Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification

Estimating white matter hyperintensities volume in individuals with stroke using T1-weighted images

White matter hyperintensities (WMHs) are regions of abnormally high intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). Accurate and reproducible automatic segmentation of WMHs is important since WMHs are often seen in the elderly and are associated with various geriatric and psychiatric disorders. We developed a fully automated monospectral segmentation method for WMHs using FLAIR MRIs. Through this method, we introduce an optimal threshold intensity (I O ) for segmenting WMHs, which varies with WMHs volume (V WMH), and we establish the I O -V WMH relationship. Our method showed accurate validations in volumetric and spatial agreements of automatically segmented WMHs compared with manually segmented WMHs for 32 confirmatory images. Bland-Altman values of volumetric agreement were 0.96 ± 8.311ml (bias and 95% confidence interval), and the similarity index of spatial agreement was 0.762 ± 0.127 (mean ± standard deviation). Furthermore, similar validation accuracies were obtained in the images acquired from different scanners. The proposed segmentation method uses only FLAIR MRIs, has the potential to be accurate with images obtained from different scanners, and can be implemented with a fully automated procedure. In our study, validation results were obtained with FLAIR MRIs from only two scanner types. The design of the method may allow its use in large multicenter studies with correct efficiency.

PurposeQuantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need.MethodsWe obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change.ResultsThe correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p < 0.0001) was slightly stronger than between the latter and WMH volumes (Spearman ρ > =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change.ConclusionThe new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage.

Associations between accelerometer-derived sedentary behavior and physical activity with white matter hyperintensities in middle-aged to older adults.

This work aimed to determine the potential link between white matter hyperintensity (WMH) burden and serum amyloid A (SAA) level in patients with acute ischemic stroke. Consecutive patients with acute large artery atherosclerosis (LAA) stroke between April 2021 and May 2022 were included. WMH volumes (periventricular, deep, and total) were measured using the Fazekas score and a semiautomated volumetric analysis on fluid-attenuated inversion recovery-magnetic resonance imaging. The burdens of WMH were scored to assess the dose-dependent association between SAA and WMH volume. Multivariate regression and a two-piecewise linear regression model were used to evaluate whether SAA levels are an independent predictor of WMH, and to discover the threshold effect or saturation effect of SAA levels with respect to WMH volume. The mean age of patients was 63.2 ± 11.5years, with 65.9% men. The median SAA level was 3.93mg/L and the total WMH volume of 6.86cm3. In the multivariable analysis, SAA remained an independent predictor of total WMH volume [β = 0.82, 95% confidence interval (CI) = 0.49-1.07, p < 0.001], periventricular WMH volume (adjusted β = 0.76, 95% CI = 0.46-1.07, p < 0.001), and deep WMH volume (adjusted β = 0.26, 95% CI = 0.06-0.45, p = 0.011) after controlling for confounders. Furthermore, SAA levels were associated with periventricular Fazekas score, deep Fazekas score, and Fazekas grades. Threshold effect and saturation effect analyses demonstrated a nonlinear relationship between SAA levels and periventricular white matter hyperintensity (PVWMH) volumes, with SAA levels (2.12-19.89mg/L) having significant dose-dependent relationships with periventricular WMH volumes (adjusted β = 1.98, 95% CI = 1.12-2.84, p < 0.001). SAA level ranging from 2.12 to 19.89mg/L is dose-dependently associated with periventricular WMH development. These findings point the way forward for future research into the pathophysiology of WMH.

Quantitative approaches for assessment of white matter hyperintensities in elderly populations

Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular and neurodegenerative diseases among the elderly. Consequently, detection and characterization of WMHs are of significant clinical importance. We propose a novel approach for WMH segmentation from multi-contrast MRI where both voxel-based and lesion-based information are used to improve overall performance in both volume-oriented and object-oriented metrics. Our segmentation method (AMOS-2D) consists of four stages following a "generate-and-test" approach: pre-processing, Gaussian white matter (WM) modelling, hierarchical multi-threshold WMH segmentation and object-based WMH filtering using support vector machines. Data from 28 subjects was used in this study covering a wide range of lesion loads. Volumetric T1-weighted images and 2D fluid attenuated inversion recovery (FLAIR) images were used as basis for the WM model and lesion masks defined manually in each subject by experts were used for training and evaluating the proposed method. The method obtained an average agreement (in terms of the Dice similarity coefficient, DSC) with experts equivalent to inter-expert agreement both in terms of WMH number (DSC=0.637 vs. 0.651) and volume (DSC=0.743 vs. 0.781). It allowed higher accuracy in detecting WMH compared to alternative methods tested and was further found to be insensitive to WMH lesion burden. Good agreement with expert annotations combined with stable performance largely independent of lesion burden suggests that AMOS-2D will be a valuable tool for fully automated WMH segmentation in patients with cerebrovascular and neurodegenerative pathologies.

Enhanced Long-Term Brain Magnetic Resonance Imaging Evaluation of Children with Sickle Cell Disease after Hematopoietic Cell Transplantation

To retrospectively investigate the association between changes in total cerebral blood flow and progression of total, periventricular, and deep white matter hyperintensities over time. The institutional ethics review board approved the protocol for the prospective magnetic resonance (MR) imaging study, and all participants gave written informed consent. Participants also agreed to future retrospective analysis of their MR data for research purposes. In this substudy of the Prospective Study of Pravastatin in the Elderly at Risk, investigators performed a repeated MR imaging examination after an average interval of 33 months (standard deviation, 1.4) in 390 elderly men and women (ages 70-82 years at baseline) without dementia who were at high vascular risk. White matter hyperintensities were quantified with a semiautomatic method, and total cerebral blood flow was measured with a gradient-echo phase-contrast MR imaging technique. The association between total cerebral blood flow and volume of white matter hyperintensities was analyzed with logistic regression. There was no association between baseline cerebral blood flow and prevalence of total, periventricular, or deep white matter hyperintensities at baseline MR imaging. Moreover, decline in cerebral blood flow was not associated with increase in total load of white matter hyperintensities. When the total volume of white matter hyperintensities was separated into periventricular and deep hyperintensities, for every 50 mL/min decrease in total cerebral blood flow there was a 1.32 (95% confidence interval: 1.06, 1.66; P = .015) increase in risk for developing periventricular white matter hyperintensities; there was no association, however, between decrease in total cerebral blood flow and risk of developing deep white matter hyperintensities (odds ratio, 1.00 [95% confidence interval: 0.79, 1.25]; P = .98). Decline in total cerebral blood flow is associated with increase in volume of periventricular but not deep white matter hyperintensities.

Objectives:The objective of our study is to determine whether the addition of3-dimensional (3D) MRI to standard MRI sequences is comparable to 3D CT scanevaluation of glenoid and humeral bone loss in glenohumeral instability.There is increasing understanding of the importance of the glenoid, as wellas humeral bone loss in outcomes and surgical decision making in patientswith shoulder instability. Although magnetic resonance imaging (MRI) isperformed as standard of care for soft tissue evaluation of shoulderinstability, CT scan remains the gold standard in bony measurements.Standard MRI evaluation has been shown to inaccurately measure bone loss.However, additional CT scans result in increased financial, time, andionizing radiation cost to the patient.Methods:Eighteen patients who presented with glenohumeral instability wereprospectively enrolled and received both MRI and CT within 1 week of eachother. The MRI included an additional sequence (VIBE) which underwentpost-processing for reformations (Figure 1a). The addition of a VIBEprotocol, on average is an addition 4 to 4.5minutes in the scanner. CT dataalso underwent 3D post-processing (Figure 1b) and therefore each patient hadfour imaging modalities (2D CT, 2D MRI, 3D CT reformats, and 3D MRIreformats). Each sequence underwent the following measurements from twoseparate reviewers: glenoid defect, glenoid defect percent, humeral defect,humeral defect percentage (Figure 2a and 2b), and evaluation of glenoidtrack and version. Paired t-tests were used to assess differences betweenimaging modalities and chi squared for glenoid track. Intra-observer andinter-observer reliability were evaluated. Bland-Altman tests were alsoperformed to assess the agreement between CT and MRI. In addition, wedetermined cost of each imaging modality at our single institution.Results:3D MRI measurements for glenoid and humeral bone loss measurements werecomparable to 3D CT (Table 1). There were no significant differences forglenoid defect size and percentage, or humeral defect size and percentage(P>0.05) (Table 2). Bland-Altman analysis demonstrated strong agreementwith small measurement errors for 3D CT and 3D MRI percentage glenoid boneloss. There was also no difference in evaluation for determining on vs offtrack between any of the imaging modalities. Inter- and intra-raterreliability was good to excellent for all CT and MRI measurements (r >=0.7).Conclusions:3D MRI measurements for bone loss in glenohumeral instability throughutilization of VIBE sequence were equivalent to 3D CT. At our institution,the costs of MRI with 3D reconstruction was 1.67x cheaper than MRI and CTwith 3D reconstructions. 3D MRI may be a useful adjuvant to standard MRIsequences to allow concurrent soft tissue and accurate assessment of glenoidand humeral bone loss in glenohumeral instability.Figure 1a.(3D MRI glenoid reformation) and lb (3D CT glenoid reformation):Glenoid measurement for 3D reconstruction of MRI and CT usingsagittal en face of the glenoid. A circle was placed over theinferior portion of the glenoid using margins as a guide for thecircle. The glenoid height line (blue) runs from the supraglenoidtubercle to the medial aspect of the inferior base. Glenoid boneloss size (orange) and circle diameter (green) was quantified withglenoid height line marking the center of the circle. Percentglenoid bone loss was determined as [(glenoid bone loss size/circlediameter)* 100]. Glenoid track was determined through equation[(0.83 * circle diameter) - glenoid bone loss size]Figure 2a.(2D CT) and 2b (3D CT): 2a: Measurement of Hill Sachs maximal defecton axial 2D CT recorded in mm. 2b: Measurement of humeral defectpercentage from the 3D reformats using sagittal projection with bestfit circle. A line (blue) was placed to determine maximal humeralhead height (A) then perpendicular line (green) to measure residualhumeral head (B) and therefore bone defect (A-B)(orange). Percentbone loss was determined through [(A-B)/A)*100]Table 1:Mean 2D and 3D CT and MRI measurementsTable 2:Paired t-test Glenoid and Humeral Bone Loss Measurements.

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered ”normal”. Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions’ segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

BackgroundWMH in MRI are common among the elderly. WMH are associated with increased cognitive impairment and risk of Alzheimer`s disease (AD). Although WMH contribute to AD pathology, their clinical implications are not fully understood. This study aimed to assess whether WMH predict AD and investigate the association between WMH (including subclassified WMH volume) and cognitive function in AD.MethodThis study enrolled 171 patients with AD. All participants underwent clinical evaluations including brain MRI study and neuropsychological tests using the CERAD‐K neuropsychological assessment battery. In addition, Charlson comorbidity index, the Korean version of the Geriatric Depression Scale short from were administered. WMH volume was calculated using automated quantification method with SPM and MATLAB image processing software. WMH were classified according to the distance from the ventricular surface. WMH located in juxtaventricular areas, within 3 mm from the ventricular surface, were classified as juxtaventricular with matter hyperintensities (JVWMH). WMH located within 3–13 mm or farther than 13 mm from the ventricular surface were classified as periventricular white matter hyperintensities (PVWMH) or deep white matter hyperintensities (DWMH), respectively. WMH volume data were right‐skewed. Consequently, WMH volume data were logarithmic transformed.ResultThe AD group had a higher mean WMH volume(20.7 ± 18.2 ml). Multivariate linear regression analysis showed that total WMH volume in AD was associated with poor performance in categorical verbal fluency test (p = 0.008) and word list memory test (p = 0.023). JVWMH volume in AD was associated with poor performance on categorical verbal fluency test (p = 0.013) and digit span test forward (p = 0.037). PVWMH volume in AD was associated with poor performance on categorical verbal fluency test (p = 0.011) and word list memory test (p = 0.021), whereas DWMH volume showed no association with cognitive dysfunction in AD.ConclusionTotal WMH, JVWMH and PVWMH were associated specifically with executive function, immediate memory, and working memory, independently of hippocampal atrophy in AD. WMH were associated with AD risk and cognitive dysfunction differentially according to the distance from the ventricular surface.

Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning-based SPACE.

BackgroundPatients with Alzheimer’s disease (AD) often exhibit neuropsychiatric symptoms (NPS), particularly delusions. Previous studies have shown an association between white matter hyperintensities (WMH) and specific NPS. This study aims to explore the relationship between WMH volume and delusions in AD patients by comparing the WMH volumes of delusional and non-delusional subgroups.Methods80 AD patients were divided into a delusion group (n = 36) and a non-delusion group (n = 44) based on the Neuropsychiatric Inventory (NPI). The brain cortical volume and WMH volume were quantitatively calculated for all 80 patients, including total WMH volume, periventricular WMH (PVWMH) volume, deep WMH volume, as well as bilateral frontal lobe, temporal lobe, parietal lobe, and occipital lobe WMH volumes. Firstly, we compared the differences in WMH volumes between the delusion group and non-delusion group. Then, within the delusion group, we further categorized patients based on severity scores of their delusional symptoms into mild (1 point), moderate (2 points), or severe groups (3 points). We compared the WMH volumes among these three groups to investigate the role of WMH volume in delusional symptoms.ResultsThere was a significant difference in left occipital lobe WMH volume between the delusion group and non-delusion group(P < 0.05). Within the delusion group itself, there were significant differences in overall WMH volume as well as PVWMH volume among patients with mild or severe levels of delusions(P < 0.05).ConclusionLeft occipital lobe WMH volume may be associated with the occurrence of delusional AD patients, and the total volume of whole-brain WMH and PVWMH volume may affect the degree of severity of delusional symptoms.

To the Editor: The recent article by DeCarli et al1 addresses a somewhat neglected aspect of white matter hyperintensities (WMH), the significance of their anatomical location. The authors argue that the commonly accepted categorization into deep (DWMH) and periventricular (PVWMH) WMH is arbitrary, because the 2 are very highly correlated, and a spatial analysis does not reveal distinct populations. We think that this conclusion is premature, because the categorization depends on a number of factors. The first limitation of their analysis is that they examined individuals in their 70s who presented to a specialty clinic, suggesting that the white matter lesions in their sample were at an advanced stage. If an analogy is drawn from cerebral atrophy in dementia, regional differences in atrophy that are present in the different subtypes of dementia become less prominent in the later stages. In our study of WMH in middle age (60 to 64 years), the correlation of DWMH and PVWMH was much lower ( r =0.621; …