Detection of the Arterial Input Function Using DSC-MRI Data

Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed, and their advantages and disadvantages are listed.

Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing

Accurate skull-stripping is crucial preprocessing in dynamic susceptibility contrast-enhanced perfusion magnetic resonance data analysis. The presence of non-brain tissues impacts the perfusion parameters assessment. In this study, we propose different integration strategies for the spatial and channel squeeze and excitation attention mechanism into the baseline U-Net+ResNet neural network architecture to provide automatic skull-striping i.e., Standard scSE, scSE-PRE, scSE-POST, and scSE Identity strategies of plugging of scSE block into the ResNet backbone. We comprehensively investigate the performance of skull-stripping in T2-star weighted MR images with abnormal brain anatomy. The comparison that utilizing any of the proposed strategies provides the robustness of skull-stripping. However, the scSE-POST integration strategy provides the best result with an average Dice Coefficient of 0.9810.

Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy

The brain perfusion ROI detection being a preliminary step, designed to exclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracy is considered as the key factor for delivering correct results of perfusion data analysis. Despite the large variety of algorithms developed on brain tissues segmentation, there is no one that works reliably and robustly on 2T-waited MR images of a human head with abnormal brain anatomy. Therefore, thresholding method is still the state-of-the-art technique that is widely used as a way of managing pixels involved in brain perfusion ROI. This paper presents the analysis of effectiveness of thresholding techniques in brain perfusion ROI detection on 2T-waited MR images of a human head with abnormal brain anatomy. Four threshold-based algorithms implementation are considered: according to Otsu method as global thresholding, according to Niblack method as local thresholding, thresholding in approximate anatomical brain location, and brute force thresholding. The analysis is done using comparison of qualitative maps produced from thresholded images and from the reference ones. Pearson correlation analysis showed strong positive (r was ranged from 0.7123 to 0.8518, p<0.01) and weak positive (r<0.35, p<0.01) relationship in case of conducted experiments with CBF, CBV, MTT and Tmax maps, respectively. Linear regression analysis showed at level of 95% confidence interval that maps produced from thresholded images were subject to scale and offset errors in all conducted experiments. The experimental results showed that widely used thresholding methods are an ineffective way of managing pixels involved in brain perfusion ROI. Thresholding as brain segmentation tool can lead to poor placement of perfusion ROI and, as a result, produced maps will be subject to artifacts and can cause falsely high or falsely low perfusion parameters assessment.

Impact of perfusion ROI detection to the quality of CBV perfusion map

The object of research in this study is quality of CBV perfusion map, considering detection of perfusion ROI as a key component in processing of dynamic susceptibility contrast magnetic resonance images of a human head. CBV map is generally accepted to be the best among others to evaluate location and size of stroke lesions and angiogenesis of brain tumors. Its poor accuracy can cause failed results for both quantitative measurements and visual assessment of cerebral blood volume. The impact of perfusion ROI detection on the quality of maps was analyzed through comparison of maps produced from threshold and reference images of the same datasets from 12 patients with cerebrovascular disease. Brain perfusion ROI was placed to exclude low intensity (air and non-brain tissues regions) and high intensity (cerebrospinal fluid regions) pixels. Maps were produced using area under the curve and deconvolution methods. For both methods compared maps were primarily correlational according to Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curve and deconvolution, respectively, p<2.2*10^-16. In spite of this, for both methods scatter plots had data points associated with missed blood regions and regression lines indicated presence of scale and offset errors for maps produced from threshold images. Obtained results indicate that thresholding is an ineffective way to detect brain perfusion ROI, which usage can cause degradation of CBV map quality. Perfusion ROI detection should be standardized and accepted into validation protocols of new systems for perfusion data analysis.

CUSUM Filter for Brain Segmentation on DSC Perfusion MR Head Scans with Abnormal Brain Anatomy

This paper presents a new approach for relatively accurate brain region of interest (ROI) detection from dynamic susceptibility contrast (DSC) perfusion magnetic resonance (MR) images of a human head with abnormal brain anatomy. Such images produce problems for automatic brain segmentation algorithms, and as a result, poor perfusion ROI detection affects both quantitative measurements and visual assessment of perfusion data. In the proposed approach image segmentation is based on CUSUM filter usage that was adapted to be applicable to process DSC perfusion MR images. The result of segmentation is a binary mask of brain ROI that is generated via usage of brain boundary location. Each point of the boundary between the brain and surrounding tissues is detected as a change-point by CUSUM filter. Proposed adopted CUSUM filter operates by accumulating the deviations between the observed and expected intensities of image points at the time of moving on a trajectory. Motion trajectory is created by the iterative change of movement direction inside the background region in order to reach brain region, and vice versa after boundary crossing. Proposed segmentation approach was evaluated with Dice index comparing obtained results to the reference standard. Manually marked brain region pixels (reference standard), as well as visual inspection of detected with CUSUM filter usage brain ROI, were provided by experienced radiologists. The results showed that proposed approach is suitable to be used for brain ROI detection from DSC perfusion MR images of a human head with abnormal brain anatomy and can, therefore, be applied in the DSC perfusion data analysis.

Detection of perfusion ROI as a quality control in perfusion analysis

In perfusion analysis automated approaches for image processing is preferable due to reduce time-consuming tasks for radiologists. Assessment of perfusion results quality is important step in development of algorithms for automated processing. One of them is an assessment of perfusion maps quality based on detection of perfusion ROI.