in GB patients can provide an MR-based biomarker, indicating a shift to an infiltrative progression pattern, and may improve therapy response assessment in patients following bevacizumab therapy.

Stroke imaging studies during the acute phase are likely to precede several vascular brain mechanisms, which have an important role in patient outcome. The aim of this study was to identify within the lesion area during the subacute phase (≥1 day) reactive tissue, which may have the potential for recovery.Twenty seven stroke patients from two cohorts were included. MRI performed during the subacute phase included conventional, perfusion and diffusion imaging. In cohort I, unsupervised multiparametric classification of the lesion area was performed. In cohort II threshold based classification was performed during the subacute phase, and radiological outcome was assessed at follow-up scan.Three tissue classes were identified in cohort I, referred to as irreversibly damaged, intermediary, and reactive tissue. Based on threshold values defined in cohort I, the reactive tissue was identified in 11/13 patients in cohort II, and showed tissue preservation/partial recovery in 9/11 patients at follow-up scan. The irreversibly damaged tissue was identified in 7/13 patients in cohort II, and predicted tissue necrosis in all cases.Identification of reactive tissue following stroke during the subacute phase can improve radiological assessment, contribute to the understanding of brain recovery processes and has implications for new therapeutic approaches.

To improve the calculation of T1 relaxation time from a set of variable flip-angle (FA) spoiled gradient recalled echo images.The proposed method: (a) uses a uniform weighting of all FAs, (b) takes into account global inaccuracies in the generation of the prescribed FAs by estimating the actual FAs, and (c) incorporates data-driven local B1 inhomogeneity corrections. The method was validated and its accuracy tested using simulated data, phantom, and in vivo experiments. Results were compared with existing analysis methods and to inversion recovery (IR). Consistency was assessed by means of repeated scans of two subjects. Reference values were obtained from eight healthy subjects from various brain regions and compared with literature values.The method accurately and consistently estimated T1 values in all cases. The method was more robust, in comparison with the standard method, to the choice of FA set; to inaccuracies in generation of the prescribed FAs (in simulated data, T1 estimation error was 12.1 ms versus 235.5 ms); demonstrated greater consistency (in vivo study showed interscan T1 difference of 80 ms versus 356 ms); and achieved a better agreement with IR on phantom (median absolute difference of 123.8 ms versus 790 ms). Reference T1 values were 883/801 ms for female/male in white matter and 1501/1349 ms in gray matter, within the range previously reported.The proposed method overcomes some inaccuracies in FA production, providing more accurate estimation of T1 values compared with standard methods, and is applicable for currently available data.

We present a new method for the estimation of the next brain MR scan in a longitudinal tumor follow-up study. Our method effectively incorporates information of the past scans in the time series to predict the future scan of the patient. Its advantages are that it requires no user intervention and does not assume any particular tumor growth model. Instead, the patient-specific tumor growth parameters are estimated individually from the past patient scans. To validate our method, we conducted an experimental study on four patients with Optic Path Gliomas (OPGs) and four patients with glioblastomas multiforma (GBM), each scanned at five time points. The tumor volumes in the predicted and actual future scans, both segmented by an expert radiologist, yield a mean volume overlap difference of 13.65% for the OPG patients, and 34.23% for the GBM patients.