Quantification of physiological and hemodynamic indices usingT1 dynamic contrast-enhanced MRI in intracranial mass lesions

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI

BACKGROUND AND PURPOSE

Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T₁ perfusion MRI parameters combined under a machine learning framework.

MATERIALS AND METHODS

This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T₁ perfusion and DWI data along with conventional MRI images. Analysis of T₁ perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T₁ perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T₁ perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter.

RESULTS

T₁ perfusion parameter values were significantly different between the two groups. The combination of T₁ perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error.

CONCLUSION

The approach of using quantitative T₁ perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

Neurocysticercosis-related Seizures: Imaging Biomarkers

Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.

Contributions of blood-brain barrier imaging to neurovascular unit pathophysiology of Alzheimer's disease and related dementias

The blood-brain barrier (BBB) plays important roles in the maintenance of brain homeostasis. Its main role includes three kinds of functions: (1) to protect the central nervous system from blood-borne toxins and pathogens; (2) to regulate the exchange of substances between the brain parenchyma and capillaries; and (3) to clear metabolic waste and other neurotoxic compounds from the central nervous system into meningeal lymphatics and systemic circulation. Physiologically, the BBB belongs to the glymphatic system and the intramural periarterial drainage pathway, both of which are involved in clearing interstitial solutes such as β-amyloid proteins. Thus, the BBB is believed to contribute to preventing the onset and progression for Alzheimer's disease. Measurements of BBB function are essential toward a better understanding of Alzheimer's pathophysiology to establish novel imaging biomarkers and open new avenues of interventions for Alzheimer's disease and related dementias. The visualization techniques for capillary, cerebrospinal, and interstitial fluid dynamics around the neurovascular unit in living human brains have been enthusiastically developed. The purpose of this review is to summarize recent BBB imaging developments using advanced magnetic resonance imaging technologies in relation to Alzheimer's disease and related dementias. First, we give an overview of the relationship between Alzheimer's pathophysiology and BBB dysfunction. Second, we provide a brief description about the principles of non-contrast agent-based and contrast agent-based BBB imaging methodologies. Third, we summarize previous studies that have reported the findings of each BBB imaging method in individuals with the Alzheimer's disease continuum. Fourth, we introduce a wide range of Alzheimer's pathophysiology in relation to BBB imaging technologies to advance our understanding of the fluid dynamics around the BBB in both clinical and preclinical settings. Finally, we discuss the challenges of BBB imaging techniques and suggest future directions toward clinically useful imaging biomarkers for Alzheimer's disease and related dementias.

Increasing of Blood Brain Barrier Permeability and the Association With Depression and Anxiety in Systemic Lupus Erythematosus Patients

Objective

To study changes in blood brain barrier (BBB) permeability in systemic lupus erythematosus (SLE) patients, and explore the association between the alterations in BBB permeability and depression/anxiety in SLE.

Methods

Brain dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images were collected from 42 SLE patients and 23 healthy controls (HCs). Based on the Patlak pharmacokinetic model, the K^trans value of each voxel in the whole brain of each subject was calculated. BBB permeability indicator (the K^trans value) between SLE patients and healthy control group was compared. Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) were used to assess the mental health of SLE patients. The difference in BBB permeability was compared on SLE patients with depression/anxiety, SLE patients without depression/anxiety and HCs by ANOVA analysis.

Results

The K^trans value of the right insular region of the SLE group was significantly higher than that of the healthy control group. And the K^trans value of the right insular region in SLE patients with depression/anxiety was significantly increased compared with SLE patients without depression/anxiety and HCs.

Conclusions

SLE patients have increased BBB permeability, mainly in the right insular area. The increased BBB permeability in the right insular region is associated with the depression/anxiety in SLE patients.

Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.

Assessment of Blood-Brain Barrier Integrity in Tuberculous Meningitis Using Dynamic Contrast-Enhanced MR Perfusion

Objective Tuberculous meningitis (TBM) is the most common form of central nervous system tuberculosis. The aim of the study was to quantitatively evaluate blood-brain barrier (BBB) perfusion changes in TBM patients using dynamic contrast-enhanced (DCE) MR perfusion. Methods and Material Thirty untreated patients of TBM and 10 healthy controls were prospectively evaluated by conventional imaging and DCE MR perfusion. Mean permeability indices- K trans and Ve-were calculated from multiple regions of interest (ROIs) placed in basal cisterns and comparison was done between the patients and controls. Results The permeability indices were significantly higher (where p < 0.001) in cisterns of TBM patients who showed basal meningeal enhancement when compared with healthy controls. Significant differences in permeability were observed between "enhancing" cases and controls as well as in "enhancing" cases when compared with the "non-enhancing" cases. However, no significant difference was observed in the mean cisternal value between "non-enhancing" cases and the controls. K trans with a cutoff value of > 0.0838 had 81.6% sensitivity and 78.6% specificity in differentiating cases and controls while V e mean value with a cutoff value of 0.0703 showed 86.8% sensitivity and 91.4% specificity in predicting the permeability difference between the cases and controls. Conclusion DCE MR perfusion is useful in the quantitative measurement of disruption of BBB and perfusion alterations in patients of TBM.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI

PURPOSE

This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation.

METHODS

Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features.

RESULTS

Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma.

CONCLUSION

We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.

T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading

BACKGROUND AND PURPOSE

To evaluate the performance of multiparametric MR images in differentiation of different regions of the gross tumor area and for assessment of glioma grade.

METHODS

Forty-six glioma subjects (18 grade II, 11 grade III, and 17 grade IV) underwent a comprehensive MR and spectroscopic imaging procedure. Maps were generated by subtraction of T1-weighted images from contrast-enhanced T1-weighted images (ΔT1 map) and T1-weighted images from T2-weighted images (ΔT2 map). Regions of interest (ROIs) were positioned in normal-appearing white matter (NAWM), enhancing tumor, hyperintense T2, necrotic region, and immediate and distal peritumoral regions (IPR and DPR). Relative signal contrast was estimated as difference between mean intensities in ROIs and NAWM. Classification using support vector machines was applied to all image series to determine the efficacy of regional contrast measures for differentiation of low- and high-grade lesions and grade III and IV lesions.

RESULTS

ΔT1 and ΔT2 maps offered higher contrast as compared to other parametric maps in differentiating enhancing tumor and edematous regions, respectively, and provided the highest classification accuracy for differentiating low- and high-grade tumors, of 91% and 90.4%. Choline/N-acetylaspartate maps provided significant contrast for delineating IPR and DPR. For differentiating high-grade gliomas, ΔT2 and ΔT1 maps provided a mean accuracy of 90.9% and 88.2%, which was lower than that obtained using cerebral blood volume (93.7%) and choline/creatine (93.3%) maps.

CONCLUSION

This study showed that subtraction maps provided significant contrast in differentiating several regions of the gross tumor area and are of benefit for accurate tumor grading.

Localized Blood-Brain Barrier Opening in Ovine Model Using Image-Guided Transcranial Focused Ultrasound

Transcranial application of focused ultrasound (FUS) combined with vascular introduction of microbubble contrast agents (MBs) has emerged as a non-invasive technique that can temporarily create a localized opening in the blood-brain barrier (BBB). Under image-guidance, we administered FUS to sheep brain after intravenous injection of microbubbles. BBB opening was confirmed by performing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to detect the extravasated gadolinium-based magnetic resonance contrast agents. Through pharmacokinetic analysis as well as independent component analysis of the DCE-MRI data, we observed localized enhancement in BBB permeability at the area that subjected to acoustic pressure of 0.48 MPa (mechanical index = 0.96). On the other hand, application of a higher pressure at 0.58 MPa resulted in localized, minor cerebral hemorrhage. No animals exhibited abnormal behavior during the post-FUS survival periods up to 2 mo. Our data suggest that monitoring for excessive BBB disruption is important for safe translation of the method to humans.

Comparative evaluation of cerebral gliomas using rCBV measurements during sequential acquisition of T1-perfusion and T2*-perfusion MRI

Objective

To assess the inter-technique agreement of relative cerebral blood volume (rCBV) measurements obtained using T1- and T2*-perfusion MRI on 3T scanner in glioma patients.

Methods

A total of 49 adult patients with gliomas underwent both on T1- and T2*-perfusion in the same scanning session, and rCBV maps were estimated using both methods. For the quantitative analysis; Two independent observers recorded the rCBV values from the tumor as well as contralateral brain tissue from both T1- and T2*-perfusion. Inter-observer and inter-technique rCBV measurement agreement were determined by using 95% Bland-Altman limits of agreement and intra-class correlation coefficient (ICC) statistics.

Results

Qualitative analysis of the conventional and perfusion images showed that 16/49 (32.65%) tumors showed high susceptibility, and in these patients T2*-perfusion maps were suboptimal. Bland-Altman plots revealed an agreement between two independent observers recorded rCBV values for both T1- and T2*-perfusion. The ICC demonstrated strong agreement between rCBV values recorded by two observers for both T2* (ICC = 0.96, p = 0.040) and T1 (ICC = 0.97, p = 0.026) perfusion and similarly, good agreement was noted between rCBV estimated using two methods (ICC = 0.74, P<0.001). ROC analysis showed that rCBV estimated using T1- and T2*-perfusion methods were able to discriminate between grade-III and grade-IV tumors with AUC of 0.723 and 0.767 respectively. Comparison of AUC values of two ROC curves did not show any significant difference.

Conclusions

In the current study, T1- and T2*-perfusion showed similar diagnostic performance for discrimination of grade III and grade IV gliomas; however, T1-perfusion was found to be better for the evaluation of tumors with intratumoral hemorrhage, postoperative recurrent tumors, and lesions near skull base. We conclude that T1-perfusion MRI with a single dose of contrast could be used as an alternative to T2*-perfusion to overcome the issues associated with this technique in brain tumors for reliable perfusion quantification.

Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components

BACKGROUND

Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed.

PURPOSE

To develop an artificial intelligence-based methodology for glioma grading using T₁ perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients.

STUDY TYPE

Retrospective.

POPULATION

The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients.

FIELD STRENGTH/SEQUENCE

Conventional MRI images (2D T₁ -W, dual PD-T₂ -W, and 3D FLAIR) and 3D T₁ perfusion MRI data obtained at 3 T.

ASSESSMENT

Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T₁ perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T₁ perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading.

STATISTICAL TESTS

Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis.

RESULTS

Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90^th percentile value of T₁ perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier.

DATA CONCLUSION

It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306.

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images

PURPOSE

High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T₂-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T₁ perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor.

MATERIAL AND METHODS

This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T₁-W, T₂-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T₁ perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T₁ perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing.

RESULTS

Histology results showed that resected tissue consists largely of tumorous tissue with 7.21 ± 4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing.

CONCLUSION

The current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist's manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients.

Numerical Study of Transport of Anticancer Drugs in Heterogeneous Vasculature of Human Brain Tumors Using Dynamic Contrast Enhanced-Magnetic Resonance Imaging

Systemic administration of drugs in tumors is a challenging task due to unorganized microvasculature and nonuniform extravasation. There is an imperative need to understand the transport behavior of drugs when administered intravenously. In this study, a transport model is developed to understand the therapeutic efficacy of a free drug and liposome-encapsulated drugs (LED), in heterogeneous vasculature of human brain tumors. Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data is employed to model the heterogeneity in tumor vasculature that is directly mapped onto the computational fluid dynamics (CFD) model. Results indicate that heterogeneous vasculature leads to preferential accumulation of drugs at the tumor position. Higher drug accumulation was found at location of higher interstitial volume, thereby facilitating more tumor cell killing at those areas. Liposome-released drug (LRD) remains inside the tumor for longer time as compared to free drug, which together with higher concentration enhances therapeutic efficacy. The interstitial as well as intracellular concentration of LRD is found to be 2–20 fold higher as compared to free drug, which are in line with experimental data reported in literature. Close agreement between the predicted and experimental data demonstrates the potential of the developed model in modeling the transport of LED and free drugs in heterogeneous vasculature of human tumors.

Measurement of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging with reduced scan time

PURPOSE

To investigate the feasibility of measuring the subtle disruption of blood-brain barrier (BBB) using DCE-MRI with a scan duration shorter than 10 min.

METHODS

The extended Patlak-model (EPM) was introduced to include the effect of plasma flow (F_p ) in the estimation of vascular permeability-surface area product (PS). Numerical simulation studies were carried out to investigate how the reduction in scan time affects the accuracy in estimating contrast kinetic parameters. DCE-MRI studies of the rat brain were conducted with Fisher rats to confirm the results from the simulation. Intracranial F98 glioblastoma models were used to assess areas with different levels of permeability. In the normal brain tissues, the Patlak model (PM) and EPM were compared, whereas the 2-compartment-exchange-model (TCM) and EPM were assessed in the peri-tumor and the tumor regions.

RESULTS

The simulation study results demonstrated that scan time reduction could lead to larger bias in PS estimated by PM (>2000%) than by EPM (<47%), especially when F_p is low. When F_p was high as in the gray matter, the bias in PM-PS (>900%) were larger than that in EPM-PS (<42%). The animal study also showed similar results, where the PM parameters were more sensitive to the scan duration than the EPM parameters. It was also demonstrated that, in the peri-tumor region, the EPM parameters showed less change by scan duration than the TCM parameters.

CONCLUSION

The results of this study suggest that EPM can be used to measure PS with a scan duration of 10 min or less.

Dynamic contrast-enhanced (DCE) MRI derived kinetic perfusion indices may help predicting seizure control in single calcified neurocysticercosis

BACKGROUND

The factors responsible for seizure recurrence in patients with Solitary calcified neurocysticercosis (NCC) are not well understood. Blood brain barrier (BBB) breach may be associated with seizure recurrence. Dynamic contrast enhanced (DCE) MRI derived indices k_ep, k^trans and v_e are useful in quantifying BBB permeability. In this study, we assessed the possible role of DCE-MRI and matrix metalloproteinases (MMP)-9 levels in predicting seizure recurrence.

METHODS

In this prospective-observational study, patients with new-onset seizures and a solitary calcified NCC were included. DCE-MRI was done to quantify BBB integrity. DCE-MRI parameters were measured as k_ep, k^trans and v_e. MMP-9 levels were estimated. Patients were followed for 1 year, when DCE-MRI and MMP-9 levels were repeated. Patients were classified into two groups on the basis of seizure recurrence, which was defined as the recurrence of an episode of seizure at least 1 week after the initiation of the anti-epileptic drugs. Logistic regression analysis was done.

RESULTS

At 1-year of follow up, 8 out of 32 patients had seizure recurrence. Baseline DCE-MRI derived k_ep (p = 0.015) and MMP-9 levels (p = 0.019) were significantly higher in the seizure "recurrence" group compared with the "no recurrence" group. On within-group analysis, a significant increase in k_ep (p = 0.012), v_e (p = 0.012), and MMP-9 levels (p = 0.017) was observed in the seizure "recurrence" group while a decrease was seen in v_e and MMP-9 levels in the "no recurrence" group.

CONCLUSION

Higher values of DCE-MRI indices and MMP-9 levels, with a corresponding trend in the follow-up, can be useful in predicting lesions with a higher propensity for seizure recurrence.

Assessment of the Agreement between Cerebral Hemodynamic Indices Quantified Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced Perfusion Magnetic Resonance Imagings

Background:

Brain tumor is one of the most common tumors. A successful treatment might be achieved with an early identification. Pathological investigation as the gold standard method for tumor identification has some limitations. Noninvasive assessment of tumor specifications may be possible using perfusion-weighted magnetic resonance imaging (MRI). Cerebral blood volume (CBV) and cerebral blood flow (CBF) could be calculated based on dynamic contrast-enhanced MRI (DCE-MRI) in addition to dynamic susceptibility contrast MRI (DSC-MRI) modality. Each category of the cerebral hemodynamic and permeability indices revealed the specific tumor characteristics and their collection could help for better identification of the tumor. Some mathematical methods were developed to determine both cerebral hemodynamic and permeability indices based on a single-dose DCE perfusion MRI. There are only a few studies available on the comparison of DSC- and DCE-derived cerebral hemodynamic indices such as CBF and CBV.

Aim:

The objective of the study was to validate first-pass perfusion parameters derived from T1-based DCE method in comparison to the routine T2*-based DSC protocol.

Materials and Methods:

Twenty-nine patients with brain tumor underwent DCE- and DSC-MRIs to evaluate the agreement between DSC- and DCE-derived cerebral hemodynamic parameters. Agreement between DSC- and DCE-derived cerebral hemodynamic indices was determined using the statistical method described by Bland and Altman. The reliability between DSC- and DCE-derived cerebral hemodynamic indices was measured using the intraclass correlation analysis.

Results:

The achieved magnitudes for DCE-derived CBV (gray matter [GM]: 5.01 ± 1.40 mL/100 g vs. white matter [WM]: 1.84 ± 0.74 mL/100 g) and DCE-derived CBF (GM: 60.53 ± 12.70 mL/100 g/min vs. WM: 32.00 ± 6.00 mL/100 g/min) were in good agreement with other studies. The intraclass correlation coefficients showed that the cerebral hemodynamic indices could accurately be estimated based on the DCE-MRI using a single-compartment model (>0.87), and DCE-derived cerebral hemodynamic indices are significantly similar to the magnitudes achieved based on the DSC-MRI (P < 0.001). Furthermore, an acceptable agreement was observed between DSC- and DCE-derived cerebral hemodynamic indices.

Conclusion:

Based on the measurement of the cerebral hemodynamic and blood–brain barrier permeability using DCE-MRI, a more comprehensive collection of the physiological parameters cloud be achieved for tumor evaluations.

Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T

Background

Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B₁ inhomogeneity (B₁inh). The purpose of the study was to evaluate the effect of B₁inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B₁inh correction of perfusion parameters (PPs) on tumor grading.

Methods

An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B₁-mapping, T₁-mapping and DCE-MRI were acquired. Relative B₁ maps (B_1rel) were generated using the saturated-double-angle method. T₁-maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal–intensity time (S(t)) curve to concentration–time (C(t)) curve followed by tracer kinetic analysis (K^trans, Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B₁inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B₁ inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain K^trans of glioma patients at different B_1rel values and see whether grading is affected or not.

Results

Current study shows that B_1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B_1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B₁inh.

Conclusions

B₁inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B₁inh correction during DCE-MRI data analysis.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1349-7) contains supplementary material, which is available to authorized users.

Differentiation of grade II/III and grade IV glioma by combining "T1 contrast-enhanced brain perfusion imaging" and susceptibility-weighted quantitative imaging

PURPOSE

MRI is a useful method for discriminating low- and high-grade glioma using perfusion MRI and susceptibility-weighted imaging (SWI). The purpose of this study is to evaluate the usefulness of T1-perfusion MRI and SWI in discriminating among grade II, III, and IV gliomas.

METHODS

T1-perfusion MRI was used to measure relative cerebral blood volume (rCBV) in 129 patients with glioma (70 grade IV, 33 grade III, and 26 grade II tumors). SWI was also used to measure the intratumoral susceptibility signal intensity (ITSS) scores for each tumor in these patients. rCBV and ITSS values were compared to seek differences between grade II vs. grade III, grade III vs. grade IV, and grade III+II vs. grade IV tumors.

RESULTS

Significant differences in rCBV values of the three grades of the tumors were noted and pairwise comparisons showed significantly higher rCBV values in grade IV tumors as compared to grade III tumors, and similarly increased rCBV was seen in the grade III tumors as compared to grade II tumors (p < 0.001). Grade IV gliomas showed significantly higher ITSS scores on SWI as compared to grade III tumors (p < 0.001) whereas insignificant difference was seen on comparing ITSS scores of grade III with grade II tumors. Combining the rCBV and ITSS resulted in significant improvement in the discrimination of grade III from grade IV tumors.

CONCLUSION

The combination of rCBV values derived from T1-perfusion MRI and SWI derived ITSS scores improves the diagnostic accuracy for discrimination of grade III from grade IV gliomas.

Novel Treatment Strategies for Brain Metastases in Non-small-cell Lung Cancer

OPINION STATEMENT

Brain metastases are common in patients with non-small cell lung cancer (NSCLC), and due to associated poor prognosis, this field is an important area of need for the development of innovative medical therapies. Therapies including local approaches through surgical intervention and/or radiation and evolving systemic therapies have led to improvements in the treatment of brain metastases in patients with lung cancer. Strategies that consider applying advanced radiation techniques to minimize toxicity, intervening early with effective systemic therapies to spare radiation/surgery, testing radiosensitization combinations, and developing drug penetrant molecules have and will continue to define new practice patterns. We believe that in carefully considered asymptomatic patients, first-line systemic therapy may be considered before radiation therapy and small-molecule targeted therapy may provide an opportunity to defer radiation therapy for recurrence or progression of disease. The next several years in oncology drug development will see the reporting on of brain penetrant molecules in oncogene-defined non-small cell lung cancer. Ongoing studies will evaluate immunotherapies in patients with brain metastases with associated endpoints. We hope that continued drug development and carefully designed clinical trials may afford an opportunity to improve the lives of patients with brain metastases.

Transport of Liposome Encapsulated Drugs in Voxelized Computational Model of Human Brain Tumors

There are many obstacles in the transport of chemotherapeutic drugs to tumor cells that lead to irregular and non-uniform uptake of drugs inside tumors. The study of these transport problems will help with accurate prediction of drug transport and optimizing treatment strategy. To this end, liposome mediated drug delivery has emerged as an excellent anticancer therapy due to its ability to deliver drugs at site of action and reducing the chances of side effects to the healthy tissues. In this paper, a computational fluid dynamics (CFD) model based on realistic vasculature of human brain tumor is presented. This model utilizes dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data to account for heterogeneity in tumor vasculature. Porosity of the interstitial space inside the tumor and normal tissue is determined voxel-wise by processing the DCE-MRI images by general tracer kinetic model (GTKM). The CFD model is applied to predict transport of two different types of liposomes (stealth and conventional) in tumors. The amount of accumulated liposomes is compared with accumulated free drug (doxorubicin) in the interstitial space. Simulation results indicate that stealth liposomes accumulate more and remain for longer periods of time in tumors as compared with conventional liposomes and free drug. The present model provides us a qualitative and quantitative examination on the transport and deposition of liposomes as well as free drugs in actual human brain tumors.

Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1- weighted DCE-MRI

PURPOSE

Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI).

MATERIAL AND METHODS

Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC).

RESULT

The diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV_GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading.

CONCLUSION

It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach.

Perfusion kinetics in human brain tumor with DCE-MRI derived model and CFD analysis

Cancer is one of the leading causes of death all over the world. Among the strategies that are used for cancer treatment, the effectiveness of chemotherapy is often hindered by factors such as irregular and non-uniform uptake of drugs inside tumor. Thus, accurate prediction of drug transport and deposition inside tumor is crucial for increasing the effectiveness of chemotherapeutic treatment. In this study, a computational model of human brain tumor is developed that incorporates dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data into a voxelized porous media model. The model takes into account realistic transport and perfusion kinetics parameters together with realistic heterogeneous tumor vasculature and accurate arterial input function (AIF), which makes it patient specific. The computational results for interstitial fluid pressure (IFP), interstitial fluid velocity (IFV) and tracer concentration show good agreement with the experimental results. The computational model can be extended further for predicting the deposition of chemotherapeutic drugs in tumor environment as well as selection of the best chemotherapeutic drug for a specific patient.

Diffusion-compensated tofts model suggests contrast leakage through aneurysm wall

PURPOSE

The purpose of this study was to investigate the diffusional transport of contrast agent and its effects on kinetic modeling of dynamic contrast enhanced (DCE) images.

METHODS

We performed simulations of our diffusion-compensated model and compared these results to human intracranial aneurysms (IAs). We derive an easy-to-use parameterization of diffusional effects that can provide an accurate estimate of diffusion corrected contrast agent leakage rates (k^trans ). Finally, we performed re-ansalysis of an existing data set to determine whether diffusion-corrected kinetic parameters improve the identification of high-risk IAs, thereby providing a new MRI-based imaging metric of IA stability based on wall integrity.

RESULTS

Probability distributions of simulated versus measured data show contrast leakage away from the aneurysm wall. Parameterization of diffusional effects on k^trans showed high correlation with long-chain methods in both surrounding tissue and near the aneurysm wall (r²  = 0.91 and r²  = 0.90, respectively). Finally, size, k^trans , and ( ktrans-kDCtrans) showed significant univariate relationships with rupture risk (P < 0.05).

CONCLUSIONS

We report the first evidence of diffusion-compensated permeability modeling in intracranial aneurysms and propose a parameterization of diffusional effects on k^trans . Furthermore, a comparison of measured versus simulated data suggests that contrast leakage occurs across the aneurysm wall. Magn Reson Med 78:2388-2398, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Comparison of actual with default hematocrit value in dynamic contrast enhanced MR perfusion quantification in grading of human glioma

PURPOSE

Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed hematocrit (Hct) value for adults is assumed to be ~45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI.

MATERIALS AND METHODS

Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values.

RESULTS

The measured Hct value in patients was found to be (40.4±4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15-20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases.

CONCLUSION

Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model.

Dynamic Contrast-Enhanced MR Imaging in Head and Neck Cancer: Techniques and Clinical Applications

In the past decade, dynamic contrast-enhanced MR imaging has had an increasing role in assessing the microvascular characteristics of various tumors, including head and neck cancer. Dynamic contrast-enhanced MR imaging allows noninvasive assessment of permeability and blood flow, both important features of tumor hypoxia, which is a marker for treatment resistance for head and neck cancer. Dynamic contrast-enhanced MR imaging has the potential to identify early locoregional recurrence, differentiate metastatic lymph nodes from normal nodes, and predict tumor response to treatment and treatment monitoring in patients with head and neck cancer. Quantitative analysis is in its early stage and standardization and refinement of technique are essential. In this article, we review the techniques of dynamic contrast-enhanced MR imaging data acquisition, analytic methods, current limitations, and clinical applications in head and neck cancer.

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

AIM

To evaluate the sensitivity and specificity of single-dose dynamic contrast-enhanced (DCE) perfusion magnetic resonance imaging (MRI) in prospective evaluation of glioma grading and to correlate the relative cerebral blood volume (rCBV) values with mitotic and ki-67 indexes obtained at histopathology.

MATERIALS AND METHODS

A total of 53 histologically proven patients with glioma were included in this study. DCE-MRI perfusion with a single dose of contrast medium was included in brain tumour protocol and prospective grading of glioma into low and high grade was done based on a previously reported rCBV cut-off value of 3. Tumours with rCBV ≥ 3 were considered to be high grade and rCBV < 3 were considered to be low grade. The sensitivity and specificity of the cut-off value were estimated. Ki-67 and mitotic indexes were also obtained on histopathological analysis along with histological grading.

RESULTS

Based on pre-defined rCBV cut-off values, prospective grading of low- and high-grade glioma was achieved with a sensitivity and specificity of 97.22% and 100%, respectively. Significant correlation was found between the mitotic/ki-67 indexes and rCBV values when data for high- and low-grade tumours was combined.

CONCLUSION

DCE-MRI performed with a single dose of contrast medium is as effective as a protocol with a double-dose of contrast medium for glioma grading using 3 T MRI and could be added to the routine evaluation protocol of brain tumours.

Dynamic Contrast-Enhanced Perfusion MRI of High Grade Brain Gliomas Obtained with Arterial or Venous Waveform Input Function

BACKGROUND AND PURPOSE

The aim of this study was to evaluate the differences in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion estimates of high-grade brain gliomas (HGG) due to the use of an input function (IF) obtained respectively from arterial (AIF) and venous (VIF) approaches by two different commercially available software applications.

METHODS

This prospective study includes 20 patients with pathologically confirmed diagnosis of high-grade gliomas. The data source was processed by using two DCE dedicated commercial packages, both based on the extended Toft model, but the first customized to obtain input function from arterial measurement and the second from sagittal sinus sampling. The quantitative parametric perfusion maps estimated from the two software packages were compared by means of a region of interest (ROI) analysis. The resulting input functions from venous and arterial data were also compared.

RESULTS

No significant difference has been found between the perfusion parameters obtained with the two different software packages (P-value < .05). The comparison of the VIFs and AIFs obtained by the two packages showed no statistical differences.

CONCLUSIONS

Direct comparison of DCE-MRI measurements with IF generated by means of arterial or venous waveform led to no statistical difference in quantitative metrics for evaluating HGG. However, additional research involving DCE-MRI acquisition protocols and post-processing would be beneficial to further substantiate the effectiveness of venous approach as the IF method compared with arterial-based IF measurement.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: a simulation and in vivo study on healthy subjects and multiple sclerosis patients

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used to estimate permeability in situations with subtle blood-brain barrier (BBB) leakage. However, the method's ability to differentiate such low values from zero is unknown, and no consensus exists on optimal selection of total measurement duration, temporal resolution, and modeling approach under varying physiologic circumstances. To estimate accuracy and precision of the DCE-MRI method we generated simulated data using a two-compartment model and progressively down-sampled and truncated the data to mimic low temporal resolution and short total measurement duration. Model fit was performed with the Patlak, the extended Tofts, and the Tikhonov two-compartment (Tik-2CM) models. Overall, 17 healthy controls were scanned to obtain in vivo data. Long total measurement duration (15 minutes) and high temporal resolution (1.25 seconds) greatly improved accuracy and precision for all three models, enabling us to differentiate values of permeability as low as 0.1 ml/100 g/min from zero. The Patlak model yielded highest accuracy and precision for permeability values <0.3 ml/100 g/min, but for higher values the Tik-2CM performed best. Our results emphasize the importance of optimal parameter setup and model selection when characterizing low BBB permeability.

Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques

Perfusion is a fundamental biological function that refers to the delivery of oxygen and nutrients to tissue by means of blood flow. Perfusion MRI is sensitive to microvasculature and has been applied in a wide variety of clinical applications, including the classification of tumors, identification of stroke regions, and characterization of other diseases. Perfusion MRI techniques are classified with or without using an exogenous contrast agent. Bolus methods, with injections of a contrast agent, provide better sensitivity with higher spatial resolution, and are therefore more widely used in clinical applications. However, arterial spin-labeling methods provide a unique opportunity to measure cerebral blood flow without requiring an exogenous contrast agent and have better accuracy for quantification. Importantly, MRI-based perfusion measurements are minimally invasive overall, and do not use any radiation and radioisotopes. In this review, we describe the principles and techniques of perfusion MRI. This review summarizes comprehensive updated knowledge on the physical principles and techniques of perfusion MRI.

Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review

There is increasing recognition of the importance of blood-brain barrier (BBB) disruption in aging, dementia, stroke and multiple sclerosis in addition to more commonly-studied pathologies such as tumors. Dynamic contrast-enhanced MRI (DCE-MRI) is a method for studying BBB disruption in vivo. We review pathologies studied, scanning protocols and data analysis procedures to determine the range of available methods and their suitability to different pathologies. We systematically review the existing literature up to February 2014, seeking studies that assessed BBB integrity using T1-weighted DCE-MRI techniques in animals and humans in normal or abnormal brain tissues. The literature search provided 70 studies that were eligible for inclusion, involving 417 animals and 1564 human subjects in total. The pathologies most studied are intracranial neoplasms and acute ischemic strokes. There are large variations in the type of DCE-MRI sequence, the imaging protocols and the contrast agents used. Moreover, studies use a variety of different methods for data analysis, mainly based on model-free measurements and on the Patlak and Tofts models. Consequently, estimated K (Trans) values varied widely. In conclusion, DCE-MRI is shown to provide valuable information in a large variety of applications, ranging from common applications, such as grading of primary brain tumors, to more recent applications, such as assessment of subtle BBB dysfunction in Alzheimer's disease. Further research is required in order to establish consensus-based recommendations for data acquisition and analysis and, hence, improve inter-study comparability and promote wider use of DCE-MRI.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.

The role of dynamic susceptibility contrast-enhanced perfusion MR imaging in differentiating between infectious and neoplastic focal brain lesions: results from a cohort of 100 consecutive patients

Background and Purpose

Differentiating between infectious and neoplastic focal brain lesions that are detected by conventional structural magnetic resonance imaging (MRI) may be a challenge in routine practice. Brain perfusion-weighted MRI (PWI) may be employed as a complementary non-invasive tool, providing relevant data on hemodynamic parameters, such as the degree of angiogenesis of lesions. We aimed to employ dynamic susceptibility contrast-enhanced perfusion MR imaging (DSC-MRI) to differentiate between infectious and neoplastic brain lesions by investigating brain microcirculation changes.

Materials and Methods

DSC-MRI perfusion studies of one hundred consecutive patients with non-cortical neoplastic (n = 54) and infectious (n = 46) lesions were retrospectively assessed. MRI examinations were performed using a 1.5-T scanner. A preload of paramagnetic contrast agent (gadolinium) was administered 30 seconds before acquisition of dynamic images, followed by a standard dose 10 seconds after starting imaging acquisitions. The relative cerebral blood volume (rCBV) values were determined by calculating the regional cerebral blood volume in the solid areas of lesions, normalized to that of the contralateral normal-appearing white matter. Discriminant analyses were performed to determine the cutoff point of rCBV values that would allow the differentiation of neoplastic from infectious lesions and to assess the corresponding diagnostic performance of rCBV when using this cutoff value.

Results

Neoplastic lesions had higher rCBV values (4.28±2.11) than infectious lesions (0.63±0.49) (p<0.001). When using an rCBV value <1.3 as the parameter to define infectious lesions, the sensitivity of the method was 97.8% and the specificity was 92.6%, with a positive predictive value of 91.8%, a negative predictive value of 98.0%, and an accuracy of 95.0%.

Conclusion

PWI is a useful complementary tool in distinguishing between infectious and neoplastic brain lesions; an elevated discriminatory value for diagnosis of infectious brain lesions was observed in this sample of patients when the rCBV cutoff value was set to 1.3.

Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome

BACKGROUND

Measures of tumor vascularity and hypoxia have been correlated with glioma grade and outcome. Dynamic contrast-enhanced (DCE) MRI can noninvasively map tumor blood flow, vascularity, and permeability. In this prospective observational cohort pilot study, preoperative imaging was correlated with molecular markers of hypoxia, vascularity, proliferation, and progression-free and overall patient survival.

METHODS

Pharmacokinetic modeling methods were used to generate maps of tumor blood flow, extraction fraction, permeability-surface area product, transfer constant, washout rate, interstitial volume, blood volume, capillary transit time, and capillary heterogeneity from preoperative DCE-MRI data in human glioma patients. Tissue was obtained from areas of peritumoral edema, active tumor, hypoxic penumbra, and necrotic core and evaluated for vascularity, proliferation, and expression of hypoxia-regulated molecules. DCE-MRI parameter values were correlated with hypoxia-regulated protein expression at tissue sample sites.

RESULTS

Patient survival correlated with DCE parameters in 2 cases: capillary heterogeneity in active tumor and interstitial volume in areas of peritumoral edema. Statistically significant correlations were observed between several DCE parameters and tissue markers. In addition, MIB-1 index was predictive of overall survival (P = .044) and correlated with vascular endothelial growth factor expression in hypoxic penumbra (r = 0.7933, P = .0071) and peritumoral edema (r = 0.4546). Increased microvessel density correlated with worse patient outcome (P = .026).

CONCLUSIONS

Our findings suggest that DCE-MRI may facilitate noninvasive preoperative predictions of areas of tumor with increased hypoxia and proliferation. Both imaging and hypoxia biomarkers are predictive of patient outcome. This has the potential to allow unprecedented prognostic decisions and to guide therapies to specific tumor areas.

Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications

A review of the selection of models in dynamic contrast-enhanced MRI (DCE-MRI) is conducted, with emphasis on the balance between the bias and variance required to produce stable and accurate estimates of vascular parameters. The vascular parameters considered as a first-order model are the forward volume transfer constant K(trans) , the plasma volume fraction vp and the interstitial volume fraction ve . To illustrate the critical issues in model selection, a data-driven selection of models in an animal model of cerebral glioma is followed. Systematic errors and extended models are considered. Studies with nested and non-nested pharmacokinetic models are reviewed; models considering water exchange are considered.

Classic models for dynamic contrast-enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Utility of multiparametric 3-T MRI for glioma characterization

INTRODUCTION

Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural magnetic resonance imaging and MR measurements of perfusion, diffusion, and whole-brain spectroscopic parameters for glioma grading.

METHODS

Fifty-six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, diffusion tensor imaging, and volumetric whole-brain MR spectroscopic imaging. Receiver-operating characteristic analysis was performed using the relative cerebral blood volume (rCBV), apparent diffusion coefficient, fractional anisotropy, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together.

RESULTS

The rCBV individually classified glioma as low and high grade with a sensitivity and specificity of 100 and 88 %, respectively, based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2 % error and sensitivity and specificity of 100 and 96 %, respectively.

CONCLUSION

Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters.

T1-weighted dynamic contrast-enhanced MR evaluation of different stages of neurocysticercosis and its relationship with serum MMP-9 expression

BACKGROUND AND PURPOSE

Epileptogenesis in NCC is associated with perilesional inflammation and disruption in BBB. We quantified BBB in different stages of NCC by using DCE-MR imaging to look for the differences in perfusion indices and to correlate these indices with serum MMP-9 expression.

MATERIALS AND METHODS

DCE-MR imaging along with conventional MR imaging was performed in 57 single cysticercous brain lesions to quantify the kep, K(trans), and ve around the lesions, which were in different stages of evolution. There were 6 lesions in the vesicular stage and 17 lesions each in the colloidal, granular-nodular, and calcified stages. Serum MMP-9 was quantified from all patients, whereas perfusion indices were quantified from all stages except for the vesicular stage.

RESULTS

We observed significant differences among the 3 stages of NCC in serum MMP-9 expression as well as DCE-derived kep values. In addition, kep showed a strongly significant positive correlation with MMP-9 expression when modeled with the individual stage of the disease as well as with all stages when pooled together. Other DCE-derived hemodynamic and pharmacokinetic parameters showed inconsistent differences with each stage of the disease. The correlation of DCE-derived parameters with serum MMP-9 expression and edema volume also showed inconsistency with the stage of the disease.

CONCLUSIONS

We conclude that kep correlates best with serum MMP-9 expression among the pharmacokinetic indices and most closely represents the degree of BBB breakdown, which is highest in the colloidal stage and lowest in the calcified stage. kep may be used as a noninvasive image biomarker of BBB breakdown in different stages of NCC.

Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high- and low-grade glioma using dynamic contrast-enhanced (DCE) MRI

PURPOSE

To modify the generalized tracer kinetic model (GTKM) by introducing an additional tissue uptake leakage compartment in extracellular extravascular space (LTKM). In addition, an implicit determination of voxel-wise local arterial input function (AIF) Cp (t) was performed to see whether these changes help in better discrimination between low- and high-grade glioma using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

The modified model (LTKM) was explored and fitted to the concentration-time curve C(t) of each voxel, in which the local AIF Cp (t) could be estimated by a time invariant convolution approximation based on a separately measured global AIF Ca (t). A comparative study of tracer kinetic analysis was performed on 184 glioma patients using DCE-MRI data on 1.5T and 3T MRI systems.

RESULTS

The LTKM analysis provided more accurate pharmacokinetic parameters as evidenced by their relative constancy with respect to the length of concentration-time curve used. In addition, LTKM with local AIF resulted in improved discrimination between low-grade and high-grade gliomas.

CONCLUSION

LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low-grade and high-grade gliomas as compared with GTKM.

Quantification of perfusion and permeability in multiple sclerosis: dynamic contrast-enhanced MRI in 3D at 3T

BACKGROUND AND PURPOSE

The quantification of cerebral blood flow (CBF), cerebral blood volume (CBV), and blood-brain barrier permeability in scattered lesions in the brain is a methodological challenge. We aimed to investigate the feasibility of a 3D T1-weighted dynamic contrast-enhanced (DCE) MRI acquisition in combination with a 2-compartment modeling approach for the quantification of CBF, CBV and permeability surface area product (PS) in lesions, and normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS).

MATERIAL AND METHODS

In all, 19 MS patients (mean age 35 years, 12 female) underwent DCE-MRI with a 3D T1-weighted spoiled gradient-echo sequence on a 3T MRI scanner. A total of 44 slices (thickness 3 mm) with an in-plane resolution of 1.7 × 1.7 mm(2) (matrix size 128 × 104), providing coverage of the whole brain, were acquired every 2.1 seconds over a total measurement time of 420 s. Data postprocessing was performed using a set of 2-compartment models with automated model selection; CBF, CBV, and PS as a measure of blood-brain barrier leakage were determined in contrast-enhancing (CE) and nonenhancing lesions as well as in NAWM.

RESULTS

Perfusion quantification produced reasonable values in lesions as well as in NAWM. In CE lesions, CBF (22.9 (22.7) vs. 15.8 (6.7) mL/100 mL/min), CBV (1.18 (0.48) vs. 0.76 (0.19) mL/100 mL), and PS (0.98 (0.46) vs. 0.04 (0.03) mL/100 mL/min) were significantly (P < 0.001) higher than in NAWM. In nonenhancing lesions, a weakly (P < 0.05) significantly increased CBV of 1.00 (0.35) mL/100 mL, compared with NAWM, was observed.

CONCLUSION

Our study demonstrates the feasibility of 3D T1-weighted DCE-MRI for the quantitative assessment of CBF, CBV, and PS in NAWM as well as in multiple MS lesions scattered throughout the brain, even without previous knowledge of their location. Quantification on the region level produced reasonable values both in lesions and in NAWM, but parameter maps would benefit from an increase in contrast-to-noise ratio. The increased values of CBF, CBV, and PS in CE lesions may reflect inflammatory activity, the heterogeneity of parameter estimates suggests a potential for lesion characterization. NAWM appears hypoperfused, this is in accordance with previous studies, but requires validation with a control group.

T1- and T2*-dominant extravasation correction in DSC-MRI: part I--theoretical considerations and implications for assessment of tumor hemodynamic properties

We present a novel contrast agent (CA) extravasation-correction method based on analysis of the tissue residue function for assessment of multiple hemodynamic parameters. The method enables semiquantitative determination of the transfer constant and can be used to distinguish between T(1)- and T(2)(*)-dominant extravasation effects, while being insensitive to variations in tissue mean transit time (MTT). Results in 101 patients with confirmed glioma suggest that leakage-corrected absolute cerebral blood volume (CBV) values obtained with the proposed method provide improved overall survival prediction compared with normalized CBV values combined with an established leakage-correction method. Using a standard gradient-echo echo-planar imaging sequence, ∼60% and 10% of tumors with detectable CA extravasation mainly exhibited T(1)- and T(2)(*)-dominant leakage effects, respectively. The remaining 30% of leaky tumors had mixed T(1)- and T(2)(*)-dominant effects. Using an MTT-sensitive correction method, our results show that CBV is underestimated when tumor MTT is significantly longer than MTT in the reference tissue. Furthermore, results from our simulations suggest that the relative contribution of T(1)- versus T(2)(*)-dominant extravasation effects is strongly dependent on the effective transverse relaxivity in the extravascular space and may thus be a potential marker for cellular integrity and tissue structure.