Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.

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Signal profiles of dynamic susceptibility contrast MRI data of brain tumors reflect hemodynamic properties of tumor tissue.

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Features in signal profiles extracted by machine learning methods revealed the hemodynamic heterogeneity of the gliomas.

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The reported approach is a new strategy to characterize the intra-tumor heterogeneity and physiological properties of gliomas.

Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload

BACKGROUND AND PURPOSE

Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli.

MATERIALS AND METHODS

Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization.

RESULTS

Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration.

CONCLUSION

The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.

Evaluation of Encephaloduroarteriosynangiosis Efficacy Using Probabilistic Independent Component Analysis Applied to Dynamic Susceptibility Contrast Perfusion MRI

BACKGROUND AND PURPOSE

Indirect cerebral revascularization has been successfully used for treatment in Moyamoya disease and symptomatic intracranial atherosclerosis. While angiographic neovascularization has been demonstrated after surgery, measurements of local tissue perfusion are scarce and may not reflect the reported successful clinical outcomes. We investigated probabilistic independent component analysis and conventional perfusion parameters from DSC-MR imaging to measure postsurgical changes in tissue perfusion.

MATERIALS AND METHODS

In this prospective study, 13 patients underwent unilateral indirect cerebral revascularization and DSC-MR imaging before and after surgery. Conventional perfusion parameters (relative cerebral blood volume, relative cerebral blood flow, and TTP) and probabilistic independent components that reflect the relative contributions of DSC signals consistent with arterial, capillary, and venous hemodynamics were calculated and examined for significant changes after surgery. Results were compared with postsurgical DSA studies to determine whether changes in tissue perfusion were due to postsurgical neovascularization.

RESULTS

Before surgery, tissue within the affected hemisphere demonstrated a high probability for hemodynamics consistent with venous flow and a low probability for hemodynamics consistent with arterial flow, whereas the contralateral control hemisphere demonstrated the reverse. Consistent with symptomatic improvement, the probability for venous hemodynamics within the affected hemisphere decreased with time after surgery (P = .002). No other perfusion parameters demonstrated this association. Postsurgical DSA revealed an association between an increased preoperative venous probability in the symptomatic hemisphere and neovascularization after surgery.

CONCLUSIONS

Probabilistic independent component analysis yielded sensitive measurements of changes in local tissue perfusion that may be associated with newly formed vasculature after indirect cerebral revascularization surgery.

Quantitative measurement of blood flow in paediatric brain tumours-a comparative study of dynamic susceptibility contrast and multi time-point arterial spin labelled MRI

Objective:

Arterial spin-labelling (ASL) MRI uses intrinsic blood water to quantify the cerebral blood flow (CBF), removing the need for the injection of a gadolinium-based contrast agent used for conventional perfusion imaging such as dynamic susceptibility contrast (DSC). Owing to the non-invasive nature of the technique, ASL is an attractive option for use in paediatric patients. This work compared DSC and multi-timepoint ASL measures of CBF in paediatric brain tumours.

Methods:

Patients (n = 23; 20 low-grade tumours and 3 high-grade tumours) had DSC and multi-timepoint ASL with and without vascular crushers (VC). VC removes the contribution from larger vessel blood flow. Mean perfusion metrics were extracted from control and T₁-enhanced tumour regions of interest (ROIs): arterial arrival time (AAT) and CBF from the ASL images with and without VC, relative cerebral blood flow (rCBF), relative cerebral blood volume, delay time (DT) and mean transit time (MTT) from the DSC images.

Results:

Significant correlations existed for: AAT and DT (r = 0.77, p = 0.0002) and CBF and rCBF (r = 0.56, p = 0.02) in control ROIs for ASL-noVC. No significant correlations existed between DSC and ASL measures in the tumour region. Significant differences between control and tumour ROI were found for MTT (p < 0.001) and rCBF (p < 0.005) measures.

Conclusion:

Significant correlations between ASL-noVC and DSC measures in the normal brain suggest that DSC is most sensitive to macrovascular blood flow. The absence of significant correlations within the tumour ROI suggests that ASL is sensitive to different physiological mechanisms compared with DSC measures.

Advances in knowledge:

ASL provides information which is comparable with that of DSC in healthy tissues, but appears to reflect a different physiology in tumour tissues.

Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

An computer-aided diagnosis system of pathological brain detection (PBD) is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy (WPTE) from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine (FSVM). We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method “WPTE + FSVM” is effective in PBD.