scholarly journals MR fingerprinting ASL: Sequence characterization and comparison with dynamic susceptibility contrast (DSC) MRI

2019 ◽  
Vol 33 (1) ◽  
Author(s):  
Pan Su ◽  
Hongli Fan ◽  
Peiying Liu ◽  
Yang Li ◽  
Ye Qiao ◽  
...  
Keyword(s):  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Sequence Characterization ◽  
Dsc Mri ◽  
Susceptibility Contrast

Attempts to improve absolute quantification of cerebral blood flow in dynamic susceptibility contrast magnetic resonance imaging: a simplified T1-weighted steady-state cerebral blood volume approach

2007 ◽  
Vol 48 (5) ◽  
pp. 550-556 ◽  
Author(s):  
R. Wirestam ◽  
L. Knutsson ◽  
J. Risberg ◽  
S. Börjesson ◽  
E.-M. Larsson ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Steady State ◽  
Water Exchange ◽  
Cerebral Blood Volume ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Contrast Magnetic Resonance ◽  
Susceptibility Contrast

Background: Attempts to retrieve absolute values of cerebral blood flow (CBF) by dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) have typically resulted in overestimations. Purpose: To improve DSC-MRI CBF estimates by calibrating the DSC-MRI-based cerebral blood volume (CBV) with a corresponding T1-weighted (T1W) steady-state (ss) CBV estimate. Material and Methods: 17 volunteers were investigated by DSC-MRI and 133Xe SPECT. Steady-state CBV calculation, assuming no water exchange, was accomplished using signal values from blood and tissue, before and after contrast agent, obtained by T1W spin-echo imaging. Using steady-state and DSC-MRI CBV estimates, a calibration factor K = CBV(ss)/CBV(DSC) was obtained for each individual. Average whole-brain CBF(DSC) was calculated, and the corrected MRI-based CBF estimate was given by CBF(ss) = K×CBF(DSC). Results: Average whole-brain SPECT CBF was 40.1±6.9 ml/min·100 g, while the corresponding uncorrected DSC-MRI-based value was 69.2±13.8 ml/min·100 g. After correction with the calibration factor, a CBF(ss) of 42.7±14.0 ml/min·100 g was obtained. The linear fit to CBF(ss)-versus-CBF(SPECT) data was close to proportionality ( R = 0.52). Conclusion: Calibration by steady-state CBV reduced the population average CBF to a reasonable level, and a modest linear correlation with the reference 133Xe SPECT technique was observed. Possible explanations for the limited accuracy are, for example, large-vessel partial-volume effects, low post-contrast signal enhancement in T1W images, and water-exchange effects.

Functional Information on Meningiomas Through Perfusion Magnetic Resonance Imaging

2009 ◽  
Vol 4 (1) ◽  
pp. 88
Author(s):  
Hao Zhang ◽  
Guixiang Zhang ◽  
Matthijs Oudkerk ◽  
◽  
◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Susceptibility Contrast

This article focuses on the use of perfusion magnetic resonance imaging (MRI), and in particular dynamic susceptibility contrast-enhanced MRI (DSC-MRI), to assess haemodynamics in meningiomas. We first introduce the basic principles of DSC-MRI and the most popular imaging techniques and perfusion parameters for data analysis of DSC-MRI. We then review the blood supply characteristics of meningiomas and how perfusion MRI is applied in meningiomas to help the subtyping of different meningiomas and to differentiate between benign and malignant meningiomas. Our first-hand experiences are also included. We conclude that DSC perfusion MRI can provide critical information on the vascularity of meningiomas that is not available with conventional MRI. DSC perfusion MRI measurements are helpful in the pre-operative subtyping and grading of meningiomas.

Functional Information on Meningiomas Through Perfusion Magnetic Resonance Imaging

US Neurology ◽  
2009 ◽  
Vol 05 (01) ◽  
pp. 78
Author(s):  
Hao Zhang ◽  
Guixiang Zhang ◽  
Matthijs Oudkerk ◽  
◽  
◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Susceptibility Contrast

This article focuses on the use of perfusion magnetic resonance imaging (MRI), and in particular dynamic susceptibility contrast-enhanced MRI (DSCMRI), to assess hemodynamics in meningiomas. We first introduce the basic principles of DSC-MRI and the most popular imaging techniques and perfusion parameters for data analysis of DSC-MRI. We then review the blood supply characteristics of meningiomas and how perfusion MRI is applied in meningiomas to help the subtyping of different meningiomas and to differentiate between benign and malignant meningiomas. Our firsthand experiences are also included. We conclude that DSC perfusion MRI can provide critical information on the vascularity of meningiomas that is not available with conventional MRI. DSC perfusion MRI measurements are helpful in the pre-operative subtyping and grading of meningiomas.

scholarly journals Intra-tumoural perfusion habitats showed prognostic value in glioblastoma patients

2019 ◽  
Vol 21 (Supplement_4) ◽  
pp. iv3-iv3
Author(s):  
Chao Li ◽  
Chang Sun ◽  
Shuo Wang ◽  
Stephen Price
Keyword(s):  
Cerebral Blood Volume ◽  
Cox Regression ◽  
Mean Transit Time ◽  
Tumour Microenvironment ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Post Contrast ◽  
Dsc Mri ◽  
Contrast Enhanced ◽  
Susceptibility Contrast

Abstract The perfusion within glioblastoma is associated with tumour microenvironment and may create invasive tumor habitats that could potentially be revealed by perfusion imaging. The purpose of this study is to characterize the peritumoural habitats of glioblastoma for treatment target. Dynamic susceptibility contrast-enhancement (DSC) MRI was acquired pre-operatively on 115 newly-diagnosed glioblastoma patients. All images were co-registered to post-contrast T1-weighted images. The relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated from the DSC images. The contrast-enhanced and peritumoural tumor regions were semi-automatically segmented from the post-contrast T1-weighted and FLAIR images. To delineate the habitats of different perfusion levels, a two clusters mixture model with Gaussian distribution was fitted to the rCBV, rCBF, and MTT within both contrast-enhanced and peritumoural regions. Perfusion parameters of the identified habitats were compared, and the prognostic values of habitats were investigated using survival analysis. The results showed that although non-enhanced, the peritumoral high perfusion (PHP) habitat demonstrated similar perfusion level with the contrast high perfusion (CHP) habitat, with similar rCBV (PHP: 1.13 ± 0.18, 95% CI [1.10, 1.15]; CHP: 1.21 ± 0.25, 95% CI [1.16, 1.21]) and rCBF (PHP: 1.08 ± 0.23, 95% CI [1.05, 1.08]; CHP: 1.08 ± 0.19, 95% CI [1.05, 1.08]). Multivariate Cox regression showed that the volumes of both habitats were associated with worse patient overall survival (PHP: P = 0.032; HR= 7.09; CHP: P = 0.008; HR= 12.01). Our results suggest that the intra-tumoural perfusion habitats may potentially offer treatment targets.

Application of dynamic susceptibility contrast-enhanced perfusion in temporal lobe epilepsy

2013 ◽  
Vol 54 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Wu Xing ◽  
Xiaoyi Wang ◽  
Fangfang Xie ◽  
Weihua Liao
Keyword(s):  
Temporal Lobe ◽  
Control Group ◽  
Case Group ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Temporal Lobes ◽  
Dsc Mri ◽  
Contrast Enhanced ◽  
Healthy Control ◽  
Susceptibility Contrast

Background Accurately locating the epileptogenic focus in temporal lobe epilepsy (TLE) is important in clinical practice. Single-photon emission computed tomography (SPECT) and positron-emission tomography (PET) have been widely used in the lateralization of TLE, but both have limitations. Magnetic resonance perfusion imaging can accurately and reliably reflect differences in cerebral blood flow and volume. Purpose To investigate the diagnostic value of dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) in the lateralization of the epileptogenic focus in TLE. Material and Methods Conventional MRI and DSC-MRI scanning was performed in 20 interictal cases of TLE and 20 healthy volunteers. The relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) of the bilateral mesial temporal lobes of the TLE cases and healthy control groups were calculated. The differences in the perfusion asymmetry indices (AIs), derived from the rCBV and rCBF of the bilateral mesial temporal lobes, were compared between the two groups. Results In the control group, there were no statistically significant differences between the left and right sides in terms of rCBV (left 1.55±0.32, right 1.57±0.28) or rCBF (left 99.00±24.61, right 100.38±23.46) of the bilateral mesial temporal lobes. However, in the case group the ipsilateral rCBV and rCBF values (1.75±0.64 and 96.35±22.63, respectively) were markedly lower than those of the contralateral side (2.01± 0.79 and 108.56±26.92; P < 0.05). Both the AI of the rCBV (AIrCBV; 13.03±10.33) and the AI of the rCBF (AIrCBF; 11.24±8.70) of the case group were significantly higher than that of the control group (AIrCBV 5.55± 3.74, AIrCBF 5.12±3.48; P < 0.05). The epileptogenic foci of nine patients were correctly lateralized using the 95th percentile of the AIrCBV and AIrCBF of the control group as the normal upper limits. Conclusion In patients with TLE interictal, both rCBV and rCBF of the ipsilateral mesial temporal lobe were markedly lower than that of healthy control subjects. DSC-MRI can provide lateralization for TLE.

Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas

2012 ◽  
Vol 53 (1) ◽  
pp. 95-101 ◽  
Author(s):  
H Thomsen ◽  
E Steffensen ◽  
E-M Larsson
Keyword(s):  
Blood Flow ◽  
White Matter ◽  
Blood Volume ◽  
Perfusion Mri ◽  
Dynamic Susceptibility ◽  
Reference Region ◽  
Dynamic Susceptibility Contrast ◽  
Normal Appearing White Matter ◽  
Dsc Mri ◽  
Susceptibility Contrast

Background Perfusion magnetic resonance imaging (MRI) is increasingly used in the evaluation of brain tumors. Relative cerebral blood volume (rCBV) is usually obtained by dynamic susceptibility contrast (DSC) MRI using normal appearing white matter as reference region. The emerging perfusion technique arterial spin labelling (ASL) presently provides measurement only of cerebral blood flow (CBF), which has not been widely used in human brain tumor studies. Purpose To assess if measurement of blood flow is comparable with measurement of blood volume in human biopsy-proven gliomas obtained by DSC-MRI using two different regions for normalization and two different measurement approaches. Material and Methods Retrospective study of 61 patients with different types of gliomas examined with DSC perfusion MRI. Regions of interest (ROIs) were placed in tumor portions with maximum perfusion on rCBF and rCBV maps, with contralateral normal appearing white matter and cerebellum as reference regions. Larger ROIs were drawn for histogram analyses. The type and grade of the gliomas were obtained by histopathology. Statistical comparison was made between diffuse astrocytomas, anaplastic astrocytomas, and glioblastomas. Results rCBF and rCBV measurements obtained with the maximum perfusion method were correlated when normalized to white matter (r = 0.60) and to the cerebellum (r = 0.49). Histogram analyses of rCBF and rCBV showed that mean and median values as well as skewness and peak position were correlated (0.61 < r < 0.93), whereas for kurtosis and peak height, the correlation coefficient was about 0.3 when comparing rCBF and rCBV values for the same reference region. Neither rCBF nor rCBV quantification provided a statistically significant difference between the three types of gliomas. However, both rCBF and rCBV tended to increase with tumor grade and to be lower in patients who had undergone resection/treatment. Conclusion rCBF measurements normalized to white matter or cerebellum are comparable with the established rCBV measurements used for the clinical evaluation of cerebral gliomas.

Dexamethasone Normalizes Brain Tumor Hemodynamics as Indicated by Dynamic Susceptibility Contrast MRI Perfusion Parameters

2005 ◽  
Vol 4 (3) ◽  
pp. 245-249 ◽  
Author(s):  
Christopher C. Quarles ◽  
Hendrikus G. J. Krouwer ◽  
Scott D. Rand ◽  
Kathleen M. Schmainda
Keyword(s):  
Transit Time ◽  
Spin Echo ◽  
Mean Transit Time ◽  
Normal Brain ◽  
Post Inoculation ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Functional Changes ◽  
Dsc Mri ◽  
Susceptibility Contrast

The purpose of this study is to demonstrate the utility of dynamic susceptibility contrast (DSC) MRI-derived perfusion parameters to characterize the hemodynamic effects of dexamethasone in a 9L gliosarcoma tumor model. Twenty-four rats underwent intracerebral inoculation with 9L tumor cells. Fifteen were treated with a total of 3mg/kg of dexamethasone on days 10–14 post-inoculation, while the remaining 9 rats served as controls. Fourteen days post-inoculation, MRI images, sensitive to total and micro-vascular cerebral blood flow (CBF), mean transit time (MTT), and intravoxel transit time distributions (TTD)s were obtained using a simultaneous gradient-echo(GE)/spin-echo(SE) DSC-MRI method. Dexamethasone-treated animals had a microvascular (SE) tumor CBF that was 45.9% higher ( p = 0.0008) and a MTT that was 47.8% lower ( p = 0.0005) than untreated animals. With treatment, there was a non-significant 91.3% increase in total (GE) vascular CBF ( p = 0.35), and a significant decrease in MTT (49.1%, p = 0.02). The total vascular and microvascular TTDs from the treated tumors were similar to normal brain, unlike the TTDs in the untreated tumors. These findings demonstrate that DSC-MRI perfusion methods can be used to non-invasively detect the morphological and functional changes in tumor vasculature that occur in response to dexamethasone treatment.

The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis

2015 ◽  
Vol 165 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Letizia Squarcina ◽  
Cinzia Perlini ◽  
Denis Peruzzo ◽  
Umberto Castellani ◽  
Veronica Marinelli ◽  
...  
Keyword(s):  
First Episode Psychosis ◽  
First Episode ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Episode Psychosis ◽  
Susceptibility Contrast

scholarly journals Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

2019 ◽  
Author(s):  
Carole H. Sudre ◽  
Jasmina Panovska-Griffiths ◽  
Eser Sanverdi ◽  
Sebastian Brandner ◽  
Vasileios K. Katsaros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic Value ◽  
Dynamic Susceptibility ◽  
Idh Mutation ◽  
Dynamic Susceptibility Contrast ◽  
Mutation Status ◽  
Dsc Mri ◽  
Grade Difference ◽  
Grade Ii ◽  
Susceptibility Contrast

AbstractBackgroundMachine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.Methods333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsFeatures from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1.ConclusionDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.Key points-On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% <1 grade difference)-Shape features distinguish best grade II from grade III gliomas.-Texture and distribution features distinguish best grade III from grade IV tumours.Importance of studyThis work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% <1 grade difference) and 71% across mutation status) was obtained. Therefore, our results provide a proof-of-concept for this emerging precision medicine field that has good generalisability and scalability properties. Introspection on the classification errors highlighted mostly borderline cases and helped underline the challenges of a categorical classification in a pathological continuum.With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

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