The reliability of routine clinical post-processing software in assessing potential diffusion-weighted MRI “biomarkers” in brain metastases

Background and Purpose

Diffusion MRI characteristics have been used as biomarkers to guide prognosis in cerebral pathologies including brain metastases. The measurement of ADC is often described poorly in clinical and research studies with little detail given to the practical considerations of where to place ROIs, which post processing software package to use and how reproducible the resulting metrics will be.

Method

We investigated a series of 12 patients with brain metastases and preoperative DWI. Three post processing platforms were used. ROI were placed over the tumour, peritumoural region and across the brain-tumour interface. These recordings were made by a neurosurgeon and a neuroradiologist. Inter-intra-observer variability was assessed using Bland-Altman analysis. An exploratory analysis of DWI with overall survival and tumour type was made.

Results

There was excellent correlation between the software packages used for all measures including assessing the whole tumour, selective regions with lowest ADC, the change of ADC across the brain-tumour interface and the relation of the tumour ADC to peritumoural regions and the normal white matter. There was no significant inter- or intra-observer variability for repeated readings. There were significant differences in the mean values obtained using different methodologies and different metrics had differing relationships to overall survival and primary tumour of origin.

Conclusion

Diffusion weighted MRI metrics offer promise as potential non-invasive biomarkers in brain metastases and a variety of metrics have been shown to be reliably measured using differing platforms and observers.     

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.     

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

PurposeWe aimed to develop a radiomics model to predict the histopathological grading of meningiomas by magnetic resonance imaging (MRI) before surgery.MethodsWe recruited 131 patients with pathological diagnosis of meningiomas. All the patients had undergone MRI before surgery on a 3.0 T MRI scanner to obtain T1 fluid- attenuated inversion recovery (T1 FLAIR) images, T2-weighted images (T2WI) and T1 FLAIR with contrast enhancement (CE-T1 FLAIR) images covering the whole brain. The removing features with low variance, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Six classifiers were used to train the models (logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), random forests (RF), and XGBoost), and then 24 models were established using a random verification method to differentiate low-grade from high-grade meningiomas. The performance was assessed by receiver-operating characteristic (ROC) analysis, the f1-score, sensitivity, and specificity.ResultsThe radiomics features were significantly associated with the histopathological grading. Quantitative imaging features (n = 1409) were extracted, and nine features were selected to predict the grades of meningiomas. The best performance of the radiomics model for the degree of differentiation was obtained by SVM (area under the curve (AUC), 0.956; 95% confidence interval (CI), 0.83–1.00; sensitivity, 0.87; specificity, 0.92; f1-score, 0.90).ConclusionThe radiomics models are of great value in predicting the histopathological grades of meningiomas, and have broad prospects in radiology and clinics.     

Brain imaging: Comparison of T1W FLAIR BLADE with conventional T1W SE

IntroductionAlthough T1 weighted spin echo (T1W SE) images are widely used to study anatomical details and pathologic abnormalities of the brain, its role in delineation of lesions and reduction of artifacts has not been thoroughly investigated. BLADE is a fairly new technique that has been reported to reduce motion artifacts and improve image quality.ObjectiveThe primary objective of this study is to compare the quality of T1-weighted fluid attenuated inversion recovery (FLAIR) images with BLADE technique (T1W FLAIR BLADE) and the quality of T1W SE images in the MR imaging of the brain. The goal is to highlight the advantages of the two sequences as well as which one can better reduce flow and motion artifacts so that the imaging of the lesions will not be impaired.Materials and methodsBrain examinations with T1W FLAIR BLADE and T1W SE sequences were performed on 48 patients using a 1.5 T scanner. These techniques were evaluated by two radiologists based on: a) a qualitative analysis i.e. overall image quality, presence of artifacts, CSF nulling; and b) a quantitative analysis of signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) and Relative Contrast. The statistical analysis was performed using the Kruskal-Wallis non-parametric system.ResultsIn the qualitative analysis, BLADE sequences had a higher scoring than the conventional sequences in all the cases. The overall image quality was better on T1W FLAIR BLADE. Motion and flow-related artifacts were lower in T1W FLAIR BLADE. Regarding the SNR measurements, T1W SE appeared to have higher values in the majority of cases, whilst T1W-FLAIR BLADE had higher values in the CNR and Relative Contrast measurements.ConclusionT1W FLAIR BLADE sequence appears to be superior to T1W SE in overall image quality and reduction of motion and flow-pulsation artifacts as well as in nulling CSF and has been preferred by the clinicians. T1W FLAIR BLADE may be an alternative approach in brain MRI imaging.     

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.     

The imaging appearance of Creutzfeldt-Jakob disease caused by the E200K mutation

The E200K mutation on chromosome 20 can cause familial Creutzfeldt-Jakob disease (CJD). Patients with this mutation are clinically similar to those with sporadic CJD, but their imaging features are not well documented. We report here the quantitative and qualitative evaluation of the magnetic resonance (MR) imaging characteristics of this unique group of patients using three-dimensional spoiled gradient recalled (SPGR) echo images, diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) measurements, MR spectroscopy and a fluid-attenuated inversion recovery (FLAIR) sequence. The SPGR and ADC data were analyzed with SPM99. ANCOVA and regression models were used for a region-of-interest (ROI) analysis of ADC and metabolic ratios. CJD patients had a decreased fraction of gray matter and an increased fraction of cerebrospinal fluid (P=.001) in the cortex and cerebellum and increased ADC values in the cortex (P<.001). Focal decreases of ADC were found in the putamen via ROI analysis (548+/-83 vs. 709+/-9 microm(2)/s, P=.02). N-acetyl aspartate (NAA) was generally reduced, with the NAA/Cho ratio lowest in the cingulate gyrus. Qualitative assessment revealed hyperintensities on FLAIR, DWI or both in the putamen (three out of four patients), caudate (three out of four patients) and thalamus. These results provide a framework for future study of patients with genetically defined familial CJD.     

Evaluation of atlas-based segmentation of hippocampi in healthy humans

Introduction and aim

Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.

Material and methods

High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/).

Results

The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas.

Conclusion

Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.     

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   

Quantitative MRI: Defining repeatability,reproducibility and accuracy for prostate cancer imaging biomarker development

IntroductionQuantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response.Materials and methodsADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms.ResultsOverall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between −8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners.ConclusionRepeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.     

On the relationship between the apparent diffusion coefficient and extravascular extracellular volume fraction in human breast cancer

MRI techniques have been developed that can noninvasively probe the apparent diffusion coefficient (ADC) of water via diffusion-weighted MRI (DW-MRI). These methods have found much application in cancer where it is often found that the ADC within tumors is inversely correlated with tumor cell density, so that an increase in ADC in response to therapy can be interpreted as an imaging biomarker of positive treatment response. Dynamic contrast enhanced MRI (DCE-MRI) methods have also been developed and can noninvasively report on the extravascular extracellular volume fraction of tissues (denoted by v_e). By conventional reasoning, the ADC should therefore also be directly proportional to v_e. Here we report measurements of both ADC and v_e obtained from breast cancer patients at both 1.5 and 3.0 T. The 1.5-T data were acquired as part of normal standard of care, while the 3.0-T data were obtained from a dedicated research protocol. We found no statistically significant correlation between ADC and v_e for the 1.5- or 3.0-T patient sets on either a voxel-by-voxel or a region-of-interest (ROI) basis. These data, combined with similar results from other disease sites in the literature, may indicate that the conventional interpretation of either ADC, v_e or their relationship is not sufficient to explain experimental findings.     

Differentiation of high-grade and low-grade intra-axial brain tumors by time-dependent diffusion MRI

IntroductionOscillating gradient spin-echo (OGSE) sequences enable acquisitions with shorter diffusion times. There is growing interest in the effect of diffusion time on apparent diffusion coefficient (ADC) values in patients with cancer. However, little evidence exists regarding its usefulness for differentiating between high-grade and low-grade brain tumors. The purpose of this study is to investigate the utility of changes in the ADC value between short and long diffusion times in distinguishing low-grade and high-grade brain tumors.Material and methodsEleven patients with high-grade brain tumors and ten patients with low-grade brain tumors were scanned using a 3 T magnetic resonance imaging with diffusion-weighted imaging (DWI) using OGSE and PGSE (effective diffusion time [Δ_eff]: 6.5 ms and 35.2 ms) and b-values of 0 and 1000 s/mm². Using a region of interest (ROI) analysis of the brain tumors, we measured the ADC for two Δeff (ADC_Δeff) values and computed the subtraction ADC (ΔADC = ADC_{6.5 ms} − ADC_{35.2 ms}) and the relative ADC (ΔADC = (ADC_{6.5 ms} − ADC_{35.2 ms}) / ADC_{35.2 ms} × 100). The maximum values for the subtraction ADC (ΔADC_max) and the relative ADC (rADC_max) on the ROI were compared between low-grade and high-grade tumors using the Wilcoxon rank-sum test. A P-value <.05 was considered significant. The ROIs were also placed in the normal white matter of patients with high- and low-grade brain tumors, and ΔADC_max values were determined.ResultsHigh-grade tumors had significantly higher ΔADC_max and rADC_max than low-grade tumors. The ΔADC_max values of the normal white matter were lower than the ΔADC_max of high- and low-grade brain tumors.ConclusionThe dependence of ADC values on diffusion time between 6.5 ms and 35.2 ms was stronger in high-grade tumors than in low-grade tumors, suggesting differences in internal tissue structure. This finding highlights the importance of reporting diffusion times in ADC evaluations and might contribute to the grading of brain tumors using DWI.     

A comparison of the sensitivity of MRI after double- and triple-dose Gd-DTPA for detecting enhancing lesions in multiple sclerosis

We compared the number and volume of enhancing lesions detected in patients with multiple sclerosis (MS) seen on post-contrast T(1)-weighted scans obtained after the injection of different gadolinium-DTPA (Gd) doses. Enhanced magnetic resonance imaging (MRI) scans were obtained from 16 patients with relapsing remitting or secondary progressive MS on two different occasions separated by an interval of approximately 24 h. On the first occasion, enhanced scans were obtained 15 min after the injection of a double dose of Gd (0.2 mmol/Kg), on the second 15 min after the injection of a triple dose (0.3 mmol/Kg) of Gd. Scans were assessed by consensus in a random order by two observers unaware of the dose of Gd used. We counted the same 30 enhancing lesions on both double dose and triple dose scans from 9 patients. The mean (SD) volumes of enhancing lesions were 1.7 (2.7) mL on double dose and 1.9 (3.4) mL on triple-dose scans. This difference was not statistically significant. This study demonstrated that double dose of Gd has a sensitivity for detecting MS activity similar to that of a triple dose, with the advantage of a significant cost saving.     

Reproducibility of myelin water fraction analysis: a comparison of region of interest and voxel-based analysis methods

This study compared region of interest (ROI) and voxel-based analysis (VBA) methods to determine the optimal method of myelin water fraction (MWF) analysis. Twenty healthy controls were scanned twice using a multi-echo T₂ relaxation sequence and ROIs were drawn in white and grey matter. MWF was defined as the fractional signal from 15 to 40 ms in the T₂ distribution. For ROI analysis, the mean intensity of voxels within an ROI was fit using non-negative least squares. For VBA, MWF was obtained for each voxel and the mean and median values within an ROI were calculated. There was a slightly higher correlation between Scan 1 and 2 for the VBA method (R²=0.98) relative to the ROI method (R²=0.95), and the VBA mean square difference between scans was 300% lower, indicating VBA was the most consistent between scans. For the VBA method, mean MWF was found to be more reproducible than median MWF. As the VBA method is more reproducible and gives more options for visualization and analysis of MWF, it is recommended over the ROI method of MWF analysis.     

Motion corrected DWI with integrated T2-mapping for simultaneous estimation of ADC,T2-relaxation and perfusion in prostate cancer

ObjectiveMultiparametric magnetic resonance imaging (MRI) and PI-RADS (Prostate Imaging – Reporting and Data System) has become the standard to determine a probability score for a lesion being a clinically significant prostate cancer. T2-weighted and diffusion-weighted imaging (DWI) are essential in PI-RADS, depending partly on visual assessment of signal intensity, while dynamic-contrast enhanced imaging is less important. To decrease inter-rater variability and further standardize image evaluation, complementary objective measures are in need.MethodsWe here demonstrate a sequence enabling simultaneous quantification of apparent diffusion coefficient (ADC) and T2-relaxation, as well as calculation of the perfusion fraction f from low b-value intravoxel incoherent motion data. Expandable wait pulses were added to a FOCUS DW SE-EPI sequence, allowing the effective echo time to change at run time. To calculate both ADC and f, b-values 200 s/mm² and 600 s/mm² were chosen, and for T2-estimation 6 echo times between 64.9 ms and 114.9 ms were used.ResultsThree patients with prostate cancer were examined and all had significantly decreased ADC and T2-values, while f was significantly increased in 2 of 3 tumors. T2 maps obtained in phantom measurements and in a healthy volunteer were compared to T2 maps from a SE sequence with consecutive scans, showing good agreement. In addition, a motion correction procedure was implemented to reduce the effects of prostate motion, which improved T2-estimation.ConclusionsThis sequence could potentially enable more objective tumor grading, and decrease the inter-rater variability in the PI-RADS classification.     

Effect of noise and estimator type on bias for analysis of liver proton density fat fraction

PurposeProton-density fat-fraction (PDFF) is typically measured from PDFF maps by calculating the mean PDFF value within a region of interest (ROI). However, the mean estimator has been shown to result in bias when signal-to-noise ratio (SNR) is low, resulting from a skewed distribution of PDFF noise statistics. Thus, the purpose of this work was to determine the relative performance of three estimation methods (mean, median, maximum likelihood estimators (MLE)) for analysis of liver PDFF maps.MethodsObservational study of adult patients (n = 56) undergoing abdominal MRI. Both 2D-sequential CSE-MRI (‘low-SNR’) and 3D CSE-MRI (‘high-SNR’) acquisitions were obtained. Single-voxel MRS formed the independent reference measurement of hepatic PDFF. Intra-class correlation was tested on a subset of ‘low-SNR’ acquisitions. ROIs were semi-automatically co-registered across all acquisitions. Bland-Altman analysis and intra-class correlation coefficients were used for statistical analysis. A p-value of <0.05 was considered significant.ResultsFor in vivo low-SNR acquisitions, the mean estimator had a larger error than either the median or MLE values (bias ~ −1% absolute PDFF). The intra-class correlation coefficient was significantly greater for median and maximum likelihood estimators (0.992 and 0.993, respectively) compared to the mean estimator (0.973).ConclusionAlternative ROI analysis strategies, such as MLE or median estimators, are useful to avoid SNR-related PDFF bias. Median may be the most clinically practical strategy given its ease of calculation.     

DWI and PRECISE criteria in men on active surveillance for prostate cancer: A multicentre preliminary experience of different ADC calculations

PurposeThe PRECISE score estimates the likelihood of radiological progression in patients on active surveillance (AS) for prostate cancer (PCa) with serial multiparametric magnetic resonance imaging (mpMRI). A PRECISE score of 1 or 2 denotes radiological regression, PRECISE 3 indicates stability and PRECISE 4 or 5 implies progression.We evaluated the inter-reader reproducibility of different apparent diffusion coefficient (ADC) calculations and their relationship to the PRECISE score.Material and methodsBaseline and follow-up scans (on the same MR systems) of 30 patients with visible lesions from two different institutions (University College London and Sapienza University of Rome) were analysed by two radiologists (one from each site). The PRECISE score was initially assessed in consensus. At least six weeks later, to reduce the likelihood of being influenced by the consensus PRECISE reading, each radiologist independently calculated ADC for the following: lesion, non-cancerous tissue and urine in the bladder. Normalised ADC ratios were calculated with respect to normal prostatic tissue (npADC) and urine. Spearman's correlation (ρ), intraclass correlation coefficients (ICC), differences in ADC and ROC curves were computed.ResultsInterobserver reproducibility was very good (ρ > 0.8; ICC > 0.90). Lesion ADC (0.91 vs 0.73 × 10⁻³ mm²/s; p=0.025) and npADC ratio (0.68 vs 0.53; p=0.012) at follow-up mpMRI were different between patients with radiological regression or stability vs progression. Cut-offs of 0.77 × 10⁻³ mm²/s (lesion ADC) and 0.59 (npADC ratio) could differentiate the two groups (area under the curve: 0.74 and 0.77, respectively).ConclusionThe ADC, npADC ratio and the PRECISE score should be recorded for MRI-based AS.     

Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification.     

Utility of a diffusion-weighted arterial spin labeling (DW-ASL) technique for evaluating the progression of brain white matter lesions

PurposeTo investigate the utility of diffusion-weighted arterial spin labeling (DW-ASL) for detecting the progression of brain white matter lesions.Materials and methodsA total of 492 regions of interest (ROIs) in 41 patients were prospectively analyzed. DW-ASL was performed using the diffusion gradient prepulse of five b-values (0, 25, 60, 102, and 189) before the ASL readout. We calculated the water exchange rate (K_w) with post-processing using the ASL signal information for each b-value. The cerebral blood flow (CBF) was also calculated using b0 images. Using the signal information in FLAIR (fluid-attenuated inversion recovery) images, we classified the severity of white matter lesions into three grades: non-lesion, moderate, and severe. In addition, the normal K_w level was measured from DW-ASL data of 60 ROIs in five control subjects. The degree of variance of the K_w values (K_w-var) was calculated by squaring the value of the difference between each K_w value and the normal K_w level. All patient's ROIs were divided into non-progressive and progressive white matter lesions by comparing the present FLAIR images with those obtained 2 years before this acquisition.ResultsCompared to the non-progressive group, the progressive group had significantly lower CBF, significantly higher severity grades in FLAIR, and significantly greater K_w-var values. In a receiver operator characteristic curve analysis, a high area under the curve (AUC) of 0.89 was obtained with the use of K_w-var. In contrast, the AUCs of 0.59 for CBF and 0.72 for severity grades in FLAIR were obtained.ConclusionsThe DW-ASL technique can be useful to detect the progression of brain white matter lesions. This technique will become a clinical tool for patients with various degrees of white matter lesions.     

Investigation of sandwiched gadolinium (III) complexes with tungstosilicates as potential MRI contrast agents

Two gadolinium-sandwiched complexes with tungstosilicates, K(13)[Gd(SiW(11)O(39))(2)] (Gd(SiW(11))(2)) and K(11)H(6)[Gd(3)O(3)(SiW(9)O(34))(2)] (Gd(3)(SiW(9))(2)), have been investigated by in vitro and in vivo experiments as potential contrast agents for magnetic resonance imaging (MRI). T(1)-relaxivity of Gd(SiW(11))(2)was 6.59 mM(-1).s(-1) in aqueous solution and 6.85 mM(-1).s(-1) in 0.725 mmol.L(-1) bovine serum albumin solution at 25 degrees C and 9.39 T, respectively. The corresponding T(1)-relaxivity of Gd(3)(SiW(9))(2) was 12.6 and 19.3 mM(-1).s(-1) per Gd, respectively. MRI for Sprague-Dawley rats showed longer and more remarkable enhancement in rat liver after i.v. injection of these two complexes: 39.4 +/- 3.9% and 57.4 +/- 11.6% within the first 30 min after injection, 31.2 +/- 2.6% and 39.9 +/- 7.6% in the next 60 min for Gd(SiW(11))(2) and Gd(3)(SiW(9))(2) at doses of 0.081 and 0.084 mmol Gd/kg, respectively. Our preliminary in vitro and in vivo study indicates that Gd(SiW(11))(2) and Gd(3)(SiW(9))(2) are favorable candidates for hepatic contrast agents for MRI. However, the two complexes exhibit higher acute toxicity and need to be modified and studied further before clinical use.