Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI.

PURPOSE: One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. METHODS: Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. RESULTS: The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. CONCLUSION: Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma.

Bevacizumab, an antibody against vascular endothelial growth factor (VEGF), is a promising, yet controversial, drug in human glioblastoma treatment (GBM). Its effects on tumor burden, recurrence, and vascular physiology are unclear. We therefore determined the tumor response to bevacizumab at the phenotypic, physiological, and molecular level in a clinically relevant intracranial GBM xenograft model derived from patient tumor spheroids. Using anatomical and physiological magnetic resonance imaging (MRI), we show that bevacizumab causes a strong decrease in contrast enhancement while having only a marginal effect on tumor growth. Interestingly, dynamic contrast-enhanced MRI revealed a significant reduction of the vascular supply, as evidenced by a decrease in intratumoral blood flow and volume and, at the morphological level, by a strong reduction of large- and medium-sized blood vessels. Electron microscopy revealed fewer mitochondria in the treated tumor cells. Importantly, this was accompanied by a 68% increase in infiltrating tumor cells in the brain parenchyma. At the molecular level we observed an increase in lactate and alanine metabolites, together with an induction of hypoxia-inducible factor 1α and an activation of the phosphatidyl-inositol-3-kinase pathway. These data strongly suggest that vascular remodeling induced by anti-VEGF treatment leads to a more hypoxic tumor microenvironment. This favors a metabolic change in the tumor cells toward glycolysis, which leads to enhanced tumor cell invasion into the normal brain. The present work underlines the need to combine anti-angiogenic treatment in GBMs with drugs targeting specific signaling or metabolic pathways linked to the glycolytic phenotype.

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling.

PURPOSE: Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS: The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS: The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION: We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Dynamic contrast-enhanced MRI in endometrial carcinoma identifies patients at increased risk of recurrence.

OBJECTIVES: To study the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessment of tumour microvasculature in endometrial carcinoma patients, and to explore correlations with histological subtype, clinical course and microstructural characteristics based on apparent diffusion coefficient (ADC) values. METHODS: Diffusion-weighted imaging (DWI) and three-dimensional DCE-MRI (1.5 T) with high temporal resolution (2.49 s) were acquired preoperatively in 55 patients. Quantitative modelling allowed the calculation of four independent parameters describing microvasculature: blood flow (Fb), extraction fraction (E), capillary transit time (Tc) and transfer constant from the extravascular extracellular space [EES] to blood (Kep); and four derived parameters: blood volume (Vb), volume of EES (Ve), capillary permeability surface area product (PS) and transfer from blood to EES (Ktrans). RESULTS: Endometrial carcinoma tissue exhibited reduced Fb, E, Vb, Ve, PS and Ktrans compared with normal myometrium. Non-endometrioid carcinomas (n = 12) had lower Fb, and E than endometrioid carcinomas (n = 43; P < 0.05). Tumour Ve positively correlated with tumour ADC value (r = 0.29, P = 0.03). Reduced survival was observed in patients with low tumour Fb and high tumour Tc (P < 0.05). CONCLUSIONS: We demonstrate the feasibility of DCE-MRI in reflecting histological subtype and clinical course in primary endometrial carcinomas. DCE-MRI may potentially provide future biomarkers for preoperative risk stratification in endometrial carcinomas. KEY POINTS: • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers new information about endometrial carcinoma. • Pelvic DCE-MRI with subsequent quantitative modelling seems feasible in endometrial carcinoma patients. • Low tumour perfusion is a feature of a more aggressive tumour subtype. • DCE-MRI provides potential biomarkers for preoperative risk stratification in endometrial carcinoma patients.

Single voxel vascular transport functions of arteries, capillaries and veins; and the associated arterial input function in dynamic susceptibility contrast magnetic resonance brain perfusion imaging.

PURPOSE: The composite vascular transport function of a brain voxel consists of one convolutional component for the arteries, one for the capillaries and one for the veins in the voxel of interest. Here, the goal is to find each of these three convolutional components and the associated arterial input function. PHARMACOKINETIC MODELLING: The single voxel vascular transport functions for arteries, capillaries and veins were all modelled as causal exponential functions. Each observed multipass tissue contrast function was as a first approximation modelled as the resulting parametric composite vascular transport function convolved with a nonparametric and voxel specific multipass arterial input function. Subsequently, the residue function was used in the true perfusion equation to optimize the three parameters of the exponential functions. DECONVOLUTION METHODS: For each voxel, the parameters of the three exponential functions were estimated by successive iterative blind deconvolutions using versions of the Lucy-Richardson algorithm. The final multipass arterial input function was then computed by nonblind deconvolution using the Lucy-Richardson algorithm and the estimated composite vascular transport function. RESULTS: Simulations showed that the algorithm worked. The estimated mean transit time of arteries, capillaries and veins of the simulated data agreed with the known input values. For real data, the estimated capillary mean transit times agreed with known values for this parameter. The nonparametric multipass arterial input functions were used to derive the associated map of the arrival time. The arrival time map of a healthy volunteer agreed with known arterial anatomy and physiology. CONCLUSION: Clinically important new voxelwise hemodynamic information for arteries, capillaries and veins separately can be estimated using multipass tissue contrast functions and the iterative blind Lucy-Richardson deconvolution algorithm.

Voxel-specific brain arterial input functions from dynamic susceptibility contrast MRI and blind deconvolution in a group of healthy males.

BACKGROUND: Arterial input functions may differ between brain regions due to delay and dispersion effects in the vascular supply network. Unless corrected for, these differences may degrade quantitative estimations of cerebral blood flow in dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI). PURPOSE: To investigate in a healthy population (n=44) the properties of voxel-specific arterial input functions that were obtained using a recently published blind estimation approach. MATERIAL AND METHODS: The voxel-specific arterial input functions were qualitatively and quantitatively assessed, through visual inspection or by comparing time-to-peak (delays) and peak amplitude (dispersion) values between eight regions of the brain. Furthermore, they were compared to arterial input functions selected manually in the middle cerebral artery (MCA), where normally no delay or dispersion of the contrast agent was expected. RESULTS: The estimated voxel-specific arterial input functions varied between brain regions. Differences in delays and dispersion were larger within one brain region among all participants than between regions in one participant. A good correlation was typically found between the estimated voxel-specific arterial input functions and the manually selected arterial input functions in the MCA region. CONCLUSION: Given knowledge of neurovascular anatomy, the current blind approach seemingly produced reasonable estimates of voxel-specific arterial input functions. In addition to potentially reducing quantification errors in DSC-MRI, these user-independent voxel-specific arterial input functions could be useful for visualizing abnormal blood supply patterns in patients.

Cerebral diffusion and perfusion deficits in North Sea divers.

BACKGROUND: Diving is associated with a risk of cerebral decompression illness, and the prevalence of neurological symptoms is higher in divers compared with control groups. Microvascular dysfunction due to gas microembolism and exposure to hyperoxia are possible mechanisms, which may result in cerebral diffusion and perfusion deficits. PURPOSE: To investigate if possible functional derangements of the microvasculature and microstructure would be more prevalent among symptomatic divers. MATERIAL AND METHODS: Magnetic resonance imaging (MRI) was performed in 91 former divers and 45 controls. Individual parametric images of apparent diffusion coefficient (ADC), cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) were generated on the basis of diffusion- and perfusion-weighted imaging. To identify regions with statistically significant differences between groups (P < 0.05, corrected for false discovery rate), voxel-wise ANCOVA analysis was performed for each of the four parametric images. RESULTS: Significant regional group differences were found in all four parametric comparisons. Gross regional ADC differences were seen throughout the brain, including large frontal and temporal white-matter regions, the hippocampus, and parts of the cerebellum. Differences in the perfusion maps were localized in fewer and smaller clusters, including parts of the cerebellum, the putamen, and the anterior watershed regions. CONCLUSION: Regional functional abnormalities as measured by diffusion- and perfusion-weighted imaging were identified in the divers, and there was a partial co-localization of the regions identified in the perfusion and the diffusion images. The findings may explain some of the long-term clinical symptoms reported among professional divers.

Multispectral analysis of multimodal images.

INTRODUCTION: An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. MATERIALS AND METHODS: We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. RESULTS: The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentations that seem to be sensible. DISCUSSION: The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

Epac1-/- mice have elevated baseline permeability and do not respond to histamine as measured with dynamic contrast-enhanced magnetic resonance imaging with contrast agents of different molecular weights.

AIM: Epac1-/- mice, but not Epac2-/- mice have elevated baseline permeability to albumin. This study extends the investigations of how Epac-dependent pathways modulate transvascular exchange in response to the classical inflammatory agent histamine. It also evaluates the limitations of models of blood-to-tissue exchange in transgenic mice in DCE-MRI measurements. METHODS: We measured DCE-MRI signal intensity in masseter muscle of wt and Epac1-/- mice with established approaches from capillary physiology to determine how changes in blood flow and vascular permeability contribute to overall changes of microvascular flux. We used two tracers, the high molecular weight tracer (Gadomer-17, MW 17 kDa, apparent MW 30-35 kDa) is expected to be primarily limited by diffusion and therefore less dependent on changes in blood flow and the low molecular weight tracer (Dotarem (MW 0.56 kDa) whose transvascular exchange is determined by both blood flow and permeability. Paired experiments in each animal combined with analytical methods provided an internally consistent description of microvascular transport. RESULTS: Epac1-/- mice had elevated baseline permeability relative to wt control mice for Dotarem and Gadomer-17. In contrast to wt mice, Epac1-/- mice failed to increase transvascular permeability in response to histamine. Dotarem underestimated blood flow and vascular volume and Gadomer-17 has limited sensitivity in extravascular accumulation. CONCLUSION: The study suggests that the normal barrier loosening effect of histamine in venular microvessels do not function when the normal barrier tightening effect of Epac1 is already compromised. The study also demonstrated that the numerical analysis of DCE-MRI data with tracers of different molecular weight has significant limitations.

Blind deconvolution estimation of an arterial input function for small animal DCE-MRI.

PURPOSE: One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution. METHODS: A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution. RESULTS: Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight. CONCLUSION: Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

Pancreatic lipomatosis is a structural marker in nondiabetic children with mutations in carboxyl-ester lipase.

Both pancreatic volume reduction and lipomatosis have been observed in subjects with diabetes. The underlying molecular and pathological mechanisms are, however, poorly known, and it has been speculated that both features are secondary to diabetes. We have recently described pancreatic atrophy and lipomatosis in diabetic subjects of two Norwegian families with a novel syndrome of diabetes and exocrine pancreatic dysfunction caused by heterozygous carboxyl-ester lipase (CEL) mutations. To explore the early pathological events in this syndrome, we performed radiological examinations of the pancreas in nondiabetic mutation carriers with signs of exocrine dysfunction. In a case series study at a tertiary hospital, we evaluated 11 nondiabetic and mutation-positive children with fecal elastase deficiency and 11 age- and sex-matched control subjects using ultrasound and magnetic resonance imaging (MRI) to estimate pancreatic fat content. The pancreata of nondiabetic mutation carriers exhibited increased reflectivity on ultrasound and had MRI findings indicative of lipomatosis. Apparently, carriers of heterozygous CEL mutations accumulate fat in their pancreas before the anticipated development of diabetes. Our findings suggest that lipomatosis of the pancreas reflects early events involved in the pathogenesis of diabetes and exocrine pancreatic dysfunction syndrome.

Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice.

OBJECTIVE: An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS: The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS: In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION: When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE: This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.

Lack of functional normalisation of tumour vessels following anti-angiogenic therapy in glioblastoma.

Neo-angiogenesis represents an important factor for the delivery of oxygen and nutrients to a growing tumour, and is considered to be one of the main pathodiagnostic features of glioblastomas (GBM). Anti-angiogenic therapy by vascular endothelial growth factor (VEGF) blocking agents has been shown to lead to morphological vascular normalisation resulting in a reduction of contrast enhancement as seen by magnetic resonance imaging (MRI). Yet the functional consequences of this normalisation and its potential for improved delivery of cytotoxic agents to the tumour are not known. The presented study aimed at determining the early physiologic changes following bevacizumab treatment. A time series of perfusion MRI and hypoxia positron emission tomography (PET) scans were acquired during the first week of treatment, in two human GBM xenograft models treated with either high or low doses of bevacizumab. We show that vascular morphology was normalised over the time period investigated, but vascular function was not improved, resulting in poor tumoural blood flow and increased hypoxia.

High-resolution ultrasonic imaging using fast two-dimensional homomorphic filtering.

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.

EGFRvIII mutations can emerge as late and heterogenous events in glioblastoma development and promote angiogenesis through Src activation.

BACKGROUND: Amplification of the epidermal growth factor receptor (EGFR) and its mutant EGFRvIII are among the most common genetic alterations in glioblastoma (GBM), the most frequent and most aggressive primary brain tumor. METHODS: In the present work, we analyzed the clonal evolution of these major EGFR aberrations in a small cohort of GBM patients using a unique surgical multisampling technique. Furthermore, we overexpressed both receptors separately and together in 2 patient-derived GBM stem cell lines (GSCs) to analyze their functions in vivo in orthotopic xenograft models. RESULTS: In human GBM biopsies, we identified EGFR amplification as an early event because EGFRvIII mutations emerge from intratumoral heterogeneity later in tumor development. To investigate the biological relevance of this distinct developmental pattern, we established experimental model systems. In these models, EGFR+ tumor cells showed activation of classical downstream signaling pathways upon EGF stimulation and displayed enhanced invasive growth without evidence of angiogenesis in vivo. In contrast, EGFRvIII+ tumors were driven by activation of the prototypical Src family kinase c-Src that promoted VEGF secretion leading to angiogenic tumor growth. CONCLUSIONS: The presented work shows that sequential EGFR amplification and EGFRvIII mutations might represent concerted evolutionary events that drive the aggressive nature of GBM by promoting invasion and angiogenesis via distinct signaling pathways. In particular, c-SRC may be an attractive therapeutic target for tumors harboring EGFRvIII as we identified this protein specifically mediating angiogenic tumor growth downstream of EGFRvIII.

Absolute ultrasound perfusion parameter quantification of a tissue-mimicking phantom using bolus tracking [Correspondence].

This study presents three methods for absolute quantification in ultrasound perfusion analysis based on bolus tracking. The first two methods deconvolve the perfusion time sequence with a measured AIF, using a nonparametric or a parametric model of the tissue residue function, respectively. The third method is a simplified approach avoiding deconvolution by assuming a narrow AIF. A phantom with a dialyzer filter as a tissue-mimicking model was used for evaluation. Estimated mean transit times and blood volumes were compared with the theoretical values. A match with a maximum error of 12% was achieved.

Two-dimensional phase unwrapping using robust derivative estimation and adaptive integration.

The adaptive integration (ADI) method for two-dimensional (2-D) phase unwrapping is presented. The method uses an algorithm for noise robust estimation of partial derivatives, followed by a noise robust adaptive integration process. The ADI method can easily unwrap phase images with moderate noise levels, and the resulting images are congruent modulo 2pi with the observed, wrapped, input images. In a quantitative evaluation, both the ADI and the BLS methods (Strand et al.) were better than the least-squares methods of Ghiglia and Romero (GR), and of Marroquin and Rivera (MRM). In a qualitative evaluation, the ADI, the BLS, and a conjugate gradient version of the MRM method (MRMCG), were all compared using a synthetic image with shear, using 115 magnetic resonance images, and using 22 fiber-optic interferometry images. For the synthetic image and the interferometry images, the ADI method gave consistently visually better results than the other methods. For the MR images, the MRMCG method was best, and the ADI method second best. The ADI method was less sensitive to the mask definition and the block size than the BLS method, and successfully unwrapped images with shears that were not marked in the masks. The computational requirements of the ADI method for images of nonrectangular objects were comparable to only two iterations of many least-squares-based methods (e.g., GR). We believe the ADI method provides a powerful addition to the ensemble of tools available for 2-D phase unwrapping.

Superresolution of ultrasound images using the first and second harmonic signal.

This paper presents a new method of blind two-dimensional (2-D) homomorphic deconvolution and speckle reduction applied to medical ultrasound images. The deconvolution technique is based on an improved 2-D phase unwrapping scheme for pulse estimation. The input images are decomposed into minimum-phase and allpass components. The 2-D phase unwrapping is applied only to the allpass component. The 2-D phase of the minimum-phase component is derived by a Hilbert transform. The accuracy of 2-D phase unwrapping is also improved by processing small (16 x 16 pixels) overlapping subimages separately. This takes the spatial variance of the ultrasound pulse into account. The deconvolution algorithm is applied separately to the first and second harmonic images, producing much sharper images of approximately the same resolution and different speckle patterns. Speckle reduction is made by adding the envelope images of the deconvolved first and second harmonic images. Neither the spatial resolution nor the frame rate decreases, as the common compounding speckle reduction techniques do. The method is tested on sequences of clinical ultrasound images, resulting in high-resolution ultrasound images with reduced speckle noise.

The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters.

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than~20dB. The application of the new analysis model on a real DCE-MRI data set is also presented.

Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies.

The vast majority of malignant gliomas relapse after surgery and standard radio-chemotherapy. Novel molecular and cellular therapies are thus being developed, targeting specific aspects of tumor growth. While histopathology remains the gold standard for tumor classification, neuroimaging has over the years taken a central role in the diagnosis and treatment follow up of brain tumors. It is used to detect and localize lesions, define the target area for biopsies, plan surgical and radiation interventions and assess tumor progression and treatment outcome. In recent years the application of novel drugs including anti-angiogenic agents that affect the tumor vasculature, has drastically modulated the outcome of brain tumor imaging. To properly evaluate the effects of emerging experimental therapies and successfully support treatment decisions, neuroimaging will have to evolve. Multi-modal imaging systems with existing and new contrast agents, molecular tracers, technological advances and advanced data analysis can all contribute to the establishment of disease relevant biomarkers that will improve disease management and patient care. In this review, we address the challenges of glioma imaging in the context of novel molecular and cellular therapies, and take a prospective look at emerging experimental and pre-clinical imaging techniques that bear the promise of meeting these challenges.