Fiber visualization for preoperative glioma assessment: Tractography versus local connectivity mapping.

In diffusion MRI, the advent of high angular resolution diffusion imaging (HARDI) and HARDI with compressed sensing (HARDI+CS) has led to clinically practical signal acquisition techniques which allow for the assessment of white matter architecture in routine patient studies. However, the reconstruction and visualization of fiber pathways by tractography has not yet been established as a standard methodology which can easily be applied. This is due to various algorithmic problems, such as a lack of robustness, error propagation and the necessity of fine-tuning parameters depending on the clinical question. In the framework of a clinical study of glioma patients, we compare two different whole-brain tracking methods to a local connectivity mapping approach which has recently shown promising results in an adaptation to diffusion MRI. The ability of the three methods to correctly depict fiber affection is analyzed by comparing visualization results to representations of local diffusion profiles provided by orientation distribution functions (ODFs). Our results suggest that methods beyond fiber tractography, which visualize local connectedness rather than global connectivity, should be evaluated further for pre-surgical assessment of fiber affection.

Effect of Perfusion on Diffusion Kurtosis Imaging Estimates for In Vivo Assessment of Integrated 2016 WHO Glioma Grades : A Cross-Sectional Observational Study.

PURPOSE: To assess the role of perfusion-related signal decay on diffusion kurtosis imaging (DKI) estimates for in vivo stratification of glioma according to the integrated approach of the 2016 World Health Organization classification of tumors of the central nervous system (2016 CNS WHO). METHODS: In this study 77 patients with histopathologically confirmed glioma were retrospectively assessed between January 2013 and February 2017 in a prospective trial. Mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were assessed by two physicians blinded to the study from a volume of interest around the entire solid tumor. Wilcoxon's signed-rank test compared perfusion-biased and perfusion-corrected MK (MKpb and MKpc) and MD (MDpb, MDpc) values. One-way ANOVA was used to compare MKpb&pc and MDpb&pc values between 2016 WHO glioma grades. Spearman's correlation coefficient was used to correlate them with 2016 WHO glioma grades. Receiver operating characteristic (ROC) analysis was performed on MKpb&pc and MDpb&pc for the significant results. RESULTS: The MKpc values were significantly higher than MKpb values (p < 0.001), whereas MDpc values were significantly lower than MDpb values (p < 0.001). For stratifying gliomas, MKpb values (ROC AUC range, 0.818-0.979) showed a higher diagnostic performance than MKpc values (ROC AUC range, 0.773-0.975), whereas MDpb values (ROC AUC range, 0.744-0.928) showed less diagnostic performance than MDpc values (ROC AUC range, 0.753-0.934). The diagnostic accuracy of MKpb was 80.0%. CONCLUSION: The MK and MD estimates of DKI are influenced by microcapillary blood perfusion; however, taking the effect of perfusion on DKI metrics into account does not substantially impact their overall diagnostic performance in classifying glioma according to the 2016 CNS WHO.

Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: A cross-sectional observational study.

PURPOSE: To assess the diagnostic performance of histogram analysis of diffusion kurtosis imaging (DKI) maps for in vivo assessment of the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO) integrated glioma grades. MATERIALS AND METHODS: Seventy-seven patients with histopathologically-confirmed glioma who provided written informed consent were retrospectively assessed between 01/2014 and 03/2017 from a prospective trial approved by the local institutional review board. Ten histogram parameters of mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were independently assessed by two blinded physicians from a volume of interest around the entire solid tumor. One-way ANOVA was used to compare MK and MD histogram parameter values between 2016 CNS WHO-based tumor grades. Receiver operating characteristic analysis was performed on MK and MD histogram parameters for significant results. RESULTS: The 25th, 50th, 75th, and 90th percentiles of MK and average MK showed significant differences between IDH1/2wild-type gliomas, IDH1/2mutated gliomas, and oligodendrogliomas with chromosome 1p/19q loss of heterozygosity and IDH1/2mutation (p<0.001). The 50th, 75th, and 90th percentiles showed a slightly higher diagnostic performance (area under the curve (AUC) range; 0.868-0.991) than average MK (AUC range; 0.855-0.988) in classifying glioma according to the integrated approach of 2016 CNS WHO. CONCLUSIONS: Histogram analysis of DKI can stratify gliomas according to the integrated approach of 2016 CNS WHO. The 50th (median), 75th, and the 90th percentiles showed the highest diagnostic performance. However, the average MK is also robust and feasible in routine clinical practice.

Acute stroke imaging: feasibility and value of MR angiography with high spatial and temporal resolution for vessel assessment and perfusion analysis in patients with wake-up stroke.

RATIONALE AND OBJECTIVES: Magnetic resonance (MR) imaging (MRI) provides information that can be used to estimate the symptom onset in patients with wake-up stroke (WUS). Time-resolved MR angiography (MRA) is the fastest available MR sequence technique for vessel assessment, and the different phases acquired can provide information about cerebral perfusion. The aim of this study was to evaluate the diagnostic performance of time-resolved MRA both for the assessment of vessel morphology and for the feasibility of perfusion. MATERIALS AND METHODS: Nineteen patients with WUS were included. Image quality and vessel pathologies were evaluated and correlated to time-of-flight-MRA (n = 14), computed tomography-angiography (n = 4), sonography (n = 12), and conventional angiography (n = 6). The temporal delay of signal enhancement in all pixels of the time-resolved MRA measurement after contrast injection was evaluated and compared to dynamic susceptibility contrast-enhanced (DSC) perfusion imaging (n = 13). RESULTS: Time-resolved MRA resulted in the diagnosis of large vessel disease in 14 of 19 patients, involving the internal carotids (n = 4), the vertebral arteries (n = 3), and the circle of Willis (n = 10). All severe vascular pathologies which influence patients' acute stroke therapy were obtained by time-resolved MRA. Overestimation of stenoses in two of 14 patients resulted in sensitivity and specificity of 100% and 71%, respectively. Time-to-peak (TTP) estimations were hampered by movement artifacts in four patients (31%). Compared to DSC, the area of TTP delay was comparable in size and localization without relevant overestimation or underestimation. CONCLUSIONS: Time-resolved MRA is a valuable technique in patients with WUS with high sensitivity and high negative predictive value. Cerebral perfusion estimation can be performed in selected cases for therapy decision but can be hampered by patient movement.

Correlative assessment of tumor microcirculation using contrast-enhanced perfusion MRI and intravoxel incoherent motion diffusion-weighted MRI: is there a link between them?

The purpose of this study was to correlate intravoxel incoherent motion (IVIM) imaging with classical perfusion-weighted MRI metrics in human gliomas. Parametric images for slow diffusion coefficient (D), fast diffusion coefficient (D*), and fractional perfusion-related volume (f) in patients with high-grade gliomas were generated. Maps of Fp (plasma flow), vp (vascular plasma volume), PS (permeability surface-area product), ve (extravascular, extracellular volume), E (extraction ratio), ke (influx ratio into the interstitium), and tc (vascular transit time) from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast-enhanced (DSC) MRI were also generated. A region-of-interest analysis on the contralateral healthy white matter and on the tumor areas was performed and the extracted parameter values were tested for any significant differences among tumor grades or any correlations. Only f could be significantly correlated to DSC-derived vp and tc in healthy brain tissue. Concerning the tumor regions, Fp was significantly positively correlated with D* and inversely correlated with f in DSC measurements. The D*, f, and f × D* values in the WHO grade III gliomas were non-significantly different from those in the grade IV gliomas. There was a trend to significant negative correlations between f and PS as well as between f × D* and ke in DCE experiments. Presumably due to different theoretical background, tracer properties and modeling of the tumor vasculature in the IVIM theory, there is no clearly evident link between D*, f and DSC- and DCE-derived metrics.

Positioning of electronic subretinal implants in blind retinitis pigmentosa patients through multimodal assessment of retinal structures.

PURPOSE: To optimize methods for positioning subretinal visual implants, customizing their cable length, guiding them to the predetermined retinal position, and evaluating their performance. METHODS: Ten eyes of 10 patients (6 male, 4 female, mean age 46.4 years) were investigated before implantation of a subretinal visual implant. The structural characteristics of the retina as well as the ocular dimensions were determined. Topographic images of the prospective implantation site were subdivided into grids of squares. Each square received a weighted score for suitability. The sum of the scores was calculated, and the region with the highest score was chosen for the implant. In each case, the implant's power supply cable length was calculated by means of magnetic resonance imaging. The planned and achieved positions before and after implantation were compared. RESULTS: The mean light sensitivity ratio between the area actually covered by the chip and that of the planned position was 90.8% with an SD of 11.4%. In two cases with almost perfect positioning, the computed ratio was 100%. Measurements showed that to achieve a 95% sensitivity rate the difference between the planned and achieved chip position must be less than 1.7 mm. Preoperative calculations of the intraocular cable length proved accurate in all cases. CONCLUSIONS: Preoperative evaluation of retinal structures and eye morphology is useful for guiding a retinal implant to the designated area. It is a meaningful tool for planning and performing retinal chip implantation, and it optimizes personalized implantation. (ClinicalTrials.gov numbers, NCT00515814, NCT01024803.).