Differential MRI diagnosis between brain abscesses and necrotic or cystic brain tumors using the apparent diffusion coefficient and normalized diffusion-weighted images

Magnetic Resonance Diffusion-Weighted Imaging (DWI) has been reported to be helpful for the differential diagnosis between abscesses and cystic/necrotic brain tumors. However the number of patients is still limited, and the sensitivity and specificity of the method remain to be confirmed. The primary purpose of this study was to investigate a larger sample of patients, all investigated under the same experimental conditions, in order to obtain statistically significant data. Moreover, there is no consensus about the appropriate values of b required to use to make an accurate diagnosis from DWI. The secondary purpose of this study was to determine the discriminating threshold b values for raw diffusion-weighted images and for normalized diffusion-weighted images. On the basis of 14 abscesses, 10 high-grade gliomas and 2 metastases, we show that the calculation of accurate Apparent Diffusion Coefficient (ADC) values gives a specificity rate of 100%. Without ADC calculation, we show that image normalization is required to make an accurate differential diagnosis, and we highlight the ability of DWI to discriminate between brain abscesses and cystic/necrotic brain tumors using normalized signal intensity at lower b values (503 s/mm(2)) than usual.     

Abdominal apparent diffusion coefficient measurements: effect of diffusion-weighted image quality and usefulness of anisotropic images

This study aimed to assess the effect of diffusion-weighted image (DWI) quality on abdominal apparent diffusion coefficient (ADC) measurements and the usefulness of anisotropic images. Twenty-six patients (10 men and 16 women; mean, 58.1 years) who underwent DW imaging and were diagnosed not to have any abdominal diseases were analyzed. Single-shot spin-echo echo-planar DW imaging was performed, and one isotropic and three orthogonal anisotropic images were created. ADCs were calculated for liver (four segments), spleen, pancreas (head, body, tail) and renal parenchyma. Image quality for each organ part was scored visually. We estimated the correlation between ADC and image quality and evaluated the feasibility of using anisotropic images. ADCs and image quality were affected by motion probing gradient directions in the liver and pancreas. A significant inverse correlation was found between ADC and image quality. The r values for isotropic images were −.46, −.48, −.70 and −.28 for the liver, spleen, pancreas and renal parenchyma, respectively. Anisotropic images had the best quality and lowest ADC in at least one organ part in 17 patients. DWIs with the best quality among isotropic and anisotropic images should be used in the liver and pancreas.     

Biexponential apparent diffusion coefficient parametrization in adult vs newborn brain.

The decay of brain water signal with b-factor in adult and newborn brains has been measured over an extended b-factor range. Measurements of the apparent diffusion coefficient (ADC) decay curves were made at 16 b-factors from 100 to 5000 s/mm(2) along three orthogonal directions using a line scan diffusion imaging (LSDI) sequence to acquire data from 0.09 ml voxels in a mid-brain axial slice. Regions-of-interest (ROIs) in cortical gray (CG) and white matter in the internal capsule (IC) were selected for ADC decay curve analyses using a biexponential fitting model over this extended b-factor range. Measures of the fast and slow ADC component amplitudes and the traces of the fast and slow diffusion coefficients were obtained from CG and IC ROIs in both adults and newborns. The ADC decay curves from the newborn brain regions were found to have a significantly higher fraction of the fast diffusion ADC component than corresponding regions in the adult brain. The results demonstrate that post-natal brain development has a profound affect on the biexponential parameters which characterize the decay of water signal over an extended b-factor range in both gray and white matter.     

Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants

Objectives

The objective was to perform ex vivo evaluation of non-Gaussian diffusion kurtosis imaging (DKI) for assessment of hepatocellular carcinoma (HCC), including presence of treatment-related necrosis, using fresh liver explants.

Methods

Twelve liver explants underwent 1.5-T magnetic resonance imaging using a DKI sequence with maximal b-value of 2000 s/mm². A standard monoexponential fit was used to calculate apparent diffusion coefficient (ADC), and a non-Gaussian kurtosis fit was used to calculate K, a measure of excess kurtosis of diffusion, and D, a corrected diffusion coefficient accounting for this non-Gaussian behavior. The mean value of these parameters was measured for 16 HCCs based upon histologic findings. For each metric, HCC-to-liver contrast was calculated, and coefficient of variation (CV) was computed for voxels within the lesion as an indicator of heterogeneity. A single hepatopathologist determined HCC necrosis and cellularity.

Results

The 16 HCCs demonstrated intermediate-to-substantial excess diffusional kurtosis, and mean corrected diffusion coefficient D was 23% greater than mean ADC (P=.002). HCC-to-liver contrast and CV of HCC were greater for K than ADC or D, although these differences were significant only for CV of HCCs (P≤.046). ADC, D and K all showed significant differences between non-, partially and completely necrotic HCCs (P≤.004). Among seven nonnecrotic HCCs, cellularity showed a strong inverse correlation with ADC (r=−0.80), a weaker inverse correlation with D (− 0.24) and a direct correlation with K (r= 0.48).

Conclusions

We observed non-Gaussian diffusion behavior for HCCs ex vivo; this DKI model may have added value in HCC characterization in comparison with a standard monoexponential model of diffusion-weighted imaging.     

Evaluation of thermal diffusion factor and diffusion coefficient from measurements on a trennschaukel

Measurements have been taken on a glass trennschaukel of eight tubes for the Ar-He system as a function of composition and with its hot and cold bulbs at 100°c and 0°c. For each mixture, several speeds for the mechanical push-pull motion of the gas which cover a wide enough range were tried. The results have been interpreted to determine the thermal diffusion factor α _T, in conventional fashion as well as on the basis of a theory characterizing the low speed operation, developed here. Four such different possibilities of α _t evaluation are discussed and values compared with the traditional ones obtained from the convection-free two-bulb apparatus. Lastly we discuss also in brief the prospect of estimating the diffusion coefficient as an important byproduct from measurements on trennschaukel runs.     

Effect of impermeable interfaces on apparent diffusion coefficient in heterogeneous media

It is known that the short-time behavior of the diffusion coefficient, which is measurable by nuclear magnetic resonance (NMR), provides an estimate of the specific surface of porous samples filled with an NMR-detectable fluid. This method is not directly applicable to the exploration of structure of impermeable restrictions in mixtures such as living tissues, in which the inherent microscopic structure in the bulk medium results in the same pattern of the temporal dynamics of the diffusion coefficient as the impermeable restrictions studied. Here, an approach to describe diffusion and the boundary effect in heterogeneous media is developed in the framework of a cumulant expansion of the NMR signal. The leading term of this expansion is determined by the velocity autocorrelation function which is expressed in terms of properties of microscopic transport in the medium. Given these properties, the apparent diffusion coefficient as measured by NMR can be found by a straightforward integration. Calculations are performed in one spatial dimension.     

Association between visual degeneration of intervertebral discs and the apparent diffusion coefficient

The value of apparent diffusion coefficient (ADC) measurements in intervertebral disc has been studied because ADC provides an estimate of free diffusion of unbound water and could be used as a quantitative tool to estimate degenerative changes. However, the challenging nature of diffusion imaging of spine and limited numbers of subjects in earlier studies has produced contradictory findings. We aimed to determine the relation between ADC and visual degenerative changes in lumbar intervertebral discs in a sufficiently large homogeneous study group. Lumbar spines of 228 volunteer middle-aged men were MR imaged at 1.5 T including anatomic and diffusion-weighted imaging. ADC values, T2 signal intensity and height, and width of the three lowest lumbar intervertebral discs were measured and disc degeneration visually graded. The calculated average ADC of 530 measured discs was 2.01×10⁻³ mm²/s±0.29 (±S.D.). The reduction in ADC between visually normal and moderately degenerated discs was 4%. Severely degenerated discs showed 5% larger ADC values than normal discs, presumably due to free water in cracks and fissures of those discs. T2 signal intensity of the disc was significantly correlated with the ADC values, whereas other measured parameters did not show correlation. There was no evident difference in ADC between the studied anatomic lumbar levels. Because there is considerable overlap between ADC values of normal and degenerated discs, we conclude that ADC measurements of intervertebral discs, at least with current technology, have limited clinical value.     

The effect of impermeable boundaries of arbitrary geometry on the apparent diffusion coefficient

The apparent diffusion coefficient (ADC) obtained from NMR measurements is modelled for diffusion in a compartment restricted by an impermeable boundary. For a given pulse sequence, the ADC can be determined from the connected velocity autocorrelation function (the second-order velocity cumulant), which we show can be expressed as a double surface integral over the boundary, involving the probability for molecules to diffuse from one boundary point to another. There is no restriction on the geometry of the boundary. This result allows a fast calculation of the ADC for an arbitrary time course of the diffusion-sensitizing gradient. Explicit examples are given for diffusion within three basic geometries for different pulse sequences. The ADCs measured with the Stejskal-Tanner pulse sequence and a more realistic pulse sequence with slice selection gradient and eddy current compensation are found to yield almost identical results. The application of the results are discussed in relation to determination of the microscopic structure of brain white matter.     

Optimised diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain

This work studies the effect of diffusion-weighting on the precision of measurements of the apparent diffusion coefficient (ADC, or D) by diffusion-weighted magnetic resonance imaging. The precision in the value of the ADC was described in terms of a diffusion-to-noise ratio (DNR) which was calculated as the signal-to-noise ratio in the resultant ADC. A theoretical analysis decomposed the DNR into the signal-to-noise ratio in the diffusionweighted image and the sensitivity of diffusion-weighting, “κ_D”. The latter reflects the effect of the sampling strategy in the diffusion-weighting domain on the DNR. The theoretical analysis demonstrated that optimal two-point diffusion-weighting could be achieved in the vicinity of ξ = D(b₂−b₁) = 1.1, where ξ is a nondimensional parameter of diffusion-weighting, and b₁ and b₂ are the diffusion-weighting factors for the two-point diffusion-weighting. This approach also derived an optimised signal averaging scheme. The limitations and restrictions of the two-point scheme for in vivo ADC measurement were also considered; these included a detailed discussion on partial volume effects. The theory was verified by experiments on phantoms and on the brain of a healthy volunteer using a diffusion-weighted echo-planar imaging protocol. This led to an optimal two-point diffusion-weighting for ADC measurement in human brain using b₁ = 300, and b₂ = 1550 ± 100 s/mm². Such a two-point scheme successfully measured values of the ADC in gray matter, white matter and cerebrospinal fluid in human brain. It thus offers an alternative to the commonly used multiple-point schemes and has the advantage of requiring significantly shorter imaging times.     

Sulci density map to aid in use of apparent diffusion coefficient for therapy evaluation

In order to more accurately asses variations in the apparent diffusion coefficient used for therapy evaluation, we have studied the variation in sulci density in the human brain. Sagittal, axial and coronal magnetic resonance imaging scans have been analyzed to determine the change of the coefficient of variance of pixel intensity as a function of position. In the sagittal direction, relative to the 50% most medial slices, we find an 11.0%+/-4.8% (S.D.) decrease in the next 25% (12.5% on each side) of the slices. The most lateral 25% of the slices had less of a decrease and more variation: 7.0%+/-12.2%. Similar variations were observed in axial and coronal scans.     

Deriving new soft tissue contrasts from conventional MR images using deep learning

Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts from conventional MR images obtained in standard clinical MRI. Two types of experiments are performed. First, MR images corresponding to different pulse sequences are predicted from one or more images already acquired. As an example, we predict T_1ρ weighted knee image from T₂ weighted image and/or T₁ weighted image. Furthermore, we estimate images corresponding to alternative imaging parameter values. In a representative case, variable flip angle images are predicted from a single T₁ weighted image, whose accuracy is further validated in quantitative T₁ map subsequently derived. To accomplish these tasks, images are retrospectively collected from 56 subjects, and self-attention convolutional neural network models are trained using 1104 knee images from 46 subjects and tested using 240 images from 10 other subjects. High accuracy has been achieved in resultant qualitative images as well as quantitative T₁ maps. The proposed deep learning method can be broadly applied to obtain more versatile soft tissue contrasts without additional scans or used to normalize MR data that were inconsistently acquired for quantitative analysis.     

Detection of multiple sclerosis lesions using EPI-FLAIR images

Fast fluid-attenuated inversion recovery (fast-FLAIR) sequences are very sensitive for detecting lesions of patients with multiple sclerosis (MS). Echo planar imaging allows to obtain FLAIR images (EPI-FLAIR) with significantly shorter scanning times. EPI-FLAIR images obtained with 10 measurements are as sensitive as fast-FLAIR for the detection of large MS lesions. Aim of this study was to compare the numbers of MS lesions seen on EPI-FLAIR images with fewer measurements (and, as a consequence, very short scanning times) with those seen on EPI-FLAIR images with 10 measurements. EPI-FLAIR scans with 2 (EPI-2), 4 (EPI-4), 6 (EPI-6), 8 (EPI-8) and 10 (EPI-10) measurements were obtained from 29 MS patients. Lesions seen using each of the five approaches were counted by agreement by two observers. EPI-10 images were used as the "gold standard" for pairwise comparisons. EPI-FLAIR scans with fewer measurements (EPI-2, -4, -6, -8) were all significantly less sensitive than EPI-10 for the detection of small, intermediate and large MS lesions. All the EPI-FLAIR scans, however, fulfilled MR diagnostic criteria for definite MS. When rapid MR scanning of uncooperative MS patients is needed, EPI-FLAIR images covering the entire brain in less than one minute may be considered.     

The registration of MR images using multiscale robust methods

Acquisition of MR images involves their registration against some prechosen reference image. Motion artifacts and misregistration can seriously flaw their interpretation and analysis. This article provides a global registration method that is robust in the presence of noise and local distortions between pairs of images. It uses a two-stage approach, comprising an optional Fourier phase-matching method to carry out preregistration, followed by an iterative procedure. The iterative stage uses a prescribed set of registration points, defined on the reference image, at which a robust nonlinear regression is computed from the squared residuals at these points. The method can readily accommodate general linear, or even nonlinear, registration transformations on the images. The algorithm was tested by recovering the registration transformation parameters when a 256 × 256 pixel T₂¹-weighted human brain image was scaled, rotated, and translated by prescribed amounts, and to which different amounts of Gaussian noise had been added. The results show subpixel accuracy of recovery when no noise is present, and graceful degradation of accuracy as noise is added. When 40% noise is added to images undergoing small shifts, the recovery errors are less than 3 pixels. The same tests applied to the Woods algorithm gave slightly inferior accuracy for these images, but failed to converge to the correct parameters in some cases of large-scale-shifted images with 10% added noise.     

Joint reconstruction of multiecho MR images using correlated sparsity

This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in wavelet domain). Such techniques can be employed to reconstruct the individual T1- or T2-weighted images. However, in the current context, the different images are not really independent; they are images of the same cross section and, hence, are highly correlated. We exploit the correlation between the images, along with the spatial correlation within the images to achieve better reconstruction results than exploiting spatial correlation only.For individual MR images, CS-based techniques lead to a sparsity-promoting optimization problem in the wavelet domain. In this article, we show that the same framework can be extended to incorporate correlation between images leading to group/row sparsity-promoting optimization. Algorithms for solving such optimization problems have already been developed in the CS literature. We show that significant improvement in reconstruction accuracy can be achieved by considering the correlation between different T1- and T2-weighted images. If the reconstruction accuracy is considered to be constant, our proposed group sparse formulation can yield the same result with 33% less K-space samples compared with simple sparsity-promoting reconstruction. Moreover, the reconstruction time by our proposed method is about two to four times less than the previous method.     

Predicting the nodal status in gastric cancers: The role of apparent diffusion coefficient histogram characteristic analysis

PurposeTo explore the application of histogram analysis in preoperative T and N staging of gastric cancers, with a focus on characteristic parameters of apparent diffusion coefficient (ADC) maps.Materials and methodsEighty-seven patients with gastric cancers underwent diffusion weighted magnetic resonance imaging (b = 0, 1000 s/mm²), which generated ADC maps. Whole-volume histogram analysis was performed on ADC maps and 7 characteristic parameters were obtained. All those patients underwent surgery and postoperative pathologic T and N stages were determined.ResultsFour parameters, including skew, kurtosis, s-sD_av and sample number, showed significant differences among gastric cancers at different T and N stages. Most parameters correlated with T and N stages significantly and worked in differentiating gastric cancers at different T or N stages. Especially skew yielded a sensitivity of 0.758, a specificity of 0.810, and an area under the curve (AUC) of 0.802 for differentiating gastric cancers with and without lymph node metastasis (P < 0.001). All the parameters, except AUC_low, showed good or excellent inter-observer agreement with intra-class correlation coefficients ranging from 0.710 to 0.991.ConclusionCharacteristic parameters derived from whole-volume ADC histogram analysis could help assessing preoperative T and N stages of gastric cancers.     

Measurement of diffusion coefficient of transparent liquid solutions using Michelson interferometer

The study of diffusivity is important for the design of chemical equipment and its knowledge is needed for mass transfer studies and is the most fundamental property of any chemical system in which the chemical engineers are interested. Here, we report a new interferometric technique to determine the diffusion coefficient of transparent liquid solutions using Michelson interferometer geometry. The experimental cell containing the diffusing solution was kept in one of the arms of Michelson Interferometer. The liquids of differing concentrations were introduced in the cell. Two separate circular interference fringe systems were formed due to the two solutions and they were recorded on a PC using a CCD camera. The time variation of these interferograms yields the diffusion coefficient. The obtained diffusivity values matched very well with the existing experimental results. Detailed theoretical and experimental analysis is given in the paper.     

Calculation of diffusion coefficient of long chain molecules using molecular dynamics

Molecular dynamics (MD) is a powerful tool for calculating several thermo-physical properties of wide range of materials. In this study, the diffusivities (D) of two widely used long chain molecules MHA and ODT are calculated at various temperatures using MD simulations coupled with Einstein relationship. Four different kinds of forcefields COMPASS, UFF, CVFF and PCFF are employed in the MD simulation and the results are compared. Diffusivity values are evaluated in a humid environment in presence of water molecules.     

Study on the variations of the apparent diffusion coefficient in areas of solid tumor in high grade gliomas

Present knowledge suggests that in glioblastoma multiforme the value of the apparent diffusion coefficient (ADC) is elevated in the solid part and hyperintense in T1, in spite of the elevated cellularity, and also in areas where peritumoral vasogenic edema is present. The purpose of our study has been to verify in vivo if the ADC increases in areas of solid tumor because of an increased presence of edema, like it happens in areas surrounding the tumor. Sixteen patients with histologically verified glioblastoma multiforme underwent a magnetic resonance (MR) examination with sequences: T1-weighted pre and post contrast, diffusion-weighted at b = 0 and b = 1000 s/mm(2), perfusion-weighted. One hundred sixty-five regions of interest (ROI) have been obtained for all set of patients. In each ROI we have estimated 4 parameters: ADC, intensity of T2-signal normalised to the white matter (SI(T2W)(n)), regional cerebral blood volume (rCBV), T1-signal enhancement (E%). With the SI(T2W)(n) the presence of edema was estimated. For each pair of measured parameters a statistical test of linear regression on the set of all ROI was made. A directed linear correlation between: ADC and SI(T2W)(n) (p 相似文献   

Reliability of brain structure morphometry in hydrocephalic children using MR images

To assess the ability of human operators to make decisions about region boundaries in significantly malformed brains, we performed a study of the reliability of morphometric measurements of specific brain structures from MRI in children with hydrocephalus and controls. Cross-sectional area measures of the corpus callosum, internal capsules and centrum semiovale, and volumes of the lateral ventricles were made in 50 children. Independent measurements were made by two raters on T₁ and T₂-weighted MR images. Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC) between the two rater's sets of measures were computed for each structure across all subjects. ICCs ranged from a low of 0.7502 to a high of 0.9895. All ICCs were significant at the p < .0001 level and were generally less than or equal to the corresponding Pearson's r value in every case. Therefore, the Pearson's r may overestimate the reliability. The results of this study support the claim that the ICC should be used rather than the Pearson's r when assessing interater reliability in situations where large between-group differences are present. In addition, the results show that brains malformed by disorders, such as hydrocephalus, can be reliably assessed using morphometric measures of MR images.