Cerebral blood flow estimation from perfusion-weighted MRI using FT-based MMSE filtering method

INTRODUCTION: Perfusion-weighted MRI can be used for estimating blood flow parameters using bolus tracking technique based on dynamic susceptibility contrast MRI. In order to extract flow parameters, several deconvolution techniques have been proposed, of which the singular value decomposition (SVD) and Fourier transform (FT)-based techniques are more popular and widely used. In this work, an FT-based method has been proposed that involves derivation of an optimal shaped filter (defined as a filter function) estimated using minimum mean-squared error (MMSE) technique in the frequency domain. The proposed technique has been compared with the well-established SVD technique using simulation experiments. SIMULATION METHODS: Simulation was performed in multiple steps. An arterial input function (AIF) was first defined based on a certain blood flow value. The T2* signal change was then derived from this AIF, and noise was added to the signal. Then, a unique and optimal shaped filter function Phi(f) was derived in order to obtain the best estimate of scaled residue function. One way is by minimizing the mean-squared error between the noiseless and noisy scaled residue function, i.e., using an MMSE method. The effect of low and moderate noise and distorted AIF on cerebral blood flow (CBF) estimates was obtained by using FT-based MMSE method. Results were compared with the SVD technique. In this work, SVD technique was assumed to be the standard reference deconvolution technique. RESULTS AND DISCUSSION: For low-noise condition, the FT-based technique was more stable than the SVD technique, while for moderate noise, both techniques consistently underestimated CBF. SVD technique was found to be more stable in presence of AIF distortions. However, SVD technique was found to be unstable due to AIF delay compared to the FT-based MMSE method. The shaped filter function was found to be sensitive to effect of AIF distortions.     

Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subject's recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis.     

Aspects on the accuracy of cerebral perfusion parameters obtained by dynamic susceptibility contrast MRI: a simulation study

Several studies have indicated that deconvolution based on singular value decomposition (SVD) is a robust concept for retrieval of cerebral blood flow in dynamic susceptibility contrast (DSC) MRI. However, the behavior of the technique under typical experimental conditions has not been completely investigated. In the present study, cerebral perfusion was simulated using different temporal resolutions, different signal-to-noise ratios (S/Ns), different shapes of the arterial input function (AIF), different signal drops, and different cut-off levels in the SVD deconvolution. Using Zierler's area-to-height relationship in combination with the central volume theorem, calculations of regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), and regional mean transit time (rMTT) were accomplished, based on simulated DSC-MRI signal curves corresponding to artery, gray matter (GM), white matter (WM), and ischemic tissue. Gaussian noise was added to the noise-free signal curves to generate different S/Ns. We studied image time intervals of 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 s, as well as different degrees of signal decrease. The singular-value threshold in the SVD procedure and the shape of the AIF were also varied. Increased rCBF was seen when noise was added, especially for rCBF in WM at the larger image time intervals. The rCBF showed large standard deviations using a low threshold value. A prolonged time interval led to a lower absolute value of rCBF both in GM and WM, and a low/broad AIF also underestimated the rCBF. When a larger maximal signal decrease was assumed, smaller standard deviations were observed. No systematic change of the average rCBV was observed with increasing noise or with increasing image time interval. At S/N = 40, a low cut-off value resulted in an rCBF that was closer to the true value. Furthermore, at low S/N it was difficult to differentiate ischemic tissue from WM.     

Validation study of a pulsed arterial spin labeling technique by comparison to perfusion computed tomography

Abnormalities in cerebral blood flow (CBF) are believed to play a significant role in the development of major neonatal neuropathologies. One approach that would appear ideal for measuring CBF in this fragile age group is arterial spin labeling (ASL) since ASL techniques are noninvasive and quantitative. The purpose of this study was to assess the accuracy of a pulsed ASL method implemented on a 3-T scanner dedicated to neonatal imaging. Cerebral blood flow was measured in nine neonatal piglets, the ASL–CBF measurements were acquired at two inversion times (TI) (1200 and 1700 ms), and CBF was measured by perfusion computed tomography (pCT) for validation. Perfusion CT also provided images of cerebral blood volume, which were used to identify large blood vessels, and contrast arrival time, which were used to assess differences in arterial transit times between gray and white matter. Good agreement was found between gray matter CBF values from pCT (76±1 ml/min per 100 g) and ASL at TI=1700 ms (73±1 ml/min per 100 g). At TI=1200 ms, ASL overestimated CBF (91±2 ml/min per 100 g), which was attributed to substantial intravascular signal. No significant differences in white matter CBF from pCT and ASL were observed (average CBF=60±1 ml/min per 100 g), nor was there any difference in contrast arrival times for gray and white matter (0.95±0.04 and 0.99±0.03 s, respectively), which suggests that the arterial transit times for ASL were the same in this animal model. This study verified the accuracy of the implemented ASL technique and showed the value of using pCT to study other factors that can affect ASL–CBF measurements.     

Application of FT-based MMSE deconvolution method for cerebral blood flow measurement in patients with leukoaraiosis

Introduction

The bolus-tracking (BT) technique is the most popular perfusion-weighted (PW) dynamic susceptibility contrast MRI method used for estimating cerebral blood flow (CBF), cerebral blood volume and mean transit time. The BT technique uses a convolution model that establishes the input–output relationship between blood flow and the vascular tracer concentration. Singular value decomposition (SVD)- and Fourier transform (FT)-based deconvolution methods are popular and widely used for estimating PW MRI parameters. However, from the published literature, it appears that SVD is more widely accepted than other methods. In a previous article, an FT-based minimum mean-squared error (MMSE) technique was proposed and simulation experiments were performed to compare it with the well-established circular SVD (oSVD) method. In this study, the FT-based MMSE method has been used to estimate relative CBF (rCBF) in 13 patients with white matter lesions (WMLs) (leukoaraiosis), and results are compared with the widely used oSVD method.

Materials and Methods

Thirteen patients with leukoaraiosis were imaged on a 1.5-T Siemens whole-body scanner. After acquiring the localizer and structural scans consisting of FLAIR (fluid attenuated with inversion recovery), T₁-weighted and T₂-weighted images, perfusion study was implemented as part of the MRI protocol. For each patient and method, two values were calculated: (a) rCBF for normal white matter (NWM) ROI, obtained by dividing the average CBF value in NWM ROI with average CBF in gray matter (GM) ROI, and (b) rCBF for WML ROI, obtained by dividing the average CBF value in WML ROI with average CBF in GM ROI. Results for the two deconvolution methods were computed.

Results and Discussion

A significant (P<.05) decrease in estimated rCBF was observed in the WML in all the patients using the MMSE method, while for the oSVD method, the decrease was observed in all but one patient. Initial results suggest that the MMSE method is comparable to the oSVD method for estimating rCBF in NMW while it may be better than oSVD for estimating rCBF in lesions of low flow. Studies involving a larger patient population may be required to further validate the findings of this work.     

Nonlinear dynamic range compression deconvolution

We introduce a dynamic range image compression technique for nonlinear deconvolution; the impulse response of the distortion function and the noisy distorted image are jointly transformed to pump a clean reference beam in a two-beam coupling arrangement. The Fourier transform of the pumped reference beam contains the deconvolved image and its conjugate. In contrast to standard deconvolution approaches, for which noise can be a limiting factor in the performance, this approach allows the retrieval of distorted signals embedded in a very high-noise environment.     

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T₂ mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method.ResultsThe proposed adversarial learning approach achieved accurate T₂ mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T₂ estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning.ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.     

Magnetic resonance image enhancement using stochastic resonance in Fourier domain

Objective

In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR).

Materials and Methods

We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively.

Results

Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures.

Conclusions

The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.     

An alternative viewpoint of the similarities and differences of SVD and FT deconvolution algorithms used for quantitative MR perfusion studies

Quantitative cerebral blood flow (CBF) values can be determined from residue function estimates obtained from magnetic resonance dynamic susceptibility contrast (DSC) perfusion studies using a variety of deconvolution approaches. However, there are significant differences between the CBF estimates obtained, differences that are not simply due to minor details of the implementation of the algorithms. The standard singular value decomposition (sSVD) shows a variation of CBF values with arterial-tissue delay (ATD) not present with the Fourier transform deconvolution algorithm. Fourier transform deconvolution and the newly suggested delay-invariant SVD algorithm implementations provide CBF estimates whose accuracy changes with tissue mean transit times (MTTs). Techniques combining sSVD with deliberate ATD manipulation have been proposed to compensate for this inaccuracy. Other studies indicate that CBF changes related to slice position in a multislice study, and other experimental factors, can be reduced using interpolative deconvolution algorithms. In this review, we use both time-domain and frequency-domain analysis to show the underlying theoretical relationships between these many approaches to obtain "the best" CBF estimate. This model allows us to better understand the similarities and differences, advantages and disadvantages between these variants of the deconvolution algorithms used in DSC perfusion studies.     

Effects of variable blast pressures on blood flow and oxygen saturation in rat brain as evidenced using MRI

It has been recognized that primary blast waves may result in neurotrauma in soldiers in theater. A new type of contrast used in magnetic resonance imaging (MRI), susceptibility-weighted imaging (SWI), has been developed that is based on the different susceptibility levels in diverse tissues and can detect decreases in cerebral blood flow (CBF) using inferred oxygen saturation changes in tissue. In addition, a continuous arterial spin-labeled (ASL) MRI sequence was used as a direct measure of regional CBF within the brain tissue. Animals were subjected to whole-body blast exposures of various overpressures within a gas-driven shock tube. When exposed to low levels of overpressure, most rats demonstrated no obvious changes between pre- and postexposure in the conventional MR images. CBF changes measured by SWI and ASL were significantly higher for the overpressure exposed groups as compared to the sham group and tended to increase with pressure increases at the highest two pressures. In the hippocampus, all blast animals had a reduction in the CBF consistently in the range of 0-27%. In summary, low levels of primary blast pressure exposure demonstrated a significant physiologic effect to the brain up to 72 h postexposure.     

Improvement of ultrasound speckle image velocimetry using image enhancement techniques

Ultrasound-based techniques have been developed and widely used in noninvasive measurement of blood velocity. Speckle image velocimetry (SIV), which applies a cross-correlation algorithm to consecutive B-mode images of blood flow has often been employed owing to its better spatial resolution compared with conventional Doppler-based measurement techniques. The SIV technique utilizes speckles backscattered from red blood cell (RBC) aggregates as flow tracers. Hence, the intensity and size of such speckles are highly dependent on hemodynamic conditions. The grayscale intensity of speckle images varies along the radial direction of blood vessels because of the shear rate dependence of RBC aggregation. This inhomogeneous distribution of echo speckles decreases the signal-to-noise ratio (SNR) of a cross-correlation analysis and produces spurious results. In the present study, image-enhancement techniques such as contrast-limited adaptive histogram equalization (CLAHE), min/max technique, and subtraction of background image (SB) method were applied to speckle images to achieve a more accurate SIV measurement. A mechanical sector ultrasound scanner was used to obtain ultrasound speckle images from rat blood under steady and pulsatile flows. The effects of the image-enhancement techniques on SIV analysis were evaluated by comparing image intensities, velocities, and cross-correlation maps. The velocity profiles and wall shear rate (WSR) obtained from RBC suspension images were compared with the analytical solution for validation. In addition, the image-enhancement techniques were applied to in vivo measurement of blood flow in human vein. The experimental results of both in vitro and in vivo SIV measurements show that the intensity gradient in heterogeneous speckles has substantial influence on the cross-correlation analysis. The image-enhancement techniques used in this study can minimize errors encountered in ultrasound SIV measurement in which RBCs are used as flow tracers instead of exogenous contrast agents.     

Spectral estimation method for noisy data using a noise reference

The development of array processing methods to extract the useful characteristics of acoustic sources such as their locations and absolute levels, starting from the measured sound field is one of the main issues in aero-acoustics. The conventional beamforming method is a very popular technique investigated to solve the power level estimation problem. It has the advantage of being robust, easy to implement and cheap in computation time. However, this technique is also known for having poor spatial resolution capabilities which prevents the correct source levels being obtained for numerous practical applications. Deconvolution techniques of the result computed with CBF, with the point spread function of the array manifold, may restore the power levels of the acoustic sources that would be observed in the absence of the array resolution effects. However, the accuracy of the results provided by deconvolution methods is very sensitive to background noise, always present in acoustic measurements. This process should be carried out after the additive noise has been suitably attenuated and, ideally, the deconvolution operator should amplify the noise as little as possible. Another approach is described in the article. It consists in using a noise reference and a new technique called spectral estimation method with additive noise to remove both the smearing effect produced by the array response and the background noise. The technique has been applied to computer and experimental simulations conducted both in an anechoic chamber and in the test section of an open wind tunnel involving acoustic sources radiating in a noisy environment. The levels of the sources were found with a good level of accuracy and the background noise greatly reduced, confirming the validity of the approach and the satisfactory performance of the method proposed.     

Rapid MR imaging by sensitivity profile indexing and deconvolution reconstruction (SPID)

A new parallel MR imaging technique, which uses localized information from the elements of a multi-coil array to accelerate imaging, is described. The technique offers an alternative reconstruction approach to currently available techniques (e.g., SMASH and SENSE). Following a partial k-space data acquisition, image reconstruction in this approach proceeds in two steps: first, fitting the measured coil sensitivities to a set of partially localized target functions, a blurred intermediate image of the studied object is produced. Blurring is obtained in a systematic manner, forming images of the studied object convolved with a known convolution kernel. Full spatial resolution is then recovered by deconvolution of the blurred images with the known kernel function. The technique offers flexibility in the arrangement of the acquired signal data k-lines, and a mechanism for controlling reconstruction quality through the convolution the deconvolution procedure. The technique was validated in phantom and in vivo imaging experiments demonstrating high time reduction factors.     

Quantitative multi-modal functional MRI with blood oxygenation level dependent exponential decays adjusted for flow attenuated inversion recovery (BOLDED AFFAIR)

A magnetic resonance imaging (MRI) method is described that allows interleaved measurements of transverse (R(2)(*) and R(2)) and longitudinal (R(1)) relaxation rates of tissue water in conjunction with spin labeling. The image-contrasts are intrinsically blood oxygenation level dependent (BOLD) and cerebral blood flow (CBF) weighted, but each contrast is made quantitative by two echo time (TE) and inversion recovery time (TIR) acquisitions with gradient echo (GE) and spin echo (SE) weighted echo-planar imaging (EPI). The EPI data were acquired at 7 Tesla with nominal spatial resolution of 430 x 430 x 1000 microm(3) in rat brain in vivo. The method is termed as blood oxygenation level dependent exponential decays adjusted for flow attenuated inversion recovery (BOLDED AFFAIR) and allows acquisition of R(2)(*), R(2), and CBF maps in an interleaved manner within approximately 12 minute. The basic theory of the method, associated experimental/systematic errors, and temporal restrictions are discussed. The method is validated by comparison of multi-modal maps obtained by BOLDED AFFAIR (i.e., two TE and TIR values with GE and SE sequences) and conventional approach (i.e., multiple TE and TIR values with GE and SE sequences) during varied levels of whole brain activity. Preliminary functional data from a rat forepaw stimulation model demonstrate the feasibility of this method for functional MRI (fMRI) studies. It is expected that with appropriate precautions this method in conjunction with contrast agent-based MRI has great potential for quantitative fMRI studies of mammalian cortex.     

Pharmacokinetic analysis of glioma compartments with dynamic Gd-DTPA-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is widely used for measuring perfusion and blood volume, especially cerebral blood volume (CBV). In case of blood-brain barrier (BBB) disruption, the conventional techniques only partially determine the pharmacokinetic parameters of contrast medium (CM) exchange between different compartments. Here a modified pharmacokinetic model is applied, which is based on the bidirectional CM exchange between blood and two interstitial compartments in terms of the fractional volumes of the compartments and the vessel permeabilities between them. The evaluation technique using this model allows one to quantify the fractional volumes of the different compartments (blood, cells, slowly and fast enhancing interstitium) as well as the vessel permeabilities and cerebral blood flow (CBF) with a single T1-weighted dynamic MRI measurement. The method has been successfully applied in 25 glioma patients for generating maps of all of these parameters. The fractional volume maps allow for the differentiation of glioma vascularization types. The maps show a good correlation with the histological grading of these tumors. Furthermore, regions with enhanced interstitial volumes are found in high-grade gliomas. Differences in permeability maps of Gd-DTPA apart from BBB disruption do not exist between different tissue types. CBF measured in high-grade glioma is less pronounced than it would be expected from their blood volume. Therefore pharmacokinetic imaging provides an additional tool for glioma characterization.     

Comparison of pulsed arterial spin labeling encoding schemes and absolute perfusion quantification

Arterial spin labeling (ASL) using magnetic resonance imaging (MRI) is a powerful noninvasive technique to investigate the physiological status of brain tissue by measuring cerebral blood flow (CBF). ASL assesses the inflow of magnetically labeled arterial blood into an imaging voxel. In the last 2 decades, various ASL sequences have been proposed which differ in their ease of implementation and their sensitivity to artifacts. In addition, several quantification methods have been developed to determine the absolute value of CBF from ASL magnetization difference images. In this study, we evaluated three pulsed ASL sequences and three absolute quantification schemes. It was found that FAIR-QUIPSSII implementation of ASL yields 10–20% higher signal-to-noise ratio (SNR) and 18% higher CBF as compared with PICORE-Q2TIPS (with FOCI pulses) and PICORE-QUIPSSII (with BASSI pulses). In addition, quantification schemes employed can give rise to up to a 35% difference in CBF values. We conclude that, although all quantitative ASL sequences and CBF calibration methods should in principle result in the similar CBF values and image quality, substantial differences in CBF values and SNR were found. Thus, comparing studies using different ASL sequences and analysis algorithms is likely to result in erroneous intra- and intergroup differences. Therefore, (i) the same quantification schemes should consistently be used, and (ii) quantification using local tissue proton density should yield the most accurate CBF values because, although still requiring definitive demonstration in future studies, the proton density of blood is assumed to be very similar to the value of gray matter.     

Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling

Purpose

To compare absolute cerebral blood flow (CBF) estimates obtained by model-free arterial spin labeling (ASL) and dynamic susceptibility contrast MRI (DSC-MRI), corrected for partial volume effects (PVEs).

Methods

CBF was measured using DSC-MRI and model-free ASL (quantitative signal targeting with alternating radiofrequency labeling of arterial regions) at 3 T in 15 subjects with brain tumor, and the two modalities were compared with regard to CBF estimates in normal gray matter (GM) and DSC-to-ASL CBF ratios in selected tumor regions. The DSC-MRI CBF maps were calculated using a global arterial input function (AIF) from the sylvian-fissure region, but, in order to minimize PVEs, the AIF time integral was rescaled by a venous output function time integral obtained from the sagittal sinus.

Results

In GM, the average DSC-MRI CBF estimate was 150±45 ml/(min 100 g) (mean±SD) while the corresponding ASL CBF was 44±10 ml/(min 100 g). The linear correlation between GM CBF estimates obtained by DSC-MRI and ASL was r=.89, and observed DSC-to-ASL CBF ratios differed by less than 3% between GM and tumor regions.

Conclusions

A satisfactory positive linear correlation between the CBF estimates obtained by model-free ASL and DSC-MRI was observed, and DSC-to-ASL CBF ratios showed no obvious tissue dependence.     

Differences between arterial occlusive and cortical photothrombosis stroke models with magnetic resonance imaging and microtubule-associated protein-2 immunoreactivity

The differences between two models of cerebral ischemia [middle cerebral arterial transection (MCAT) and cortical photothrombosis (PT)] were explored with multiparametric MRI of apparent diffusion coefficient trace (ADCtr), cerebral blood flow (CBF) and T1. Microtubule-associated protein-2 (MAP2) immunoreactivity sections aligned with the MR images in the same coronal plane were used to map the infarct and to guide region-of-interest selection. In ischemic cortex, the larger T1 increase in PT versus MCAT (42+/-7% vs. 16+/-5%) is related to the different character of edema between these models; yet, neither CBF nor ADCtr discriminated between them at 3.5 h, suggesting that different mechanisms of ischemic damage to the brain cells resulted in the same ADCtr value. CBF and ADCtr were depressed in immediately adjacent ischemic border by 27+/-7% and 47+/-10%, respectively, in MCAT but not in PT, suggesting marginal perfusion in MCAT. CBF in homotopic normal cortex in the opposite hemisphere was higher for PT compared with MCAT (199+/-20 and 134+/-10 ml/100 g/min, respectively). Different pathological processes in the two models affect CBF, ADCtr and T1 in a unique, regionally specific manner. The PT model differs substantially from the MCAT and is not a model of cortical ischemia with an appreciable border zone.     

Multicontext wavelet-based thresholding segmentation of brain tissues in magnetic resonance images

A novel segmentation method based on wavelet transform is presented for gray matter, white matter and cerebrospinal fluid in thin-sliced single-channel brain magnetic resonance (MR) scans. On the basis of the local image model, multicontext wavelet-based thresholding segmentation (MCWT) is proposed to classify 2D MR data into tissues automatically. In MCWT, the wavelet multiscale transform of local image gray histogram is done, and the gray threshold is gradually revealed from large-scale to small-scale coefficients. Image segmentation is independently performed in each local image to calculate the degree of membership of a pixel to each tissue class. Finally, a strategy is adopted to integrate the intersected outcomes from different local images. The result of the experiment indicates that MCWT outperforms other traditional segmentation methods in classifying brain MR images.