Estudio de la perfusión cerebral mediante técnicas de susceptibilidad magnética: técnica y aplicaciones

Ways of analysing extracellular gadolinium enhancement

The use of extracellular gadolinium-based contrast agents provides valuable information in magnetic resonance studies, thus increasing diagnostic confidence. These contrast agents make it easier to detect and define injuries, and narrow down the differential diagnosis. They are indicated for several different reasons, both for diagnostic purposes and for evaluating the response to treatment. Morphological analysis can assess the type of uptake, the qualitative and semiquantitative study of the signal intensity vs time curves in multiphase sequences, and the quantitative analysis of the uptake with T1 or T2* perfusion studies associated with pharmacokinetic models. Multiphase dynamic studies with 3D sequences contain valuable information that is not exploited by a simple visual analysis of 2D images. To take advantage of this information and the imaging biomarkers provided, computational analysis should be used. To this end, the future role of artificial intelligence is increasingly evident.

Arterial spin labeling in the grading of brain gliomas: could it help?

Background

Gliomas are characterized by high morbidity and mortality with low cure and high recurrence rates, which depends to a great degree on the angiogenesis of the tumor. Assessment of such angiogenesis by perfusion techniques is of utmost importance for the preoperative grading of gliomas. The purpose of this study was to assess the role of arterial spin labeling (ASL) perfusion as a non-contrast MRI technique in the grading of brain gliomas, in correlation with the dynamic susceptibility contrast perfusion imaging (DSC-PI). The study was carried out on 35 patients admitted to the Neurosurgery Department with MRI features of gliomas and sent for further perfusion imaging. Non-contrast ASL followed by DSC-PI was done for all cases. The final diagnosis of the cases was established by histopathology.

Results

Fourteen patients (14/35) had low-grade gliomas while twenty-one (21/35) had high-grade gliomas. In low-grade gliomas, four cases out of 14 were falsely graded as high-grade tumors showing hyperperfusion on ASL, three of which showed DSC-PI hypoperfusion. In high-grade gliomas, two cases out of 21 were interpreted as an indeterminate grade by ASL showing isoperfusion, however showed hyperperfusion on DSC-PI. ROC curve analysis showed ASL-derived rCBF > 2.08 to have 80.95% sensitivity, 85.71% specificity, and overall accuracy of 82.86% compared to 100% sensitivity, specificity, and accuracy of DSC-PI-derived rCBV and rCBF of > 1.1 and > 0.9, respectively. A significant positive correlation was noted between ASL and DSC-PI with correlation coefficient reaching r = 0.80 between ASL-rCBF and DSC-rCBF (p < 0.01) and r = 0.68 between ASL and DSC-rCBV (p < 0.01).

Conclusions

ASL is a relatively recent non-contrast perfusion technique that obtains results which are in fair agreement with the more established DSC perfusion imaging making it an alternative method for preoperative assessment of perfusion of gliomas, especially for patients with contraindications to contrast agents.

Detection of severity in Alzheimer's disease (AD) using computational modeling

The prevalent cause of dementia - Alzheimer's disease (AD) is characterized by an early cholinergic deficit that is in part responsible for the cognitive deficits (especially memory and attention defects). Prolonged AD leads to moderate-to-severe AD, which is one of the leading causes of death. Placebo-controlled, randomized clinical trials have shown significant effects of Acetyl cholin esterase inhibitors (ChEIs) on function, cognition, activities of daily living (ADL) and behavioral symptoms in patients. Studies have shown comparable effects for ChEIs in patients with moderate-to-severe or mild AD. Setting a fixed measurement (e.g. a Mini-Mental State Examination score, as a 'when to stop treatment limit) for the disease is not clinically rational. Detection of changed regional cerebral blood flow in mild cognitive impairment and early AD by perfusion-weighted magnetic resonance imaging has been a challenge. The utility of perfusion-weighted magnetic resonance imaging (PW-MRI) for detecting changes in regional cerebral blood flow (rCBF) in patients with mild cognitive impairment (MCI) and early AD was evaluated. We describe a computer aided prediction model to determine the severity of AD using known data in literature. We designed an automated system for the determination of AD severity. It is used to predict the clinical cases and conditions with disagreements from specialist. The model described is useful in clinical practice to validate diagnosis.

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T₁ ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (K^trans , v_p and K_b ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms

Introduction

Arterial input function (AIF) plays an important role in the quantification of cerebral hemodynamics. The purpose of this study was to select the best reproducible clustering method for AIF detection by comparing three algorithms reported previously in terms of detection accuracy and computational complexity.

Methods

First, three reproducible clustering methods, normalized cut (Ncut), hierarchy (HIER), and fast affine propagation (FastAP), were applied independently to simulated data which contained the true AIF. Next, a clinical verification was performed where 42 subjects participated in dynamic susceptibility contrast MRI (DSC-MRI) scanning. The manual AIF and AIFs based on the different algorithms were obtained. The performance of each algorithm was evaluated based on shape parameters of the estimated AIFs and the true or manual AIF. Moreover, the execution time of each algorithm was recorded to determine the algorithm that operated more rapidly in clinical practice.

Results

In terms of the detection accuracy, Ncut and HIER method produced similar AIF detection results, which were closer to the expected AIF and more accurate than those obtained using FastAP method; in terms of the computational efficiency, the Ncut method required the shortest execution time.

Conclusion

Ncut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency.

Development and validation of an open source quantification tool for DSC-MRI studies

MOTIVATION

This work presents the development of an open source tool for the quantification of dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies. The development of this tool is motivated by the lack of open source tools implemented on open platforms to allow external developers to implement their own quantification methods easily and without the need of paying for a development license.

MATERIALS AND METHODS

This quantification tool was developed as a plugin for the ImageJ image analysis platform using the Java programming language. A modular approach was used in the implementation of the components, in such a way that the addition of new methods can be done without breaking any of the existing functionalities. For the validation process, images from seven patients with brain tumors were acquired and quantified with the presented tool and with a widely used clinical software package. The resulting perfusion parameters were then compared.

RESULTS

Perfusion parameters and the corresponding parametric images were obtained. When no gamma-fitting is used, an excellent agreement with the tool used as a gold-standard was obtained (R(2)>0.8 and values are within 95% CI limits in Bland-Altman plots).

CONCLUSION

An open source tool that performs quantification of perfusion studies using magnetic resonance imaging has been developed and validated using a clinical software package. It works as an ImageJ plugin and the source code has been published with an open source license.

Evaluating the feasibility of an agglomerative hierarchy clustering algorithm for the automatic detection of the arterial input function using DSC-MRI

During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.

Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection.

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

OBJECTIVES

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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