DWI Data Research Articles

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen’d > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BackgroundDiffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.PurposeTo compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.Study TypeProspective.PopulationThirty‐three patients prospectively imaged prior to prostatectomy.Field Strength/Sequence3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.AssessmentDatasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).Statistical TestLevene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.ResultsThe DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.Data ConclusionWe found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer.Level of Evidence1Technical EfficacyStage 3

Data-driven separation of MRI signal components for tissue characterization

PurposeMRI can be utilized for quantitative characterization of tissue. To assess e.g. water fractions or diffusion coefficients for compartments in the brain, a decomposition of the signal is necessary. Imposing standard models carries the risk of estimating biased parameters if model assumptions are violated. This work introduces a data-driven multicomponent analysis, the monotonous slope non-negative matrix factorization (msNMF), tailored to extract data features expected in MR signals. MethodsThe msNMF was implemented by extending the standard NMF with monotonicity constraints on the signal profiles and their first derivatives. The method was validated using simulated data, and subsequently applied to both ex vivo DWI data and in vivo relaxometry data. Reproducibility of the method was tested using the latter. ResultsThe msNMF recovered the multi-exponential signals in the simulated data and showed superiority to standard NMF (based on the explained variance, area under the ROC curve, and coefficient of variation). Diffusion components extracted from the DWI data reflected the cell density of the underlying tissue. The relaxometry analysis resulted in estimates of edema water fractions (EWF) highly correlated with published results, and demonstrated acceptable reproducibility. ConclusionThe msNMF can robustly separate MR signals into components with relation to the underlying tissue composition, and may potentially be useful for e.g. tumor tissue characterization.

Experimental Imaging Study of Encephalomalacia Fluid-Attenuated Inversion Recovery (FLAIR) Hyperintense Lesions in Posttraumatic Epilepsy.

This study introduced new MRI techniques such as neurite orientation dispersion and density imaging (NODDI); NODDI applies a three-compartment tissue model to multishell DWI data that allows the examination of both the intra- and extracellular properties of white matter tissue. This, in turn, enables us to distinguish the two key aspects of axonal pathology—the packing density of axons in the white matter and the spatial organization of axons (orientation dispersion (OD)). NODDI is used to detect possible abnormalities of posttraumatic encephalomalacia fluid-attenuated inversion recovery (FLAIR) hyperintense lesions in neurite density and dispersion. Methods. 26 epilepsy patients associated with FLAIR hyperintensity around the trauma encephalomalacia region were in the epilepsy group. 18 posttraumatic patients with a FLAIR hyperintense encephalomalacia region were in the nonepilepsy group. Neurite density and dispersion affection in FLAIR hyperintense lesions around encephalomalacia were measured by NODDI using intracellular volume fraction (ICVF), and we compare these findings with conventional diffusion MRI parameters, namely, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Differences were compared between the epilepsy and nonepilepsy groups, as well as in the FLAIR hyperintense part and in the FLAIR hypointense part to try to find neurite density and dispersion differences in these parts. Results. ICVF of FLAIR hyperintense lesions in the epilepsy group was significantly higher than that in the nonepilepsy group (P < 0.001). ICVF reveals more information of FLAIR(+) and FLAIR(-) parts of encephalomalacia than OD and FA and ADC. Conclusion. The FLAIR hyperintense part around encephalomalacia in the epilepsy group showed higher ICVF, indicating that this part may have more neurite density and dispersion and may be contributing to epilepsy. NODDI indicated high neurite density with the intensity of myelin in the FLAIR hyperintense lesion. Therefore, NODDI likely shows that neurite density may be a more sensitive marker of pathology than FA.

Selective Motor Control is a Clinical Correlate of Brain Motor Tract Impairment in Children with Spastic Bilateral Cerebral Palsy.

Selective voluntary motor control is an important factor influencing gross motor function, interjoint coordination, and the outcome of hamstring-lengthening surgery in spastic cerebral palsy. Using DTI, we investigated whether selective voluntary motor control would show strong correlations with WM motor tract microstructure and whether selective voluntary motor control is more sensitive to global WM impairment than gross motor function. Children with spastic bilateral cerebral palsy born preterm and typically developing children were recruited. The Selective Control Assessment of the Lower Extremity (SCALE) and Gross Motor Function Measure (GMFM) were assessed in participants with cerebral palsy. Participants underwent brain MR imaging to collect DWI data. Tract-Based Spatial Statistics was used to analyze the WM for between-group differences and correlations with SCALE and GMFM. ROI analyses compared motor regions. Twelve children with cerebral palsy (mean age, 11.5 years) and 12 typically developing children (mean age, 10.3 years) participated. Altered DTI outcomes were found throughout the whole brain for the cerebral palsy group. SCALE, developed to evaluate selective voluntary motor control in cerebral palsy, showed significant positive correlations with fractional anisotropy in more WM voxels throughout the whole brain and for motor regions, including the corticospinal tract and corpus callosum, compared with GMFM. A significant negative correlation between radial diffusivity and SCALE, but not GMFM, was found within the corpus callosum. SCALE was a more sensitive clinical correlate of motor and whole-brain WM tract impairment in children with spastic bilateral cerebral palsy, suggesting greater anisotropy and myelination in these regions for those with higher selective voluntary motor control.

Regularized joint water-fat separation with B0 map estimation in image space for 2D-navigated interleaved EPI based diffusion MRI.

PurposeTo develop a new water–fat separation and B0 estimation algorithm to effectively suppress the multiple resonances of fat signal in EPI. This is especially relevant for DWI where fat is often a confounding factor.MethodsWater–fat separation based on chemical‐shift encoding enables robust fat suppression in routine MRI. However, for EPI the different chemical‐shift displacements of the multiple fat resonances along the phase‐encoding direction can be problematic for conventional separation algorithms. This work proposes a suitable model approximation for EPI under B0 and fat off‐resonance effects, providing a feasible multi‐peak water–fat separation algorithm. Simulations were performed to validate the algorithm. In vivo validation was performed in 6 volunteers, acquiring spin‐echo EPI images in the leg (B0 homogeneous) and head‐neck (B0 inhomogeneous) regions, using a TE‐shifted interleaved EPI sequence with/without diffusion sensitization. The results are numerically and statistically compared with voxel‐independent water–fat separation and fat saturation techniques to demonstrate the performance of the proposed algorithm.ResultsThe reference separation algorithm without the proposed spatial shift correction caused water–fat ambiguities in simulations and in vivo experiments. Some spectrally selective fat saturation approaches also failed to suppress fat in regions with severe B0 inhomogeneities. The proposed algorithm was able to achieve improved fat suppression for DWI data and ADC maps in the head–neck and leg regions.ConclusionThe proposed algorithm shows improved suppression of the multi‐peak fat components in multi‐shot interleaved EPI applications compared to the conventional fat saturation approaches and separation algorithms.

Evaluation of the Predictive Potential of 18F-FDG PET and DWI Data Sets for Relevant Prognostic Parameters of Primary Soft-Tissue Sarcomas

Simple SummaryIn this study, we assessed the potential of simultaneously acquired 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and magnetic resonance imaging-derived quantitative imaging parameters to predict the tumor grade, metastatic status, and response to neoadjuvant therapy of primary soft-tissue sarcomas of the extremities. Based on the results of the present study, quantifications of the 18F-FDG PET uptake provide useful prognostic data for the evaluation of histopathological response to neoadjuvant treatment as well as the aggressiveness of high-risk sarcomas, whereas no correlation between the different outcome variables and the results for tumor size and diffusion-weighted imaging-derived apparent diffusion coefficient values was found. Accordingly, measurements of the metabolic activity of primary and untreated soft-tissue sarcomas could non-invasively deliver relevant information, that may be used for treatment planning and risk-stratification of sarcoma patients in a pretherapeutic setting.Background: To evaluate the potential of simultaneously acquired 18F-FDG PET- and MR-derived quantitative imaging data sets of primary soft-tissue sarcomas for the prediction of neoadjuvant treatment response, the metastatic status and tumor grade. Methods: A total of 52 patients with a high-risk soft-tissue sarcoma underwent a 18F-FDG PET/MR examination within one week before the start of neoadjuvant treatment. For each patient, the maximum tumor size, metabolic activity (SUVs), and diffusion-restriction (ADC values) of the tumor manifestations were determined. A Mann–Whitney-U test was used, and ROC analysis was performed to evaluate the potential to predict histopathological treatment response, the metastatic status or tumor grade. The results from the histopathological analysis served as reference standard. Results: Soft-tissue sarcomas with a histopathological treatment response revealed a significantly higher metabolic activity than tumors in the non-responder group. In addition, grade 3 tumors showed a significant higher 18F-FDG uptake than grade 2 tumors. Furthermore, no significant correlation between the different outcome variables and tumor size or calculated ADC-values could be identified. Conclusion: Measurements of the metabolic activity of primary and untreated soft-tissue sarcomas could non-invasively deliver relevant information that may be used for treatment planning and risk-stratification of high-risk sarcoma patients in a pretherapeutic setting.

On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.

The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation. Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value=0-200-400-600-800-1000s/mm2) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84×39=3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ). No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation. Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation.

Proximity to dementia onset and multi-modal neuroimaging changes: The prevent-dementia study

BackgroundFirst-degree relatives of people with dementia (FH+) are at increased risk of developing Alzheimer's disease (AD). Here, we investigate “estimated years to onset of dementia” (EYO) as a surrogate marker of preclinical disease progression and assess its associations with multi-modal neuroimaging biomarkers. Methods89 FH+ participants in the PREVENT-Dementia study underwent longitudinal MR imaging over 2 years. EYO was calculated as the difference between the parental age of dementia diagnosis and the current age of the participant (mean EYO = 23.9 years). MPRAGE, ASL and DWI data were processed using Freesurfer, FSL-BASIL and DTI-TK. White matter lesion maps were segmented from FLAIR scans. The SPM Sandwich Estimator Toolbox was used to test for the main effects of EYO and interactions between EYO, Time, and APOE-ε4+. Threshold free cluster enhancement and family wise error rate correction (TFCE FWER) was performed on voxelwise statistical maps. ResultsThere were no significant effects of EYO on regional grey matter atrophy or white matter hyperintensities. However, a shorter EYO was associated with lower white matter Fractional Anisotropy and elevated Mean/Radial Diffusivity, particularly in the corpus callosum (TFCEFWERp < 0.05). The influence of EYO on white matter deficits were significantly stronger compared to that of normal ageing. APOE-ε4 carriers exhibited hyperperfusion with nearer proximity to estimated onset in temporo-parietal regions. There were no interactions between EYO and time, suggesting that EYO was not associated with accelerated imaging changes in this sample. ConclusionsAmongst cognitively normal midlife adults with a family history of dementia, a shorter hypothetical proximity to dementia onset may be associated with incipient brain abnormalities, characterised by white matter disruptions and perfusion abnormalities, particularly amongst APOE-ε4 carriers. Our findings also confer biological validity to the construct of EYO as a potential stage marker of preclinical progression in the context of sporadic dementia. Further clinical follow-up of our longitudinal sample would provide critical validation of these findings.

Why Is a b-value Range of 1500-2000 s/mm² Optimal for Evaluating Prostatic Index Lesions on Synthetic Diffusion-Weighted Imaging?

ObjectiveIt is uncertain why a b-value range of 1500–2000 s/mm2 is optimal. This study was aimed at qualitatively and quantitatively analyzing the optimal b-value range of synthetic diffusion-weighted imaging (sDWI) for evaluating prostatic index lesions.Materials and MethodsThis retrospective study included 92 patients who underwent DWI and targeted biopsy for magnetic resonance imaging (MRI)-suggested index lesions. We generated sDWI at a b-value range of 1000–3000 s/mm2 using dedicated software and true DWI data at b-values of 0, 100, and 1000 s/mm2. We hypothesized that lesion conspicuity would be best when the background (i.e., MRI-suggested benign prostatic [bP] and periprostatic [pP] regions) signal intensity (SI) is suppressed and becomes homogeneous. To prove this hypothesis, we performed both qualitative and quantitative analyses. For qualitative analysis, two independent readers analyzed the b-value showing the best visual conspicuity of an MRI-suggested index lesion. For quantitative analysis, the readers assessed the b-value showing the same bP and pP region SI. The 95% confidence interval (CI) or interquartile range of qualitatively and quantitatively selected optimal b-values was assessed, and the mean difference between qualitatively and quantitatively selected b-values was investigated.ResultsThe 95% CIs of optimal b-values from qualitative and quantitative analyses were 1761–1805 s/mm2 and 1640–1771 s/mm2 (median, 1790 s/mm2 vs. 1705 s/mm2; p = 0.003) for reader 1, and 1835–1895 s/mm2 and 1705–1841 s/mm2 (median, 1872 s/mm2 vs. 1763 s/mm2; p = 0.022) for reader 2, respectively. Interquartile ranges of qualitatively and quantitatively selected optimal b-values were 1735–1873 s/mm2 and 1573–1867 s/mm2 for reader 1, and 1775–1945 s/mm2 and 1591–1955 s/mm2 for reader 2, respectively. Bland–Altman plots consistently demonstrated a mean difference of less than 100 s/mm2 between qualitatively and quantitatively selected optimal b-values.Conclusionb-value range showing a homogeneous background signal may be optimal for evaluating prostatic index lesions on sDWI. Our qualitative and quantitative data consistently recommend b-values of 1500–2000 s/mm2.

Diffusivity in breast malignancies analyzed for b > 1000 s/mm2 at 1 mm in-plane resolutions: Insight from Gaussian and non-Gaussian behaviors.

Diffusion-weighted imaging (DWI) can improve breast cancer characterizations, but often suffers from low image quality -particularly at informative b > 1000 s/mm2 values. The aim of this study was to evaluate multishot approaches characterizing Gaussian and non-Gaussian diffusivities in breast cancer. This was a prospective study, in which 15 subjects, including 13 patients with biopsy-confirmed breast cancers, were enrolled. DWI was acquired at 3 T using echo planar imaging (EPI) with and without zoomed excitations, readout-segmented EPI (RESOLVE), and spatiotemporal encoding (SPEN); dynamic contrast-enhanced (DCE) data were collected using three-dimensional gradient-echo T1 weighting; anatomies were evaluated with T2 -weighted two-dimensional turbo spin-echo. Congruence between malignancies delineated by DCE was assessed against high-resolution DWI scans with b-values in the 0-1800 s/mm2 range, as well as against apparent diffusion coefficient (ADC) and kurtosis maps. Data were evaluated by independent magnetic resonance scientists with 3-20 years of experience, and radiologists with 6 and 20 years of experience in breast MRI. Malignancies were assessed from ADC and kurtosis maps, using paired t tests after confirming that these values had a Gaussian distribution. Agreements between DWI and DCE datasets were also evaluated using Sorensen-Dice similarity coefficients. Cancerous and normal tissues were clearly separable by ADCs: by SPEN their average values were (1.03 ± 0.17) × 10-3 and (1.69 ± 0.19) × 10-3 mm2 /s (p < 0.0001); by RESOLVE these values were (1.16 ± 0.16) × 10-3 and (1.52 ± 0.14) × 10-3 (p=0.00026). Kurtosis also distinguished lesions (K=0.64 ± 0.15) from normal tissues (K=0.45 ± 0.05), but only when measured by SPEN (p=0.0008). The best statistical agreement with DCE-highlighted regions arose for SPEN-based DWIs recorded with b=1800 s/mm2 (Sorensen-Dice coefficient=0.67); DWI data recorded with b=850 and 1200 s/mm2 , led to lower coefficients. Both ADC and kurtosis maps highlighted the breast malignancies, with ADCs providing a more significant separation. The most promising alternative for contrast-free delineations of the cancerous lesions arose from b=1800 s/mm2 DWI. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.

Multifaceted pattern of longitudinal imaging changes in cognitively normal midlife adults: The PREVENT‐Dementia study

AbstractBackgroundFirst‐degree relatives of people with dementia (FH+) are at increased risk of developing Alzheimer’s disease (AD). Identifying preclinical brain changes would facilitate both early quantifiable longitudinal disease monitoring and interventional trials. However, the literature of imaging changes in FH+ remains highly controversial. We aim to investigate “estimated years to onset of dementia” (EYOD) as a surrogate marker of preclinical progression and assessed whether FH+ approaching the onset of dementia would express more severe patterns of longitudinal brain changes.Method89 FH+ persons underwent serial 3T‐MRI with T1‐MPRAGE, DWI and ASL (mean interval: 2 years). The longitudinal Freesurfer pipeline was used to segment hippocampal subfields and early‐AD regions. Partial‐volume corrected ASL data and DWI data were processed with FSL‐BASIL4 and DTI‐TK5. Small vessel disease was indexed with white matter hyperintensities (WMHs) (Figure 1). We applied linear mixed effects models with random intercepts to predict MRI changes relative to baseline EYOD, accounting for baseline age, gender, education and time from baseline. Secondary analyses examined the interaction of APOE‐ε4+ on proximity‐related brain changes.ResultA shorter EYOD was associated with progressive decrease in FA, increase in MD/RD and atrophy of the fusiform gyrus over time (Figure 2). Interaction analyses revealed that APOE‐ε4+ carriers showed significant longitudinal atrophy of the hippocampal subfields with shorter EYOD. In contrast, cortical perfusion increased before plateauing ∼10 years before estimated onset. There were no associations of EYOD with mean cortical thickness or WMHs (See Figure 3 for a summary of main findings).ConclusionOur findings represent an extensive characterisation of longitudinal brain changes associated with proximity to dementia in FH+. Individuals who are approaching their parental age of dementia onset may be showing incipient brain abnormalities, particularly amongst APOE‐ε4 carriers. The involvement of early‐AD regions also confer support to the biological validity of EYOD as a stage marker of preclinical progression. Further clinical follow‐up of our sample would provide critical validation of these findings.

Multi‐faceted brain changes associated with proximity to dementia onset: The PREVENT‐Dementia study

AbstractBackgroundFirst‐degree relatives of people with dementia (FH+) are at increased risk of developing Alzheimer’s disease (AD). Identifying preclinical brain changes would facilitate both early quantifiable longitudinal disease monitoring and interventional trials. However, the literature of imaging changes in FH+ remains highly controversial. We aim to investigate “estimated years to onset of dementia” (EYOD) as a surrogate marker of preclinical progression and assessed whether FH+ approaching the parental age of dementia onset would express more severe patterns of longitudinal brain changes.Method89 FH+ persons underwent serial 3T‐MRI with T1‐MPRAGE, DWI and ASL (mean interval: 2 years). The longitudinal Freesurfer pipeline was used to segment hippocampal subfields and early‐AD regions. Partial‐volume corrected ASL data and DWI data were processed with FSL‐BASIL4 and DTI‐TK5. Small vessel disease was indexed with white matter hyperintensities (WMHs) (Figure 1). We applied linear mixed effects models to predict MRI changes relative to EYOD, accounting for age, gender, education and time from baseline. Secondary analyses examined the interaction of APOE‐ε4+ on proximity‐related brain changes.ResultA shorter EYOD was associated with progressive decrease in FA, increase in MD/RD and atrophy of the fusiform gyrus over time (Figure 2). Interaction analyses revealed that APOE‐ε4+ carriers showed significant longitudinal atrophy of the hippocampal subfields with shorter EYOD. In contrast, cortical perfusion increased before plateauing ∼10 years before estimated onset. There were no associations of EYOD with mean cortical thickness or WMHs (See Figure 3 for a summary of main findings).ConclusionOur findings represent the first extensive characterisation of longitudinal brain changes associated with proximity to dementia in FH+. Individuals who are approaching their parental age of dementia onset may be showing incipient brain abnormalities, particularly amongst APOE‐ε4 carriers. The specificity our findings in early‐AD regions also confer support to biological validity of EYOD as a stage marker of preclinical progression. Further ongoing clinical follow‐up of our sample would provide critical validation of these findings.

QSM in canine model of acute cerebral ischemia: A pilot study.

In the present study, we investigated the potential of QSM to assess the physiological state of cortical tissue in the middle cerebral artery occlusion canine model of a cerebral ischemia. Experiments were performed in 8 anesthetized canines. Gradient echo, perfusion, and DWI data of brains at normal and ischemic states were acquired. In the postprocessed susceptibility and quantitative cerebral blood flow maps, changes in values within the middle cerebral artery-fed cortical territories were quantified both on the ischemic and normal contralateral hemisphere side. QSM values in critically ischemic tissue were significantly different from contralateral values-namely, susceptibility increase was observed in the cases in which cerebral perfusion was maintained above the threshold of neuronal death. Furthermore, the data indicates presence of a significant correlation between the changes in susceptibility values, cerebral perfusion, and the infarct volume and pial collateral scores. Additionally, our data suggests that difference in cortical susceptibility is prospectively indicative of the infarct growth rate. In an experimental permanent middle cerebral artery occlusion model, QSM was shown to correlate with the functional parameters characterizing viability of ischemic tissue, thus warranting further research on its ability to provide complementary information during acute stroke MRI examinations in humans.

Value of reduced field-of-view diffusion-weighted MRI for prostate cancer detection in the TRUS-guided transperineal prostate biopsy

Objective To observe the value of reduced field-of-view diffusion-weighted MRI (rFOV DWI) for prostate cancer (PCa) detection in the TRUS-guided transperineal prostate biopsy. Methods A total of 54 patients with suspected PCa underwent rFOV DWI before TRUS-guided prostate biopsy were prospectively enrolled in this study. All rFOV DWI data were carried out by two independent radiologists using prostate imaging-reporting and data system (PI-RADS) assessment category, and systemic 10-core biopsy (SB) combined with rFOV DWI targeted biopsy (rFOV DWI-TB) were taken. According to histopathology results, the performance of rFOV DWI was analysed. Additionally, the detection rate of PCa and the positive rate of punctures of SB, rFOV DWI-TB and SB combined with rFOV DWI-TB were compared. Results Overall, 26 of the 54 patients (48.15%) had histologically confirmed PCa. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for detecting PCa with rFOV DWI was 80.77%, 89.29%, 87.5% and 83.33%, respectively. The ROC curve analysis revealed the AUC was 0.916. Additionally, the sensitivity for detecting clinically significant PCa with rFOV DWI was 95%. The PCa detection rate of SB, rFOV DWI-TB, and combination of SB and rFOV DWI-TB was 37.04%, 42.59% and 48.15%, respectively. The detection rate for combination of SB and rFOV DWI-TB was significantly higher than that of SB only (P=0.031). Additionally, the detection rate for rFOV DWI-TB cores (78.33%) was significantly better than for SB cores (15.5%)(χ2=124.377, P=0.000). Conclusions rFOV DWI may be useful for providing more information of PCa lesions in targeted prostate biopsy, and has potential value in clinically significant PCa detecting. Key words: Prostatic Neoplasms; Magnetic Resonance Imaging; Biopsy, Needle; Ultrasonography

1411. Differentiation of Fungal Abscess of Brain From Brain Glioblastoma by MRI Scan ADC Value

BackgroundDifferentiation of Brain fungal abscess from brain glioblastoma is very important. Treatment strategies are different in both cases. Glioblastoma needs different surgical planning as compared with abscess. We hypothesized that preoperative MRI brain will help in differentiation.MethodsA retrospective study was carried out at Aga Khan University Hospital Karachi. Data from 2007 to 2017 was retrieved from Medical records using ICD-9 coding. 90 patients (47 males and 43 females; age range 10–70 years) with aggressive grade IV brain lesion and fungal abscess was selected and their preoperative MRI data were retrieved. The apparent diffusion coefficient (ADC) maps were reconstructed from diffusion-weighted imaging DWI data. The mean ADC value and standard deviation of glioblastoma and fungal abscesses were calculated. Postoperative biopsy results were used as gold standard.The analysis of data was done to test statistically significant differences between glioblastoma and fungal brain abscess. Receiver operating characteristic (ROC) curve was used to determine the cut-off point with highest accuracy that was used to differentiate malignancy from infection. The statistical analysis of data was done using SPSS® v. 19.ResultsThe mean (±standard deviation) ADC value of fungal brain abscess (826.040 ± 94.97 × 0.001 mm2/sec) was significantly lower than that of glioblastoma (1,462.800 ± 163.66 × 0.001 mm2/sec). When an ADC value of 1,200 × 0.001 mm2/s used as a cutoff value for differentiating fungal abscess from glioblastoma the best result was obtained with an accuracy of 85%, sensitivity of 81%, specificity of 88%, negative predictive value of 80%, and positive predictive value of 90%.ConclusionADC value is a noninvasive promising imaging parameter that can be used for differentiation of fungal brain abscess and glioblastoma on preoperative scan. The cut-off value with best accuracy in our study was 1,200 × 0.001 mm2/s. Disclosures All authors: No reported disclosures.

CT and Multimodal MR Imaging Features of Embryonal Tumors with Multilayered Rosettes in Children.

Embryonal tumors with multilayered rosettes, C19MC-altered, are brain tumors occurring in young children, which were clearly defined in the 2016 World Health Organization classification of central nervous system neoplasms. Our objective was to describe the multimodal imaging characteristics of this new entity. We performed a retrospective monocentric review of embryonal brain tumors and looked for embryonal tumors with multilayered rosettes with confirmed C19MC alteration. We gathered morphologic imaging data, as well as DWI and PWI data (using arterial spin-labeling and DSC). We included 16 patients with a median age of 2 years 8 months. Tumors were both supratentorial (56%, 9/16) and infratentorial (44%, 7/16). Tumors were large (median diameter, 59 mm; interquartile range, 48-71 mm), with absent (75%, 12/16) or minimal (25%, 4/16) peritumoral edema. Enhancement was absent (20%, 3/15) or weak (73%, 11/15), whereas intratumoral macrovessels were frequently seen (94%, 15/16) and calcifications were present in 67% (10/15). Diffusion was always restricted, with a minimal ADC of 520 mm2/s (interquartile range, 495-540 mm2/s). Cerebral blood flow using arterial spin-labeling was low, with a maximal CBF of 43 mL/min/100 g (interquartile range, 33-55 mL/min/100 g 5). When available (3 patients), relative cerebral blood volume using DSC was high (range, 3.5-5.8). Embryonal tumors with multilayered rosettes, C19MC-altered, have characteristic imaging features that could help in the diagnosis of this rare tumor in young children.

Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data.

A new method for fitting diffusion-weighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multi-exponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β0 correlated with IVIM-D: ρ = 0.708, P < 0.001. ADAPT(1,1)-α1 correlated with IVIM-f: ρ = 0.667, P < 0.001. ADAPT(1,1)-β1 correlated with IVIM-D*: ρ = 0.741, P < 0.001). ADAPT provides a method that can identify the number of exponential components in DWI data without prior assumptions, and determine potential complex diffusion biomarkers. ADAPT has the potential to provide a generalized fitting method for discrete multi-exponential data, and determine meaningful coefficients without prior information.

Impact of isolated basal ganglia infarction at pretreatment DWI on outcomes after endovascular thrombectomy in acute anterior circulation stroke.

Acute infarction confined to the basal ganglia (BG) is occasionally observed on baseline imaging before endovascular thrombectomy. This study aimed to investigate the impact of isolated BG infarction revealed on pretreatment DWI in a large cohort of patients with acute anterior circulation stroke who underwent thrombectomy. We retrospectively analyzed clinical and DWI data from 328 patients who underwent thrombectomy for emergent occlusions of the intracranial internal carotid artery or the middle cerebral artery. Characteristics and treatment outcomes were compared between patients with isolated BG infarction and those with non-isolated BG infarction. Binary logistic regression analyses were performed to identify independent predictors of good outcome (90-day mRS 0-2). Isolated BG infarction was found in 57 patients (17.4%). Patients with isolated BG infarction had a higher incidence of underlying severe intracranial atherosclerotic stenosis (21.1% vs. 10.7%, P = 0.032) than those with non-isolated BG infarction. Successful reperfusion occurred more frequently in patients with isolated BG infarction than those with non-isolated BG infarction (93% vs. 79%, odds ratio 3.529, 95% confidence interval 1.226-10.161, P = 0.014). On multivariate logistic regression analysis, independent predictors of good outcome were age, DWI-ASPECTS, and admission NIHSS score. There was no significant difference in the rate of good outcome between the two groups (54.4% vs. 42.8%, P = 0.110). Isolated BG infarction on pretreatment DWI may predict successful reperfusion after endovascular thrombectomy in patients with acute anterior circulation stroke. In addition, our study suggested a novel finding that isolated BG infarction was more frequently associated with underlying severe ICAS than non-isolated BG infarction.

Collateral circulation assessment within the 4.5 h time window in patients with and without DWI/FLAIR MRI mismatch

ObjectivesThe aim of the present study was to assess the association between collateral status and DWI-FLAIR mismatch in patients with acute ischemic stroke within the 4.5 h time-window. MethodsWe analysed DWI, FLAIR, and PWI data in patients within 4.5 h after symptom onset from the I-KNOW European database. Collateral flow maps were graded by analyzing contrast ‘staining’ extent over the early, mid and late perfusion phases. ADC values, DWI lesion volume, and normalised perfusion parameters (CBV,Tmax) within DWI lesions were determined. Visibility of parenchymal hyperintensivty on FLAIR was evaluated (“FLAIR positive”), and DWI-FLAIR mismatch was assessed. Spontaneously reperfused regions were defined as voxels with Tmax <6 s within the DWI lesion. Final infarct size was assessed on day-30 FLAIR images. ResultsOf the 168 patients included in I-KNOW database, 87 were eligible for this study. DWI-FLAIR mismatch was present in 69 patients. There was no difference between poor and good collaterals status according to age, sex, baseline NIHSS score, time to MRI and DWI lesion volume. Collateral status was significantly better in the FLAIR positive group (p = .001). Patients with poor collaterals had significantly increased Tmax (p = .005). Baseline DWI lesion volume and final lesion volume were significantly smaller in patients with good collateral status (p < .001 and 0.01, respectively). ConclusionsWe found that patients with early FLAIR lesion visibility have a better collateral status. This finding has implications for the management of stroke patients with unknown time-of-onset, and more widely should be considered in the current context of extending the therapeutic window.