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Background: Amide proton transfer (APT) imaging is a chemical exchange saturation transfer (CEST) technique offering potential clinical applications such as diagnosis, characterization, and treatment planning and monitoring in glioma patients. While APT-CEST has demonstrated high potential, reproducibility remains underexplored. Purpose: To investigate whether cerebral APT-CEST with clinically feasible scan time is reproducible in healthy tissue and glioma for clinical use at 3 T. Study Type: Prospective, longitudinal. Subjects: Twenty-one healthy volunteers (11 females; mean age ± SD: 39 ± 11 years) and 6 glioma patients (3 females; 50 ± 17 years: 4 glioblastomas, 1 oligodendroglioma, 1 radiologically suspected low-grade glioma). Field Strength/Sequence: 3 T, Turbo Spin Echo - ampling perfection with application optimized contrasts using different flip angle evolution - chemical exchange saturation transfer (TSE SPACE-CEST). Assessment: APT-CEST measurement reproducibility was assessed within-session (glioma patients, scan session 1; healthy volunteers scan sessions 1, 2, and 3), between-sessions (healthy volunteers scan sessions 1 and 2), and between-days (healthy volunteers, scan sessions 1 and 3). The mean APT CEST values and standard deviation of the within-subject difference (SD diff) were calculated in whole tumor enclosed by regions of interest (ROIs) in patients, and eight ROIs in healthy volunteers—whole-brain, cortical gray matter, putamen, thalami, orbitofrontal gyri, occipital lobes, central brain—and compared. Statistical Tests: Brown-Forsythe tests and variance component analysis (VCA) were used to assess the reproducibility of ROIs for the three time intervals. Significance was set at P < 0.003 after Bonferroni correction. Results: Intratumoral mean APT CEST was significantly higher than APT CEST in healthy-appearing tissue in patients (0.5 ± 0.46%). The average within-session, between-sessions, and between-days SD diff of healthy control brains was 0.2% and did not differ significantly with each other (0.76 > P > 0.22). The within-session SD diff of whole-brain was 0.2% in both healthy volunteers and patients, and 0.21% in the segmented tumor. VCA showed that within-session factors were the most important (60%) for scanning variance. Data Conclusion: Cerebral APT-CEST imaging may show good scan–rescan reproducibility in healthy tissue and tumors with clinically feasible scan times at 3 T. Short-term measurement effects may be the dominant components for reproducibility. Level of Evidence: 2. Technical Efficacy: Stage 2.

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer’s Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences.Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features — i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia.The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features — Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) — were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.

Background and Purpose: To encourage clinical adoption of ASL-based perfusion MRI, we investigated the reproducibility of CBF measurements and the effects of protocol variations at clinical field strengths in adult participants with a broad age range. This study increases the knowledge on the tolerance to variations in scan parameters compared to the recommended ASL implementation. Materials and Methods: Thirty-four volunteers (mean age 57.8±17.0y, range 22-80y) underwent two separate scan sessions on clinical field strengths (1.5T and 3T, single vendor), using a fifteen channel head coil. Both sessions contained repeated 3D and 2D pseudo-continuous ASL (pCASL) vendor-recommended protocols, followed by three 3D pCASL scans; two with post-labeling delays (PLD) of 1600ms and 2000ms and one with increased spatial resolution. All pCASL scans were acquired with a single PLD. The effect of scan parameter variations on CBF and spatial coefficient of variation (CoV) was examined, as well as the reproducibility of the recommended protocols, both intrasession (two identical protocols scanned 5 min apart) and intersession (first 2D and 3D protocol of the first and second session). Results: Intrasession CBF reproducibility was similar across image readouts and field strengths ( coefficient of variation (CV) ranging from 4.0% to 6.9%) and did not show a statistically significant correlation with age. Intersession wsCV ranged from 6.8% to 14.8%, scanning twice at 3T versus mixed 3T-1.5T respectively. At 3T, there is sufficient SNR to increase the spatial resolution of the 3D protocol, causing less mixing of gray matter (GM) and white matter signal, therewith decreasing the bias in GM CBF between 2D and 3D protocols (ΔCBF = 2.49 (p

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Background: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.Methods: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (epsilon) and spatial overlapping coefficient (DICE) between test and retest session.Results: Significant MRI site and vendor effects (p 0.43, p 0.80).Conclusion: Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.

Background Each ultrafast dynamic contrast-enhanced (DCE) MRI sequence for breast cancer generates thousands of images in a 4D stack that need to be reviewed by a radiologist. Purpose To assess whether color intensity projections (CIP) effectively summarizes-using only the time of arrival (ToA) and amount of signal enhancement (AoE) of the contrast agent-the thousands of ultrafast images. Study type Retrospective cohort clinical trial. Subjects The study included 89 patients who had been scanned with an MRI beast protocol, of which 26 had breast cancer and 63 did not. Field strength/sequence The 115-second ultrafast DCE sequence at 3T acquired 19 consecutive frames every 4.26 seconds with 152 slices per frame, yielding a 4D stack with 2888 2D images for each of water and fat. Assessment For each slice of the water 4D stack a single CIP image was generated that encoded the ToA in the hue (red, orange, yellow, green, cyan, blue) and AoE in the brightness. Each of three experienced radiologists assigned a Breast Imaging and Reporting Data System (BI-RADS) score for each patient, first using only the CIP images, and subsequently using both CIP and the full 4D stack. Statistical tests The one-sided Fisher's exact test was used to determine statistical significance of both the sensitivity and specificity between the CIP alone and the CIP plus 4D stack. Results All malignancies were detected using only CIP by at least one of the radiologists. The CIP and CIP+4D sensitivities for reader 1 were 96% and 96% (P = 0.57), specificities were 59% and 65% (P = 0.29). For reader 2, the values were 96% and 100% (P = 0.51) with 62% and 71% (P = 0.17). For reader 3 the values were 92% and 96% (P = 0.50) with 51% and 62% (P = 0.07). Data conclusion With a 95% sensitivity, CIP provides an effective summary of ultrafast DCE images of breast cancer. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1391-1399.

Cerebral microbleeds (CMBs), also referred to as microhemorrhages, appear on magnetic resonance (MR) images as hypointense foci notably at T2*-weighted or susceptibility-weighted (SW) imaging. CMBs are detected with increasing frequency because of the more widespread use of high magnetic field strength and of newer dedicated MR imaging techniques such as three-dimensional gradient-echo T2*-weighted and SW imaging. The imaging appearance of CMBs is mainly because of changes in local magnetic susceptibility and reflects the pathologic iron accumulation, most often in perivascular macrophages, because of vasculopathy. CMBs are depicted with a true-positive rate of 48%-89% at 1.5 T or 3.0 T and T2*-weighted or SW imaging across a wide range of diseases. False-positive "mimics" of CMBs occur at a rate of 11%-24% and include microdissections, microaneurysms, and microcalcifications; the latter can be differentiated by using phase images. Compared with postmortem histopathologic analysis, at least half of CMBs are missed with premortem clinical MR imaging. In general, CMB detection rate increases with field strength, with the use of three-dimensional sequences, and with postprocessing methods that use local perturbations of the MR phase to enhance T2* contrast. Because of the more widespread availability of high-field-strength MR imaging systems and growing use of SW imaging, CMBs are increasingly recognized in normal aging, and are even more common in various disorders such as Alzheimer dementia, cerebral amyloid angiopathy, stroke, and trauma. Rare causes include endocarditis, cerebral autosomal dominant arteriopathy with subcortical infarcts, leukoencephalopathy, and radiation therapy. The presence of CMBs in patients with stroke is increasingly recognized as a marker of worse outcome. Finally, guidelines for adjustment of anticoagulant therapy in patients with CMBs are under development. © RSNA, 2018.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Introduction In this cross-sectional study, we aimed to detect differences in cerebral blood flow (CBF) between subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and subjective cognitive decline (SCD), using two-dimensional phase-contrast magnetic resonance imaging. Methods We included 74 AD patients (67 years, 51% female), 36 MCI patients (66 years, 33% female), and 62 patients with SCD (60 years, 32% female) from the Amsterdam Dementia Cohort. Patients with SCD are those who visited the memory clinic with subjective cognitive complaints without objective cognitive impairment. Whole-brain CBF (mL/100 g/min) was calculated using total volume flow measured with two-dimensional phase-contrast magnetic resonance imaging and normalized for brain volume. Results Mean CBF values (SD) were lower in AD compared to SCD (age and sex adjusted 70 ± 26 vs. 82 ± 24 mL/100 g/min, P, Highlights • The study consisted of a large sample of patients with AD, MCI, and controls. • CBF measured with 2D PC MRI differed between AD patients and controls. • Lower CBF was associated with worse cognitive function measured with MMSE. • 2D PC MRI may be used as a marker for disease severity in a memory clinic.

Purpose To investigate whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and subjects with subjective cognitive decline (SCD) after using the W score method to remove confounding effects of sex and age. Materials and Methods Pseudocontinuous 3.0-T ASL images were acquired in 100 patients with probable AD; 60 patients with MCI, of whom 12 remained stable, 12 were converted to a diagnosis of AD, and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI, and SCD groups were divided into a sex- and age-matched training set (n = 130) and an independent prediction set (n = 130). Standardized perfusion scores adjusted for age and sex (W scores) were computed per voxel for each participant. Training of a support vector machine classifier was performed with diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction set. Prediction performance was assessed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribution. Results Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD (AUC, 0.96; P < .01), good performance for AD versus MCI (AUC, 0.89; P < .01), and poor performance for MCI versus SCD (AUC, 0.63; P = .06). Application of the AD versus SCD discrimination map for prediction of MCI subgroups resulted in good performance for patients with MCI diagnosis converted to AD versus subjects with SCD (AUC, 0.84; P < .01) and fair performance for patients with MCI diagnosis converted to AD versus those with stable MCI (AUC, 0.71; P > .05). Conclusion With automated methods, age- and sex-adjusted ASL perfusion maps can be used to classify and predict diagnosis of AD, conversion of MCI to AD, stable MCI, and SCD with good to excellent accuracy and AUC values. (©) RSNA, 2016.

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.

Brain volume loss, or atrophy, has been proven to be an important characteristic of neurological diseases such as Alzheimer's disease and multiple sclerosis. To use atrophy rate as a reliable clinical biomarker and to increase statistical power in clinical treatment trials, measurement variability needs to be minimized. Among other sources, systematic differences between different MR scanners are suspected to contribute to this variability. In this study we developed and performed initial validation tests of an MR-compatible phantom and analysis software for robust and reliable evaluation of the brain volume loss. The phantom contained three inflatable models of brain structures, i.e. cerebral hemisphere, putamen, and caudate nucleus. Software to reliably quantify volumes form the phantom images was also developed. To validate the method, the phantom was imaged using 3D T1-weighted protocols at three clinical 3T MR scanners from different vendors. Calculated volume change from MRI was compared with the known applied volume change using ICC and mean absolute difference. As assessed by the ICC, the agreement between our developed software and the applied volume change for different structures ranged from 0.999–1 for hemisphere, 0.976–0.998 for putamen, and 0.985–0.999 for caudate nucleus. The mean absolute differences between measured and applied volume change were 109–332 μL for hemisphere, 2.9–11.9 μL for putamen, and 2.2–10.1 μL for caudate nucleus. This method offers a reliable and robust measurement of volume change using MR images and could potentially be used to standardize clinical measurement of atrophy rates., Highlights • We developed a method for standardizing measurements of volume change between different scanners. • The method makes use of an imaging phantom by which volume change can be applied in a highly controlled way. • The phantom structures resemble three brain structures, i.e. hemisphere, putamen and caudate nucleus. • The volume change was quantified from the phantom MR images using developed segmentation software.

Objectives To investigate arterial spin-labelling (ASL) cerebral blood flow (CBF) changes in predementia stages of Alzheimer’s disease (AD). Methods Data were obtained from 177 patients with subjective complaints, mild cognitive impairment and AD from the Amsterdam Dementia Cohort. AD stages were based on diagnosis and cerebrospinal fluid biomarkers amyloid-β (Aβ) and total-tau (tau). General-linear-models were used to assess relationships between AD stages and total and regional CBF, correcting for age and sex. Results Decreasing CBF was related to more advanced AD stages in all supratentorial regions (p for trend < 0.05). Post-hoc testing revealed that CBF was lower in AD compared to controls and stage-1 predementia patients (i.e. abnormal Aβ and normal tau) in temporal and parietal regions, and compared to stage-2 predementia patients (i.e. abnormal Aβ and tau) in temporal regions. CBF values of stage-2 predementia patients were numerically in between those of stage-1 predementia patients and AD. Conclusion The continuing decrease of CBF along the continuum of AD indicates the potential of ASL-CBF as a measure for disease progression. Key Points • Decreasing CBF relates to more advanced AD stages in all supratentorial regions. • The reduction of CBF does not reach a bottom level. • ASL-CBF has potential as a measure for disease progression in AD.

Recently, there has been an increased interest in the use of automatically segmented subfields of the human hippocampal formation derived from magnetic resonance imaging (MRI). However, little is known about the test-retest reproducibility of such measures, particularly in the context of multisite studies. Here, we report the reproducibility of automated Freesurfer hippocampal subfields segmentations in 65 healthy elderly enrolled in a consortium of 13 3T MRI sites (five subjects per site). Participants were scanned in two sessions (test and retest) at least one week apart. Each session included two anatomical 3D T1 MRI acquisitions harmonized in the consortium. We evaluated the test-retest reproducibility of subfields segmentation (i) to assess the effects of averaging two within-session T1 images and (ii) to compare subfields with whole hippocampus volume and spatial reliability. We found that within-session averaging of two T1 images significantly improved the reproducibility of all hippocampal subfields but not that of the whole hippocampus. Volumetric and spatial reproducibility across MRI sites were very good for the whole hippocampus, CA2-3, CA4-dentate gyrus (DG), subiculum (reproducibility error∼2% and DICE > 0.90), good for CA1 and presubiculum (reproducibility error ∼ 5% and DICE ∼ 0.90), and poorer for fimbria and hippocampal fissure (reproducibility error ∼ 15% and DICE

Objectives Alzheimer’s disease (AD) and frontotemporal (FTD) dementia can be differentiated using [18F]-2-deoxy-2-fluoro-D-glucose (FDG)-PET. Since cerebral blood flow (CBF) is related to glucose metabolism, our aim was to investigate the extent of overlap of abnormalities between AD and FTD. Methods Normalized FDG-PET and arterial spin labelling (ASL-MRI)-derived CBF was measured in 18 AD patients (age, 64 ± 8), 12 FTD patients (age, 61 ± 8), and 10 controls (age, 56 ± 10). Voxel-wise comparisons, region-of-interest (ROI), correlation, and ROC curve analyses were performed. Results Voxel-wise comparisons showed decreased CBF and FDG uptake in AD compared with controls and FTD in both precuneus and inferior parietal lobule (IPL). Compared with controls and AD, FTD patients showed both hypometabolism and hypoperfusion in medial prefrontal cortex (mPFC). ASL and FDG were related in precuneus (r = 0.62, p

Contains fulltext : 181592.pdf (Publisher’s version ) (Open Access) Apparent Diffusion Coefficient (ADC) is a potential quantitative imaging biomarker for tumour cell density and is widely used to detect early treatment changes in cancer therapy. We propose a strategy to improve confidence in the interpretation of measured changes in ADC using a data-driven model that describes sources of measurement error. Observed ADC is then standardised against this estimation of uncertainty for any given measurement. 20 patients were recruited prospectively and equitably across 4 sites, and scanned twice (test-retest) within 7 days. Repeatability measurements of defined regions (ROIs) of tumour and normal tissue were quantified as percentage change in mean ADC (test vs. re-test) and then standardised against an estimation of uncertainty. Multi-site reproducibility, (quantified as width of the 95% confidence bound between the lower confidence interval and higher confidence interval for all repeatability measurements), was compared before and after standardisation to the model. The 95% confidence interval width used to determine a statistically significant change reduced from 21.1 to 2.7% after standardisation. Small tumour volumes and respiratory motion were found to be important contributors to poor reproducibility. A look up chart has been provided for investigators who would like to estimate uncertainty from statistical error on individual ADC measurements.

Ultrasound image degradation originates primarily from transducer defects and potentially undermines reliable image interpretation. Systematic quantitative quality control is often neglected due to the limited resources available for this task. We propose a quantitative quality control based on in-air reverberation images. These images serve as an initial indication of image degradation. They are easily generated for any (curvi-)linear transducer independent of the level of expertise of the operator. Automated analysis is presented to extract quality parameters based on the in-air reverberation pattern. Static images acquired by the clinical user are transferred to a server where analysis is performed. The results are available to the sonographer prior to clinical use and transducer status can be remotely monitored with trend analysis over time. The method was evaluated for normal functioning and defect transducers. A pilot study was performed over a period of three weeks to assess reproducibility and practical feasibility. All reverberation images were successfully analysed for different transducer types and vendor-specific image presentation. The proposed quality parameters are sensitive to signal loss and allow differentiation of type and severity of image degradation. The pilot study was well received by the sonographers for the simplicity of the method and the measurements were consistent over time. The proposed automated analysis method of ultrasound quality control can monitor (curvi-)linear transducer status in the entire hospital, overcoming previous limitations for periodic quality control. Implementation of the method can reduce the number of defective transducers routinely used in clinical practice.

Background:Arterial spin labelling (ASL), greymatter (GM) densities and structural volumes have been shown to be strong predictors of dementia in machine learning classification studies [1,2]. Many of these experiments, however, were performed on single modalities in small 'matched' cohorts using voxels, which introduces noise, increases computation time, and limits interpretability and generalisability. We propose an automated classification pipeline thatworks in the patients' native T1 and ASL MRI spaces, is based on anatomically meaningful regions of interest (ROIs), and iteratively selects predictive biomarkers. Methods: 280 patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI) or probableAlzheimer's disease (AD) were included in the study. For all subjects T1 and ASL MRI as well as basic demographics such as age and sex were available (see Table 1). All subjects' MRI scans underwent brain tissue segmentation and parcellation using geodesic information flows (GIF) [3] yielding 143 ROIs per subject. Each ROI was associated with mean GMdensity, structural volume and meanASL perfusion.After α-priori exclusion of non-informative regions such as background or skull, 396 features were used for experiments. Support vector machine (SVM) models were used to differentiate pairwise between AD, MCI, and SCD. 50% of all subjects from each class were randomly assigned to either a training or a testing set to create and validate the classifier, and permuted 1000 times. The classifier assigns weights to each feature and our model recursively removes the 20% of features with the lowest average weight over 1000 repetitions. Results: In all three experiments, the highest accuracies can be observed when only 42- 66 out of 396 features are used (see Figure 1). The maximum obtained accuracies are 91.6% for AD vs SCD, 85.3% for AD vs MCI, and 80.1% for MCI vs SCD. The majority of selected features are structural volumes (51%-60%) but ASL also contributes strongly to the classification outcome with ≤33% of biomarkers. An illustration of selected ROIs is shown in Figure 2. Conclusions:Our SVM model provides a stable mechanism to automatically identify multimodal MRI biomarkers relevant to the diagnosis of dementia whilst surpassing the classification accuracy of previous unimodal studies [1,2]. (Figure Presented).

Item does not contain fulltext There is evidence for a beneficial effect of aerobic exercise on cognition, but underlying mechanisms are unclear. In this study, we test the hypothesis that aerobic exercise increases cerebral blood flow (CBF) in patients with vascular cognitive impairment (VCI). This study is a multicenter single-blind randomized controlled trial among 80 patients with VCI. Most important inclusion criteria are a diagnosis of VCI with Mini-Mental State Examination >/=22 and Clinical Dementia Rating

Introduction We examined the association between decreased cerebral blood flow (CBF) and cognitive impairment in Alzheimer's disease (AD), mild cognitive impairment (MCI), and subjective cognitive decline (SCD). Methods We included 161 AD, 95 MCI, and 143 SCD patients from the Amsterdam Dementia Cohort. We used 3-T pseudo-continuous arterial spin labeling to estimate whole-brain and regional partial volume–corrected CBF. Neuropsychological tests covered global cognition and five cognitive domains. Associations were investigated using linear regression analyses. Results In the whole sample, reduced overall and regional CBF was associated with impairment in all cognitive domains. We found significant interactions between diagnosis and CBF for language and between diagnosis and parietal CBF for global cognition and executive functioning. Stratification showed that decreased CBF was associated with worse performance in AD patients but not in MCI or SCD. Discussion Our results suggest that CBF may have potential as a functional marker of disease severity.

To determine whether lower cerebral blood flow (CBF) is associated with faster cognitive decline in patients with Alzheimer’s disease (AD). We included 88 patients with dementia due to AD from the Amsterdam Dementia Cohort. Mean follow-up was 2 ± 1 years. Linear mixed models were used to determine associations of lower whole brain and regional pseudo-continuous arterial spin labelling measured CBF with rate of cognitive decline as measured with repeated mini-mental state examination (MMSE). Model 1 was adjusted for age, sex, and education. Model 2 was additionally adjusted for normalized gray matter volume, medial temporal lobe atrophy, white matter hyperintensities, microbleeds, and lacunes. Analyses were repeated after partial volume correction (PVC) of CBF. Statistical significance was set at p ≤ 0.05. Patients were 65 ± 7 years old, 44 (50 %) were women, and mean baseline MMSE was 22 ± 4. Annual decline (β[SE]) on the MMSE was estimated at -2.11 (0.25) points per year. Lower whole brain (β[SE]-0.50[0.25]; p ≤ 0.05) and parietal (β[SE]-0.59[0.25]; p

Recently, a new MRI technique was developed at 3 Tesla (T), called fluid attenuated inversion recovery* (FLAIR*). In this study, we implemented FLAIR* in an existing MS cohort at 7 T, to investigate whether we could corroborate results of previous 7 T studies that introduced specific MS lesion characteristics. Furthermore, we aimed to investigate the meaning of these lesion characteristics by relating them to clinical characteristics of the MS patient. Three-dimensional FLAIR and T2*-weighted images of 33 MS patients and 7 healthy controls were fused into FLAIR* images. Lesion type, signal intensity and morphology were analysed on FLAIR*, side-by-side with the original FLAIR and T2*, and correlated with clinical characteristics using Spearman’s rho. Three morphological features of MS lesions were visualised: (1) central vessel (CV) within lesions, present in 78 % of total MS lesions; (2) hypointense rims around MS lesions, present in eight patients; (3) FLAIR* lesions that were hypointense at T2*, present in 13 patients. The presence of hypointense (rims around) lesions was not related to clinical characteristics. The simultaneous presence of rimlike lesions and hypointense lesions within MS patients was significantly correlated (ρ = 0.52, P

Aim A pilot study to phenotype young adults ( 0.05). Peak early diastolic strain rate was reduced in the group with Type 2 diabetes [1.51 (0.24)/s] compared with the lean control [1.97 (0.34)/s, P = 0.001] and obese control [1.78 (0.39)/s, P = 0.042] group. Conclusions Young adults with Type 2 diabetes and those with obesity have similar adverse cardiovascular risk profiles, higher left ventricular mass and a trend towards left ventricular concentric remodelling. In addition, those with Type 2 diabetes demonstrate diastolic dysfunction, a known risk marker for future heart failure and mortality.

Conflicting results on differentiating edema and glioma by diffusion tensor imaging (DTI) are possibly attributable to dissimilar spatial distribution of the lesions. Combining DTI-parameters and enhanced registration might improve prediction. Regions of edema surrounding 22 metastases were compared to tumor-infiltrated regions from WHO grade 2 (12), 3 (10) and 4 (18) gliomas. DTI data was co-registered using Tract Based Spatial Statistics (TBSS), to measure Fractional Anisotropy (FA) and Mean Diffusivity (MD) for white matter only, and relative changes compared to matching reference regions (dFA and dMD). A two-factor principal component analysis (PCA) on metastasis and grade 2 glioma was performed to explore a possible differentiating combined factor. Edema demonstrated equal MD and higher FA compared to grade 2 and 3 glioma (P < 0.001), but did not differ from glioblastoma. Differences were non-significant when corrected for spatial distribution, since reference regions differed strongly (P < 0.001). The second component of the PCA (PCA-C2) did differentiate edema and low-grade tumor (sensitivity 91.7 %, specificity 86.4 %). PCA-C2 scores were plotted voxel-wise as a probability-map, discerning distinct areas of presumed edema or tumor infiltration. Correction of spatial dependency appears essential when differentiating glioma from edema. A tumor-infiltration probability-map is presented, based on supplementary information of multiple DTI parameters and spatial normalization.

To compare pseudo-continuous arterial spin-labelled (PCASL) magnetic resonance imaging (MRI) measured quantitative cerebral blood flow (CBF) of patients with frontotemporal dementia (FTD), dementia with Lewy Bodies (DLB), Alzheimer’s disease (AD) and controls, in a region of interest (ROI) and voxel-wise fashion. We analysed whole-brain 3D fast-spin-echo PCASL images of 20 FTD patients, 14 DLB patients, 48 AD patients and 50 controls from the Amsterdam Dementia Cohort. Regional CBF patterns were compared using analyses of variance for repeated measures. Permutation tests were used for voxel-wise comparisons. Analyses were performed using uncorrected and partial volume corrected (PVC) maps. All analyses were corrected for age and sex. There was an interaction between diagnosis and region (p

Purpose To compare cerebral blood flow (CBF) values measured using magnetic resonance imaging (MRI) arterial spin labeling (ASL) with those obtained with [15O]H2O positron emission tomography (PET), the gold standard for measuring CBF in vivo. Materials and Methods Data were collected in 11 healthy men and in 20 age- and body mass index (BMI)-matched type 1 diabetic men. Pseudo-continuous ASL (PCASL) data were acquired at 3 T and [15O]H2O PET scans were acquired using a high-resolution PET scanner. Input functions were obtained using on-line arterial blood sampling. Whole brain and regional CBF values were compared. Results For both modalities, whole brain CBF was similar in both subject groups. In groups combined, average whole brain CBF was 0.30 ± 0.05 mL·cm−3·min−1 for [15O]H2O PET and 0.34 ± 0.05 mL·cm−3·min−1 for ASL MRI (P < 0.01). A significant correlation between methods was observed for whole brain, gray and white matter. In 12 out of 33 brain regions a significant difference between methods was observed. Conclusion PCASL provides CBF values that correlate with [15O]H2O PET-derived values, but is less accurate. PCASL may be an attractive alternative when absolute quantification is not needed.J. Magn. Reson. Imaging 2014;40:1300–1309. © 2013 Wiley Periodicals, Inc.

Objectives Although myocardial strain analysis is a potential tool to improve patient selection for cardiac resynchronization therapy (CRT), there is currently no validated clinical approach to derive segmental strains. We evaluated the novel segment length in cine (SLICE) technique to derive segmental strains from standard cardiovascular MR (CMR) cine images in CRT candidates. Methods Twenty-seven patients with left bundle branch block underwent CMR examination including cine imaging and myocardial tagging (CMR-TAG). SLICE was performed by measuring segment length between anatomical landmarks throughout all phases on short-axis cines. This measure of frame-to-frame segment length change was compared to CMR-TAG circumferential strain measurements. Subsequently, conventional markers of CRT response were calculated. Results Segmental strains showed good to excellent agreement between SLICE and CMR-TAG (septum strain, intraclass correlation coefficient (ICC) 0.76; lateral wall strain, ICC 0.66). Conventional markers of CRT response also showed close agreement between both methods (ICC 0.61–0.78). Reproducibility of SLICE was excellent for intra-observer testing (all ICC ≥0.76) and good for interobserver testing (all ICC ≥0.61). Conclusions The novel SLICE post-processing technique on standard CMR cine images offers both accurate and robust segmental strain measures compared to the ‘gold standard’ CMR-TAG technique, and has the advantage of being widely available. Key Points • Myocardial strain analysis could potentially improve patient selection for CRT. • Currently a well validated clinical approach to derive segmental strains is lacking. • The novel SLICE technique derives segmental strains from standard CMR cine images. • SLICE-derived strain markers of CRT response showed close agreement with CMR-TAG. • Future studies will focus on the prognostic value of SLICE in CRT candidates. Electronic supplementary material The online version of this article (doi:10.1007/s00330-017-4890-0) contains supplementary material, which is available to authorized users.

Purpose: To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision. Methods: Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b = 100, 500 and 800smm-2). Scanning was performed at 4 institutions (3 MRI vendors). Whole-tumour volumes-of-interest were copied from first visit onto second visit images and from one post-processing platform to an open-source platform, to assess ADC repeatability and cross-platform reproducibility. Results: Whole-tumour ADC values ranged from 0.66-1.94x10-3mm2s-1 (mean = 1.14). Within-patient coefficient-of-variation (wCV) was 7.1% (95% CI 5.7–9.6%), limits-of-agreement (LoA) -18.0 to 21.9%. Lesions >3 cm had improved repeatability: wCV 3.9% (95% CI 2.9–5.9%); and LoA -10.2 to 11.4%. Variability for lesions 2 = 0.99; CoV 2.8% (95% CI 2.3-3.4%); and LoA -7.4 to 8.0%. Conclusion: A free-breathing DW-MRI protocol for imaging malignant lung tumours achieved satisfactory within-patient repeatability and was robust to changes in post-processing software, justifying its use in multi-centre trials. For response evaluation in individual patients, a change in ADC >21.9% will reflect treatment-related change. Key Points: • In lung cancer, free-breathing DWI-MRI produces acceptable images with evaluable ADC measurement. • ADC repeatability coefficient-of-variation is 7.1% for lung tumours >2 cm. • ADC repeatability coefficient-of-variation is 3.9% for lung tumours >3 cm. • ADC measurement precision is unaffected by the post-processing software used. • In multicentre trials, 22% increase in ADC indicates positive treatment response.

BACKGROUND AND PURPOSE: Noninvasive evaluation of retinoblastoma treatment response has become more important due to increased use of eye-sparing treatments. We evaluated the relation between DCE-MR imaging and histopathologic parameters to determine the value of DCE-MR imaging in assessing tumor angiogenesis and prognostic features. MATERIALS AND METHODS: Fifteen consecutive patients with retinoblastoma (mean age, 24 months; range, 2–70 months) undergoing enucleation as the primary treatment (15 eyes) were scanned at 1.5T by using dedicated surface coils. Pretreatment DCE-MR imaging of the most affected eye was evaluated by 2 observers by using curve-pattern analysis, with the first 5 minutes of each curve and the full time-series described as κ 5min and κ 17min , respectively. Assessed histopathologic and immunologic parameters included optic nerve invasion, choroid invasion, MVD, tumor necrosis, and expression of VEGF and Flt-1. RESULTS: The median value of κ 5min was 1.28 (range, 0.87–2.07) and correlated positively with MVD ( P = .008). The median value of κ 17min was 1.33 (range, 0.35–3.08) and correlated negatively with tumor necrosis ( P = .002). Other histopathologic and immunohistopathologic parameters did not correlate with DCE-MR imaging parameters. Interobserver agreement was 0.53 for κ 5min and 0.91 for κ 17min . CONCLUSIONS: In retinoblastoma, the early phase of the DCE time curve positively correlates with MVD, while the presence of late enhancement is correlated with necrosis. Thus, the potential for DCE-MR imaging to noninvasively assess tumor angiogenesis and necrosis in retinoblastoma is promising and warrants further investigation.

Multiecho phase-contrast steady-state free precession (PC-SSFP) is a recently introduced sequence for flow quantification. In this multiecho approach, a phase reference and a velocity-encoded readout were acquired at different echo times after a single excitation. In this study, the sequence is validated in vitro for stationary flow. Subsequently, the sequence was evaluated on cardiac output measurements in vivo for through-plane flow in comparison to regular single gradient echo velocity quantification [phase-contrast spoiled gradient echo (PC-GE)]. In vitro results agreed with regular flow meters (RMS 0.1 cm/s). Cardiac output measurements with multiecho PC-SSFP on 10 healthy subjects gave on average the same results as the standard PC-GE. However, the limits of repeatability of PC-SSFP were significantly larger than those of PC-GE (2 l/min and 0.5 l/min, respectively, P=.001). The multiecho approach introduced some specific problems in vivo. The difference in echo times made the velocity maps sensitive for water-fat shifts and B(0)-drifts, which in turn made velocity offset correction problematic. Also, the addition of a single bipolar gradient cancelled the flow compensated nature of the SSFP sequence. In combination with the prolonged TR, this resulted in flow artifacts caused by high and pulsatile through-plane flow, affecting repeatability. Given the significantly lower repeatability of PC-SSFP, cardiac output in turn is less reliable, thus impairing the use of multiecho PC-SSFP.

Purpose: To test whether a 3D imaging sequence with phase contrast (PC) velocity encoding based on steady-state free precession (SSFP) improves 3D velocity quantification in the heart compared to the currently available gradient echo (GE) approach. Materials and Methods: The 3D PC-SSFP sequence with 1D velocity encoding was compared at the mitral valve in 12 healthy subjects with 3D PC-GE at 1.5T. Velocity measurements, velocity-to-noise-ratio efficiency (VNReff), intra- and interobserver variability of area and velocity measurements, contrast-to-noise-ratio (CNR), and artifact sensitivity were evaluated in both long- and short-axis orientation. Results: Descending aorta mean and peak velocities correlated well (r2 = 0.79 and 0.93) between 3D PC-SSFP and 3D PC-GE. At the mitral valve, mean velocity correlation was moderate (r2 = 0.70 short axis, 0.56 long axis) and peak velocity showed good correlation (r2 = 0.94 short axis, 0.81 long axis). In some cases VNReff was higher, in others lesser, depending on slab orientation and cardiac phase. Intra- and interobserver variability was generally better for 3D PC-SSFP. CNR improved significantly, especially at end systole. Artifact levels did not increase. Conclusion: 3D SSFP velocity quantification was successfully tested in the heart. Blood-myocardium contrast improved significantly, resulting in more reproducible velocity measurements for 3D PC-SSFP at 1.5T. J. Magn. Reson. Imaging 2009;30:947–955. © 2009 Wiley-Liss, Inc.

The relevance of cortical grey matter pathology in multiple sclerosis has become increasingly recognized over the past decade. Unfortunately, a large part of cortical lesions remain undetected on magnetic resonance imaging using standard field strength. In vivo studies have shown improved detection by using higher magnetic field strengths up to 7 T. So far, a systematic histopathological verification of ultra-high field magnetic resonance imaging pulse sequences has been lacking. The aim of this study was to determine the sensitivity of 7 T versus 3 T magnetic resonance imaging pulse sequences for the detection of cortical multiple sclerosis lesions by directly comparing them to histopathology. We obtained hemispheric coronally cut brain sections of 19 patients with multiple sclerosis and four control subjects after rapid autopsy and formalin fixation, and scanned them using 3 T and 7 T magnetic resonance imaging systems. Pulse sequences included T 1 -weighted, T 2 -weighted, fluid attenuated inversion recovery, double inversion recovery and T 2 *. Cortical lesions (type I–IV) were scored on all sequences by an experienced rater blinded to histopathology and clinical data. Staining was performed with antibodies against proteolipid protein and scored by a second reader blinded to magnetic resonance imaging and clinical data. Subsequently, magnetic resonance imaging images were matched to histopathology and sensitivity of pulse sequences was calculated. Additionally, a second unblinded (retrospective) scoring of magnetic resonance images was performed. Regardless of pulse sequence, 7 T magnetic resonance imaging detected more cortical lesions than 3 T. Fluid attenuated inversion recovery (7 T) detected 225% more cortical lesions than 3 T fluid attenuated inversion recovery (Z = 2.22, P < 0.05) and 7 T T 2 * detected 200% more cortical lesions than 3 T T 2 * (Z = 2.05, P < 0.05). Sensitivity of 7 T magnetic resonance imaging was influenced by cortical lesion type: 100% for type I (T 2 ), 11% for type II (FLAIR/T 2 ), 32% for type III (T 2 *), and 68% for type IV (T 2 ). We conclude that ultra-high field 7 T magnetic resonance imaging more than doubles detection of cortical multiple sclerosis lesions, compared to 3 T magnetic resonance imaging. Unfortunately, (subpial) cortical pathology remains more extensive than 7 T magnetic resonance imaging can reveal.

WOS:000366646700041; International audience; To date, limited data are available regarding the inter-site consistency of test-retest reproducibility of functional connectivity measurements, in particular with regard to integrity of the Default Mode Network (DMN) in elderly participants. We implemented a harmonized resting-state fMRI protocol on 13 clinical scanners at 3.0 T using vendor-provided sequences. Each site scanned a group of 5 healthy elderly participants twice, at least a week apart. We evaluated inter-site differences and test-retest reproducibility of both temporal signal-to-noise ratio (tSNR) and functional connectivity measurements derived from: i) seed-based analysis (SBA) with seed in the posterior cingulate cortex (PCC), ii) group independent component analysis (ICA) separately for each site (site ICA), and iii) consortium ICA, with group ICA across the whole consortium. Despite protocol harmonization, significant and quantitatively important inter-site differences remained in the tSNR of resting-state fMRI data; these were plausibly driven by hardware and pulse sequence differences across scanners which could not be harmonized. Nevertheless, the tSNR test-retest reproducibility in the consortium was high (ICC = 0.81). The DMN was consistently extracted across all sites and analysis methods. While significant inter-site differences in connectivity scores were found, there were no differences in the associated test-retest error. Overall, ICA measurements were more reliable than PCC-SBA, with site ICA showing higher reproducibility than consortium ICA. Across the DMN nodes, the PCC yielded the most reliable measurements (approximate to 4% test-retest error, ICC = 0.85), the medial frontal cortex the least reliable (approximate to 12%, ICC = 0.82) and the lateral parietal cortices were in between (site ICA). Altogether these findings support usage of harmonized multisite studies of resting-state functional connectivity to characterize longitudinal effects in studies that assess disease progression and treatment response. (C) 2015 Elsevier Inc. All rights reserved.

Brain activation in studies using blood oxygenation level dependent (BOLD) FMRI is associated with an increase in T2* weighted signal between baseline and an active condition. This BOLD technique is often applied to study differences in brain activation between patients and healthy controls. However, the baseline T2* signal itself may also be different between groups, as shown in the hippocampus in Alzheimer's disease using the resting oxygen or ROXY approach (Small et al. [2002]: Ann Neurol 51:290–295). In the current study, we analyzed whole brain, voxel‐wise T2* weighted signal of averaged baseline scans of a BOLD FMRI experiment in 41 healthy elderly controls and 46 patients with mild cognitive impairment or Alzheimer's disease. In each subject, T2* weighted images were normalized to the CSF signal of the same image. Additionally, gray matter probability maps of high‐resolution structural scans were also compared between groups to assess atrophy. T2* signal was decreased in dementia in the hippocampus, insula/putamen, posterior and middle cingulate cortex, and parietal cortex. Most of these regions also showed decreased gray matter, except insula/putamen. Hippocampal and posterior cingulate gray matter differences were significantly larger than T2* differences. Therefore, decreased T2* signal in most regions are likely to be caused by gray matter atrophy, although decreased metabolism or perhaps iron deposition are also factors that may contribute. We conclude that in FMRI studies of dementia, not only the dynamic BOLD signal (activation and deactivation) but also the average baseline signal is diminished in certain regions. The method we applied may also be used in task‐related BOLD FMRI and add to the understanding of the mechanism of task‐related group differences. Hum Brain Mapp, 2007. © 2007 Wiley‐Liss, Inc.

Co-registration of EEG (Electroencephalogram)-and fMRI (functional magnetic resonance imaging) remains a challenge due to the large artifacts induced on the EEG by the MR (magnetic resonance) sequence gradient and RF pulses. We present an algorithm, based on the average-subtraction method, which is able to correct EEG data for gradient and RF pulse artifacts. We optimized artifact reduction by correcting the misalignment of EEG and fMRI data samples, resulting from the asynchronous sampling of EEG and fMRI data, through interpolation of EEG data. A clustering algorithm is proposed to account for the variability of the pulse artifact. Results show that the algorithm was able to keep the spontaneous brain activity while removing gradient and pulse artifacts with only a subtraction of selectively averaged data. Pulse artifact clustering showed that most of the variability was due to the time jitter of the pulse artifact markers. We show that artifact reduction by average-subtraction is optimized by interpolating the EEG data to correct for asynchronously sampled EEG and fMRI data.