Sebastiano Barbieri

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion‐weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up‐to‐date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM‐sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521–540.

Impact of the calculation algorithm on biexponential fitting of diffusion-weighted MRI in upper abdominal organs.

To compare the variability, precision, and accuracy of six different algorithms (Levenberg–Marquardt, Trust‐Region, Fixed‐Dp, Segmented‐Unconstrained, Segmented‐Constrained, and Bayesian‐Probability) for computing intravoxel‐incoherent‐motion‐related parameters in upper abdominal organs.

Boundary estimation of fiber bundles derived from diffusion tensor images

PurposeDiffusion tensor imaging (DTI) is a non-invasive imaging technique that allows estimating the location of white matter tracts based on the measurement of water diffusion properties. Using DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgical interventions. In this paper, a novel approach for boundary estimation is presented.MethodsDTI in combination with diverse segmentation algorithms allows estimating the position and course of fiber tracts in the human brain. For additional information about the expansion of the fiber bundle, the introduced iterative approach uses the centerline of a tracked fiber bundle between two regions of interest (ROI). After sampling along this centerline, rays are sent out radially, discrete 2D contours are calculated, and the fiber bundle boundary is estimated in a stepwise manner. For this purpose, each ray is analyzed using several criteria, including anisotropy parameters and angle parameters, to find the boundary point.ResultsThe novel method for automatically calculating the boundaries has been applied to several artificially generated DTI datasets. Multiple parameters were varied: number of rays per plane, sampling rate and sampled points along the rays. For the DTI data used in the experiments, the method yielded a dice similarity coefficient (DSC) between 74.7 and 91.5%.ConclusionsIn this paper, a novel approach to retrieve significant information about the fiber bundle boundary from DTI data is presented. The method is a contribution to gather important knowledge about high-risk structures in neurosurgical interventions.

A Fast and Robust Graph-Based Approach for Boundary Estimation of Fiber Bundles Relying on Fractional Anisotropy Maps

In this paper, a fast and robust graph-based approach for boundary estimation of fiber bundles derived from Diffusion Tensor Imaging (DTI) is presented. DTI is a non-invasive imaging technique that allows the estimation of the location of white matter tracts based on measurements of water diffusion properties. Depending on DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgery. DTI in combination with tracking algorithms allows the estimation of position and course of fiber tracts in the human brain. The presented method uses these tracking results as the starting point for a graph-based approach. The overall method starts by computing the fiber bundle centerline between two user-defined regions of interests (ROIs). This centerline determines the planes that are used for creating a directed graph. Then, the mincut of the graph is calculated, creating an optimal boundary of the fiber bundle.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle. The presented segmentation algorithm utilizes information given by the estimated main diffusion direction in a voxel and the respective uncertainty, and its validity is confirmed by both qualitative and quantitative analyses.

Comparison of Intravoxel Incoherent Motion Parameters across MR Imagers and Field Strengths: Evaluation in Upper Abdominal Organs

Purpose To determine the reproducibility of intravoxel incoherent motion (IVIM) parameters measured in upper abdominal organs with magnetic resonance (MR) imagers from different vendors and with different field strengths. Materials and Methods This prospective study was approved by the independent ethics committees of Kanton Bern and Kanton Zurich, and signed informed consent was obtained from all participants. Abdominal diffusion-weighted images in 10 healthy men (mean age, 37 years ± 8 [standard deviation]) were acquired by using 1.5- and 3.0-T MR imagers from three different vendors. Two readers independently delineated regions of interest that were used to measure IVIM parameters (diffusion coefficient [Dt], perfusion fraction [Fp], and pseudodiffusion coefficient [Dp]) in the left and right lobes of the liver, and in the pancreas, spleen, renal cortex, and renal medulla. Measurement reproducibility between readers was assessed with intraclass correlation coefficients (ICCs). Variability across MR imagers was analyzed by using between- and within-subject coefficients of variation (CVs) and analysis of variance (ANOVA). Results Between-reader reproducibility was high for Dt (ICC, 94.6%), intermediate for Fp (ICC, 81.7%), and low for Dp (ICC, 69.5%). Between- and within-subject CVs of Dt were relatively high (>20%) in the left lobe of the liver and relatively low (<10%) in the renal cortex and renal medulla. CVs generally exceeded 15% for Fp values and 20% for Dp. ANOVA indicated significant differences (P < .05) between MR imagers. Conclusion IVIM parameters in the upper abdomen may differ substantially across MR imagers. (©) RSNA, 2015 Online supplemental material is available for this article.

DTI segmentation via the combined analysis of connectivity maps and tensor distances

We describe a novel approach to extract the neural tracts of interest from a diffusion tensor image (DTI). Compared to standard streamline tractography, existing probabilistic methods are able to capture fiber paths that deviate from the main tensor diffusion directions. At the same time, tensor clustering methods are able to more precisely delimit the border of the bundle. To the best of our knowledge, we propose the first algorithm which combines the advantages supplied by probabilistic and tensor clustering approaches. The algorithm includes a post-processing step to limit partial-volume related segmentation errors. We extensively test the accuracy of our algorithm on different configurations of a DTI software phantom for which we systematically vary the image noise, the number of gradients, the geometry of the fiber paths and the angle between adjacent and crossing fiber bundles. The reproducibility of the algorithm is supported by the segmentation of the corticospinal tract of nine patients. Additional segmentations of the corticospinal tract, the arcuate fasciculus, and the optic radiations are in accordance with anatomical knowledge. The required user interaction is comparable to that of streamline tractography, which allows for an uncomplicated integration of the algorithm into the clinical routine.

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

ObjectivesTo differentiate prostate cancer lesions with high and with low Gleason score by diffusion-weighted-MRI (DW-MRI).MethodsThis prospective study was approved by the responsible ethics committee. DW-MRI of 84 consenting prostate and/or bladder cancer patients scheduled for radical prostatectomy were acquired and used to compute apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM: the pure diffusion coefficient Dt, the pseudo-diffusion fraction Fp and the pseudo-diffusion coefficient Dp), and high b value (as acquired and Hessian filtered) parameters within the index lesion. These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on whether whole-prostate histopathological analysis after prostatectomy determined a high (≥7) or low (6) Gleason score.ResultsMean ADC and Dt differed significantly (p of independent two-sample t test < 0.01) between high- and low-grade lesions. The highest classification accuracy was achieved by the mean ADC (AUC 0.74) and Dt (AUC 0.70). A logistic regression model based on mean ADC, mean Fp and mean high b value image led to an AUC of 0.74 following leave-one-out cross-validation.ConclusionsClassification by IVIM parameters was not superior to classification by ADC. DW-MRI parameters correlated with Gleason score but did not provide sufficient information to classify individual patients.Key Points• Mean ADC and diffusion coefficient differ between high- and low-grade prostatic lesions.• Accuracy of trivariate logistic regression is not superior to using ADC alone.• DW-MRI is not a valid substitute for biopsies in clinical routine yet.

High signal intensity in dentate nucleus and globus pallidus on unenhanced T1-weighted MR images in three patients with impaired renal function and vascular calcification.

Gadolinium‐based contrast agents (primarily those with linear chelates) are associated with a dose‐dependent signal hyperintensity in the dentate nucleus and the globus pallidus on unenhanced T1‐weighted MRI following administration to selected patients with normal renal function. The accumulation of gadolinium has also been reported in the skin, heart, liver, lung, and kidney of patients with impaired renal function suffering from nephrogenic systemic fibrosis (NSF). Here we report on three patients with impaired renal function and vascular calcification (two with confirmed NSF) whose unenhanced T1‐weighted MRIs showed conspicuous high signal intensity in the dentate nucleus and the globus pallidus after they had been exposed to relatively low doses of linear gadolinium‐based contrast agents (0.27, 0.45, and 0.68 mmol/kg). Signal ratios between dentate nucleus and pons and between globus pallidus and thalamus were comparable with previously reported measurements in subjects without renal impairment. Of note, all three analysed patients suffered from transient signs of neurological disorders of undetermined cause. In conclusion, the exposure to 0.27‐0.68 mmol/kg of linear gadolinium‐based contrast agent was associated with probable gadolinium accumulation in the brain of three patients suffering from impaired renal function and vascular calcification.

Qualitative and quantitative analysis of probabilistic and deterministic fiber tracking

Fiber tracking (FT) and quantification algorithms are approximations of reality due to limited spatial resolution, model assumptions, user-defined parameter settings, and physical imaging artifacts resulting from diffusion sequences. Until now, correctness, plausibility, and reliability of both FT and quantification techniques have mainly been verified using histologic knowledge and software or hardware phantoms. Probabilistic FT approaches aim at visualizing the uncertainty present in the data by incorporating models of the acquisition process and noise. The uncertainty is assessed by tracking many possible paths originating from a single seed point, thereby taking the tensor uncertainty into account. Based on the tracked paths, maps of connectivity probabilities can be produced, which may be used to delineate risk structures for presurgical planning. In this paper, we explore the advantages and disadvantages of probabilistic approaches compared to deterministic algorithms and give both qualitative and quantitative comparisons based on clinical data. We focus on two important clinical applications, namely, on the reconstruction of fiber bundles within the proximity of tumors and on the quantitative analysis of diffusion parameters along fiber bundles. Our results show that probabilistic FT is superior and suitable for a better reconstruction at the borders of anatomical structures and is significantly more sensitive than the deterministic approach for quantification purposes. Furthermore, we demonstrate that an alternative tracking approach, called variational noise tracking, is qualitatively comparable with a standard probabilistic method, but is computationally less expensive, thus, enhancing its appeal for clinical applications.