Imaging of Brain Tumors: Perfusion/Permeability

Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases

Background: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. Methods: Patients with histopathology-confirmed GBMs (n = 62) and BMs (n = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment. Median values of mean diffusivity (MD), fractional anisotropy, linear, planar and spheric anisotropic coefficients, and relative cerebral blood volume (rCBV) and maximum rCBV values were measured from CERs and immediate peritumor regions. Data normalization and scaling were performed. In the next step, most relevant features were extracted (non-interacting features), which were subsequently used to generate a set of new, innovative, high-order features (interacting features) using a feature engineering method. Finally, 10 machine learning classifiers were employed in distinguishing GBMs and BMs. Cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance. Results: A random forest classifier with ANOVA F-value feature selection algorithm using both interacting and non-interacting features provided the best diagnostic performance in distinguishing GBMs from BMs with an area under the ROC curve of 92.67%, a classification accuracy of 87.8%, a sensitivity of 73.64% and a specificity of 97.5%. Conclusions: A machine learning based approach involving the combined use of interacting and non-interacting physiological MRI parameters shows promise to differentiate between GBMs and BMs with high accuracy.

Perfusion MRI in the evaluation of brain metastases: current practice review and rationale for study of baseline MR perfusion imaging prior to stereotactic radiosurgery (STARBEAM-X)

Stereotactic radiosurgery is an established focal treatment for brain metastases with high local control rates. An important side-effect of stereotactic radiosurgery is the development of radionecrosis. On conventional MR imaging, radionecrosis and tumour progression often have similar appearances, but have contrasting management approaches. Perfusion MR imaging is often used in the post-treatment setting in order to help distinguish between the two, but image interpretation can be fraught with challenges.Perfusion MR plays an established role in the baseline and post-treatment evaluation of primary brain tumours and a number of studies have concentrated on the value of perfusion imaging in brain metastases. Of the parameters generated, relative cerebral blood volume is the most widely used variable in terms of its clinical value in differentiating between radionecrosis and tumour progression. Although it has been suggested that the relative cerebral blood volume tends to be elevated in active metastatic disease following treatment with radiosurgery, but not with treatment-related changes, the literature available on interpretation of the ratios provided in the context of defining tumour progression is not consistent.This article aims to provide an overview of the role perfusion MRI plays in the assessment of brain metastases and introduces the rationale for the STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion extra imaging), which will prospectively evaluate baseline perfusion imaging in brain metastases. We hope this will allow insight into the vascular appearance of metastases from different primary sites, and aid in the interpretation of post-treatment perfusion imaging.

Perfusion-weighted imaging in vestibular schwannoma: the influence that cystic status and tumor size have on perfusion profiles

Objective

The perfusion profile of vestibular schwannomas (VSs) and the factors that influence it have yet to be determined.

Materials and Methods

Twenty patients with sporadic VS were analyzed by calculating parameters related to the extravascular extracellular space (EES)-the volume transfer constant between a vessel and the EES (Ktrans); the EES volume per unit of tissue volume (Ve); and the rate transfer constant between EES and blood plasma (Kep)-as well as the relative cerebral blood volume (rCBV), and by correlating those parameters with the size of the tumor and its structure (solid, cystic, or heterogeneous).

Results

Although Ktrans, Ve, and Kep were measurable in all tumors, rCBV was measurable only in large tumors. We detected a positive correlation between Ktrans and rCBV (r = 0.62, p = 0.031), a negative correlation between Ve and Kep (r = -0.51, p = 0.021), and a positive correlation between Ktrans and Ve only in solid VSs (r = 0.64, p = 0.048). Comparing the means for small and large VSs, we found that the former showed lower Ktrans (0.13 vs. 0.029, p < 0.001), higher Kep (0.68 vs. 0.46, p = 0.037), and lower Ve (0.45 vs. 0.83, p < 0.001). The mean Ktrans was lower in the cystic portions of cystic VSs than in their solid portions (0.14 vs. 0.32, p < 0.001), as was the mean Ve (0.37 vs. 0.78, p < 0.001). There were positive correlations between the solid and cystic portions for Ktrans (r = 0.71, p = 0.048) and Kep (r = 0.74, p = 0.037).

Conclusion

In VS, tumor size appears to be consistently associated with perfusion values. In cystic VS, the cystic portions seem to have lower Ktrans and Ve than do the solid portions.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI

OBJECTIVES

To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.

METHODS

Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum.

RESULTS

Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.

CONCLUSION

Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.

ADVANCES IN KNOWLEDGE

The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: a comparison study

PURPOSE

To investigate the association and agreement between magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and computed tomography perfusion (CTP) in determining vascularity and permeability of primary and secondary brain tumors.

MATERIAL AND METHODS

DSC-PWI and CTP studies from 97 patients with high-grade glioma, low-grade glioma and solitary brain metastasis were retrospectively reviewed. Normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), capillary transfer constant (nK2) and permeability surface area product (nPS) values were obtained. Variables among groups were compared, and correlation and agreement between DSC-PWI and CTP were tested.

RESULTS

All DSC-PWI and CTP parameters were higher in high-grade than in low-grade gliomas (p < 0.01 and p < 0.001). Metastases had greater DSC-PWI nCBV (p < 0.05), nCTP-CBF (p < 0.05), nCTP-CBV (p < 0.01) and nCTP-PS (p < 0.0001) than low-grade gliomas and more elevated nCTP-PS (p < 0.01) than high-grade gliomas. The correlation was strong between DSC-PWI nCBF and CTP nCBF (r = 0.79; p < 0.00001) and between DSC-PWI nCBV and CTP nCBV (r = 0.83; p < 0.00001), weaker between DSC-PWI nK2 and CTP nPS (r = 0.29; p < 0.01). Bland-Altman plots indicated that the agreement was strong between DSC-PWI nCBF and CTP nCBF, good between DSC-PWI nCBV and CTP nCBV and poorer between DSC-PWI nK2 and CTP nPS.

CONCLUSION

DSC-PWI and CTP CBF and CBV maps were comparable and interchangeable in the assessment of tumor vascularity, unlike DSC-PWI K2 and CTP PS maps that were more discordant in the analysis of tumor permeability. CTP could be an alternative method to quantify tumor neoangiogenesis when MRI is not available or when the patient does not tolerate it.

Diffuse midline gliomas, H3 K27M-mutant are associated with less peritumoral edema and contrast enhancement in comparison to glioblastomas, H3 K27M-wildtype of midline structures

Purpose

The entity ‘diffuse midline glioma, H3 K27M-mutant (DMG)’ was introduced in the revised 4^th edition of the 2016 WHO classification of brain tumors. However, there are only a few reports on magnetic resonance imaging (MRI) of these tumors. Thus, we conducted a retrospective survey focused on MRI features of DMG compared to midline glioblastomas H3 K27M-wildtype (mGBM-H3wt).

Methods

We identified 24 DMG cases and 19 mGBM-H3wt patients as controls. After being retrospectively evaluated for microscopic evidence of microvascular proliferations (MVP) and tumor necrosis by two experienced neuropathologists to identify the defining histological criteria of mGBM-H3wt, the samples were further analyzed by two experienced readers regarding imaging features such as shape, peritumoral edema and contrast enhancement.

Results

The DMG were found in the thalamus in 37.5% of cases (controls 63%), in the brainstem in 50% (vs. 32%) and spinal cord in 12.5% (vs. 5%). In MRI and considering MVP, DMG were found to be by far less likely to develop peritumoral edema (OR: 0.13; 95%-CL: 0.02–0.62) (p = 0.010). They, similarly, were associated with a significantly lower probability of developing strong contrast enhancement compared to mGBM-H3wt (OR: 0.10; 95%-CL: 0.02–0.47) (P = 0.003).

Conclusion

Despite having highly variable imaging features, DMG exhibited markedly less edema and lower contrast enhancement in MRI compared to mGBM-H3wt. Of these features, the enhancement level was associated with evidence of MVP.

Clinical MR Biomarkers

Non-invasive magnetic resonance imaging (MRI) techniques are increasingly applied in the clinic with a fast growing body of evidence regarding its value for clinical decision making. In contrast to biochemical or histological markers, the key advantages of imaging biomarkers are the non-invasive nature and the spatial and temporal resolution of these approaches. The following chapter focuses on clinical applications of novel MR biomarkers in humans with a strong focus on oncologic diseases. These include both clinically established biomarkers (part 1-4) and novel MRI techniques that recently demonstrated high potential for clinical utility (part 5-7).

A potential non-invasive glioblastoma treatment: Nose-to-brain delivery of farnesylthiosalicylic acid incorporated hybrid nanoparticles

New drug delivery systems are highly needed in research and clinical area to effectively treat gliomas by reaching a high antineoplastic drug concentration at the target site without damaging healthy tissues. Intranasal (IN) administration, an alternative route for non-invasive drug delivery to the brain, bypasses the blood-brain-barrier (BBB) and eliminates systemic side effects. This study evaluated the antitumor efficacy of farnesylthiosalicylic acid (FTA) loaded (lipid-cationic) lipid-PEG-PLGA hybrid nanoparticles (HNPs) after IN application in rats. FTA loaded HNPs were prepared, characterized and evaluated for cytotoxicity. Rat glioma 2 (RG2) cells were implanted unilaterally into the right striatum of female Wistar rats. 10days later, glioma bearing rats received either no treatment, or 5 repeated doses of 500μM freshly prepared FTA loaded HNPs via IN or intravenous (IV) application. Pre-treatment and post-treatment tumor sizes were determined with MRI. After a treatment period of 5days, IN applied FTA loaded HNPs achieved a significant decrease of 55.7% in tumor area, equal to IV applied FTA loaded HNPs. Herewith, we showed the potential utility of IN application of FTA loaded HNPs as a non-invasive approach in glioblastoma treatment.

Quantitative Evaluation of Diffusion and Dynamic Contrast-Enhanced MR in Tumor Parenchyma and Peritumoral Area for Distinction of Brain Tumors

Purpose

To quantitatively evaluate the diagnostic efficiency of parameters from diffusion and dynamic contrast-enhanced MR which based on tumor parenchyma (TP) and peritumoral (PT) area in classification of brain tumors.

Methods

45 patients (male: 23, female: 22; mean age: 46 y) were prospectively recruited and they underwent conventional, DCE-MR and DWI examination. With each tumor, 10–15 regions of interest (ROIs) were manually placed on TP and PT area. ADC and permeability parameters (K^trans, Ve, Kep and iAUC) were calculated and their diagnostic efficiency was assessed.

Results

In TP, all permeability parameters and ADC value could significantly discriminate Low- from High grade gliomas (HGG) (p<0.001); among theses parameters, Ve demonstrated the highest diagnostic power (iAUC: 0.79, cut-off point: 0.15); the most sensitive and specific index for gliomas grading were K^trans (84%) and Kep (89%). While, in PT area, only K^trans could help in gliomas grading (P = 0.009, cut-off point: 0.03 min^-1). Moreover, in TP, mean Ve and iAUC of primary central nervous system lymphoma (PCNSL) and metastases were significantly higher than that in HGG (p<0.003). Further, in PT area, mean K^trans (p≤0.004) could discriminate PCNSL from HGG and ADC (p≤0.003) could differentiate metastases with HGG.

Conclusions

Quantitative ADC and permeability parameters from Diffusion and DCE-MR in TP and PT area, especially DCE-MR, can aid in gliomas grading and brain tumors discrimination. Their combined application is strongly recommended in the differential diagnosis of these tumor entities.

Advances in Magnetic Resonance Imaging and Positron Emission Tomography Imaging for Grading and Molecular Characterization of Glioma

In recent years, the management of glioma has evolved significantly, reflecting our better understanding of the underlying mechanisms of tumor development, tumor progression, and treatment response. Glioma grade, along with a number of underlying molecular and genetic biomarkers, has been recognized as an important prognostic and predictive factor that can help guide the management of patients. This article highlights advances in magnetic resonance imaging (MRI), including diffusion-weighted imaging, diffusion tensor imaging, magnetic resonance spectroscopy, dynamic contrast-enhanced imaging, and perfusion MRI, as well as position emission tomography using various tracers including methyl-(11)C-l-methionine and O-(2-(18)F-fluoroethyl)-l-tyrosine. Use of multiparametric imaging data has improved the diagnostic strength of imaging, introduced the potential to noninvasively interrogate underlying molecular features of low-grade glioma and to guide local therapies such as surgery and radiotherapy.

ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain

MR perfusion imaging is becoming an increasingly common means of evaluating a variety of cerebral pathologies, including tumors and ischemia. In particular, there has been great interest in the use of MR perfusion imaging for both assessing brain tumor grade and for monitoring for tumor recurrence in previously treated patients. Of the various techniques devised for evaluating cerebral perfusion imaging, the dynamic susceptibility contrast method has been employed most widely among clinical MR imaging practitioners. However, when implementing DSC MR perfusion imaging in a contemporary radiology practice, a neuroradiologist is confronted with a large number of decisions. These include choices surrounding appropriate patient selection, scan-acquisition parameters, data-postprocessing methods, image interpretation, and reporting. Throughout the imaging literature, there is conflicting advice on these issues. In an effort to provide guidance to neuroradiologists struggling to implement DSC perfusion imaging in their MR imaging practice, the Clinical Practice Committee of the American Society of Functional Neuroradiology has provided the following recommendations. This guidance is based on review of the literature coupled with the practice experience of the authors. While the ASFNR acknowledges that alternate means of carrying out DSC perfusion imaging may yield clinically acceptable results, the following recommendations should provide a framework for achieving routine success in this complicated-but-rewarding aspect of neuroradiology MR imaging practice.

Advances in pediatric neuroimaging

Conventional MRI protocols are an integral part of routine clinical imaging in pediatric patients. The advent of several newer MRI techniques provides crucial insight into the structural integrity and functional aspects of the developing brain, especially with the introduction of 3T MRI systems in clinical practice. The field of pediatric neuroimaging continues to evolve, with greater emphasis on high spatial resolution, faster scan time, as well as a quest for visualization of the functional aspects of the human brain. MR vendors are increasingly focusing on optimizing MR technology to make it suitable for children, in whom as compared to adults the head size is usually smaller and demonstrates inherent neuroanatomical differences relating to brain development. The eventual goal of these advances would be to evolve as potential biomarkers for predicting neurodevelopment outcomes and prognostication, in addition to their utility in routine diagnostic and therapeutic decision-making. Advanced MR techniques like diffusion tensor imaging, functional MRI, MR perfusion, spectroscopy, volumetric imaging and arterial spin labeling add to our understanding of normal brain development and pathophysiology of various neurological disease processes. This review is primarily focused on outlining advanced MR techniques and their current and potential pediatric neuroimaging applications as well as providing a brief overview of advances in hardware and machine design.

The role of magnetic resonance imaging in the management of brain metastases: diagnosis to prognosis

This article reviews the different MRI techniques available for the diagnosis, treatment and monitoring of brain metastases with a focus on applying advanced MR techniques to practical clinical problems. Topics include conventional MRI sequences and contrast agents, functional MR imaging, diffusion weighted MR, MR spectroscopy and perfusion MR. The role of radiographic biomarkers is discussed as well as future directions such as molecular imaging and MR guided high frequency ultrasound.

Assessment of irradiated brain metastases using dynamic contrast-enhanced magnetic resonance imaging

INTRODUCTION

The purpose of this study was to evaluate the effect of stereotactic radiosurgery (SRS) on cerebral metastases using the transfer constant (K trans) assessed by dynamic contrast-enhanced (DCE) MRI. Furthermore, we aimed to evaluate the ability of K trans measurements to predict midterm tumor outcomes after SRS.

METHODS

The study received institutional review board approval, and informed consent was obtained from all subjects. Twenty-six adult patients with a total of 34 cerebral metastases underwent T1-weighted DCE MRI in a 1.5-T magnet at baseline (prior to SRS) and 4-8 weeks after treatment. Quantitative analysis of DCE MRI was performed by generating K trans parametric maps, and region-of-interest-based measurements were acquired for each metastasis. Conventional MRI was performed at least 16 weeks after SRS to assess midterm tumor outcome using volume variation.

RESULTS

The mean (±SD) K trans value was 0.13 ± 0.11 min(-1) at baseline and 0.08 ± 0.07 min(-1) after 4-8 weeks post-treatment (p < 0.001). The mean (±SD) total follow-up time was 7.9 ± 4.7 months. Seventeen patients (22 lesions) underwent midterm MRI. Of those, nine (41 %) lesions had progressed at the midterm follow-up. An increase in K trans after SRS was predictive of tumor progression (hazard ratio = 1.50; 95 % CI = 1.16-1.70, p < 0.001). An increase of 15 % in K trans showed a sensitivity of 78 % and a specificity of 85 % for the prediction of progression at midterm follow-up.

CONCLUSION

SRS was associated with a reduction of K trans values of the cerebral metastases in the early post-treatment period. Furthermore, K trans variation as assessed using DCE MRI may be helpful to predict midterm outcomes after SRS.

UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine

To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032 ± 0.02 ml/100 ml/min for the plasma flow, 0.004 ± 0.0007 ml/100 ml for the volume of distribution, and 0.037 ± 0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plug-in for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

[Perfusion magnetic resonance imaging for high grade astrocytomas: Can cerebral blood volume, peak height, and percentage of signal intensity recovery distinguish between progression and pseudoprogression?]

OBJECTIVES

To study the usefulness of common MRI perfusion parameters for identifying pseudoprogression in high grade astrocytomas.

MATERIAL AND METHODS

This retrospective case-control study compared the relative cerebral blood volume (rCBV), the relative percentage of signal intensity recovery (rPSR), and the relative peak height (rPH) recorded in a sample of 17 cases of anaplastic astrocytomas and gliomas considered to be undergoing pseudoprogression by biopsy or follow-up with those recorded in a sample of histologically similar tumors that were treated and considered to be undergoing progression by histologic study or follow-up. We evaluated the accuracy of these parameters and the correlations among them. Statistical significance was set at P<.05.

RESULTS

The rCBV, rPSR, and rPH were significantly different between the two groups (P=.001). The cutoff values rPH=1.37, rCBV=0.9, and rPSR=99% yielded sensitivity (S)=88% and specificity (Sp)=82.2% for rPH, S=100% and Sp=100% for rCBV, and S=100% and Sp=70.6% for rPSR, respectively. We found negative correlations between rPRS and rPH (-0.76) and between rPRS and rCBV (-0.81) and a high positive correlation between rPH and rCBV (0.87).

CONCLUSION

The variables rPH and rCBV were useful for differentiating between pseudoprogression and true progression in our sample. The variable rPRS was also very sensitive, although the overlap in the values between samples make it less useful a priori.

Glioma grading capability: comparisons among parameters from dynamic contrast-enhanced MRI and ADC value on DWI

Objective

Permeability parameters from dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) value on diffusion-weighted imaging (DWI) can be quantitative physiologic metrics for gliomas. The transfer constant (K^trans) has shown efficacy in grading gliomas. Volume fraction of extravascular extracellular space (v_e) has been underutilized to grade gliomas. The purpose of this study was to evaluate v_e in its ability to grade gliomas and to assess the correlation with other permeability parameters and ADC values.

Materials and Methods

A total of 33 patients diagnosed with pathologically-confirmed gliomas were examined by 3 T MRI including DCE-MRI and ADC map. A region of interest analyses for permeability parameters from DCE-MRI and ADC were performed on the enhancing solid portion of the tumors. Permeability parameters form DCE-MRI and ADC between low- and high-grade gliomas; the diagnostic performances of presumptive metrics and correlation among those metrics were statistically analyzed.

Results

High-grade gliomas showed higher K^trans (0.050 vs. 0.010 in median value, p = 0.002) and higher v_e (0.170 vs. 0.015 in median value, p = 0.001) than low-grade gliomas. Receiver operating characteristic curve analysis showed significance in both K^trans and v_e for glioma grading. However, there was no significant difference in diagnostic performance between K^trans and v_e. ADC value did not correlate with any of the permeability parameters from DCE-MRI.

Conclusion

Extravascular extracellular space (v_e) appears to be comparable with transfer constant (K^trans) in differentiating high-grade gliomas from low-grade gliomas. ADC value does not show correlation with any permeability parameters from DCE-MRI.

Perfusion MRI: the five most frequently asked technical questions

OBJECTIVE

This and its companion article address the 10 most frequently asked questions that radiologists face when planning, performing, processing, and interpreting different MR perfusion studies in CNS imaging.

CONCLUSION

Perfusion MRI is a promising tool in assessing stroke, brain tumors, and patients with neurodegenerative diseases. Most of the impediments that have limited the use of perfusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results

PURPOSE

To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs).

MATERIALS AND METHODS

DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations.

RESULTS

There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters.

CONCLUSION

Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.

Advanced magnetic resonance imaging biomarkers of cerebral metastases

There are a number of magnetic resonance imaging techniques available for use in the diagnosis and management of patients with cerebral metastases. This article reviews these techniques, in particular, the advanced imaging methodologies from which quantitative parameters can be derived, the role of these imaging biomarkers have in distinguishing metastases from primary central nervous system tumours and tumour mimics, and metrics that may be of value in predicting the origin of the primary tumour.

Quantifying heterogeneity in human tumours using MRI and PET

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response. Documenting and quantifying regional heterogeneity within the tumour requires histological or imaging techniques. There is increasing evidence that quantitative imaging biomarkers can be used in vivo to provide important, reproducible and repeatable estimates of tumoural heterogeneity. In this article we review the imaging methods available to provide appropriate biomarkers of tumour structure and function. We also discuss the significant technical issues involved in the quantitative estimation of heterogeneity and the range of descriptive metrics that can be derived. Finally, we have reviewed the existing clinical evidence that heterogeneity metrics provide additional useful information in drug discovery and development and in clinical practice.