Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding.

Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.

On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol.

Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.

Evaluation of the Imaging Process for a Novel Subtraction Method Using Apparent Diffusion Coefficient Values.

Diffusion-weighted imaging may be used to obtain the apparent diffusion coefficient (ADC), which aids the diagnosis of cerebral infarction and tumors. An ADC reflects elements of free diffusion. Diffusion kurtosis imaging (DKI) has attracted attention as a restricted diffusion imaging technique. The ADC subtraction method (ASM) was developed to visualize restricted diffusion with high resolution by using two ADC maps taken with different diffusion times. We conducted the present study to provide a bridge between the reported basic ASM research and clinical research. We developed new imaging software for clinical use and evaluated its performance herein. This software performs the imaging process automatically and continuously at the pixel level, using ImageJ software. The new software uses a macro or a plugin which is compatible with various operating systems via a Java Virtual Machine. We tested the new imaging software's performance by using a Jurkat cell bio-phantom, and the statistical evaluation of the performance clarified that the ASM values of 99.98% of the pixels in the bio-phantom and physiological saline were calculated accurately (p<0.001). The new software may serve as a useful tool for future clinical applications and restricted diffusion imaging research.

Probing tissue microstructure by diffusion skewness tensor imaging.

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

Premio Sixto Obrador SENEC 2019: el uso de la secuencia tensor de difusión como herramienta pronóstica en los pacientes con traumatismo craneoencefálico grave y moderado. Parte I. Análisis de las características del tensor de difusión realizado durante la fase subaguda precoz / Sixto Obrador SENEC prize 2019: Utility of diffusion tensor imaging as a prognostic tool in moderate to severe traumatic brain injury. Part I. Analysis of DTI metrics performed during the early subacute stage

ANTECEDENTES Y OBJETIVOS: La lesión axonal traumática (LAT) contribuye significativamente a la mortalidad y morbilidad tras traumatismo craneoencefálico (TCE). Sin embargo, la caracterización de la LAT supone un reto diagnóstico para las técnicas de neuroimagen habitual. La secuencia de RM tensor de difusión (diffusion tensor imaging [DTI]) es capaz de detectar el grado de difusión de las moléculas de agua tisular y así inferir la afectación traumática de la sustancia blanca. El objetivo principal de este trabajo ha sido caracterizar la LAT a través de la secuencia DTI realizada en la fase subaguda precoz en nuestra serie de pacientes con TCE moderado y grave y evaluar si existe asociación con la evolución de los pacientes. MATERIALES Y MÉTODOS: Se ha realizado RM-DTI a 217 pacientes con TCE moderado y grave en la fase subaguda precoz tras el TCE (mediana = 19 días). El método de análisis elegido es por región de interés para calcular el valor medio de fractional anisotropy (FA) en 28 haces de sustancia blanca. Los valores obtenidos en los pacientes se han comparado con aquellos medidos en 58 sujetos sanos. RESULTADOS: Los resultados principales han sido que los pacientes, independientemente de la gravedad, demostraron valores de FA significativamente inferiores al grupo control en prácticamente todos los haces estudiados. Se detectó asociación entre el valor de FA y algunas variables clínicas de interés. Adicionalmente, los valores de FA de las tres porciones del cuerpo calloso, cíngulo y pedúnculos cerebrales se correlacionaron con la evolución del paciente evaluada a los 6 y 12 meses tras el TCE. CONCLUSIONES: El DTI es una herramienta útil para caracterizar la LAT y la detección de la reducción de FA en la fase subaguda precoz se relaciona con evolución desfavorable de los pacientes a medio y largo plazo

BACKGROUND AND OBJECTIVES: Traumatic axonal injury (TAI) contributes significantly to mortality and morbidity after traumatic brain injury (TBI). Its identification is still a diagnostic challenge because of the limitations of conventional imaging techniques to characterized it. Diffusion tensor imaging (DTI) can indirectly identify areas of damaged white matter integrity by detecting water molecule diffusion alterations. Our main objective is to characterize the TAI using DTI at the early subacute stage in our series of moderate to severe TBI patients and to evaluate if there is a relationship between the information provided by the DTI and patient's outcome. MATERIALS AND METHODS: We have obtained DTI data from 217 patients with moderate to severe TBI acquired at a median of 19 days after TBI, and patient DTI metrics were compared with data obtained from 58 age-matched healthy controls. Region of interest method was applied to obtain mean fractional anisotropy (FA) value in 28 white matter fiber bundles susceptible to TAI. RESULTS: Our main results were that when we compared patients with controls, patients, regardless of TBI severity, showed significantly reduced mean FA in almost all region of interest measured. We found statistically significant correlation between FA metrics and some clinical characteristics. Additionally, the FA values of the three portions of Corpus callosum, Cingulum and cerebral peduncles measured at the early subacute stage were highly associated with outcome assessed at hospital discharge and at 6 and 12 months after TBI. CONCLUSIONS: We conclude that DTI is a useful tool to characterize TAI and the detection of FA reduction in the subacute stage after TBI is associated with long-term unfavorable outcome

Usefulness of diffusion tensor imaging findings as biomarkers for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease. However, no reliable biomarkers have been identified to represent the clinical status. This study aimed to investigate whether diffusion tensor imaging (DTI) findings are useful imaging biomarkers to indicate the clinical status of ALS patients. Ninety-six probable or definite ALS cases and 47 age- and sex-matched, normal controls were enrolled. Demographic and clinical data were collected at the time of DTI. DTI data were acquired using a 3-Tesla magnetic resonance imaging scanner and analysed by voxel-wise statistical analyses for fractional anisotropy, axial diffusivity, radial diffusivity, mean diffusivity, and mode of anisotropy. Compared with the healthy control group, the ALS group had significant differences in DTI scalars in the diffuse tracts of the brain, which was predominant in the corticospinal tract at the brainstem and cerebellar peduncle area. Furthermore, the DTI values correlated with the ALS functional rating scale-revised (ALSFRS-R) scores and the delta ALSFRS-R score representing the rate of disease progression. The subgroup analysis revealed a more severe and widespread brain degeneration was observed in rapidly progressive ALS. Therefore, our results suggest that DTI findings are useful as imaging biomarkers for evaluating the clinical severity and rate of disease progression in ALS.

Test-retest reliability and sample size estimates after MRI scanner relocation.

OBJECTIVE: Many factors can contribute to the reliability and robustness of MRI-derived metrics. In this study, we assessed the reliability and reproducibility of three MRI modalities after an MRI scanner was relocated to a new hospital facility. METHODS: Twenty healthy volunteers (12 females, mean age (standard deviation) â€‹= â€‹41 (11) years, age range [25-66]) completed three MRI sessions. The first session (S1) was one week prior to the 3T GE HDxt scanner relocation. The second (S2) occurred nine weeks after S1 and at the new location; a third session (S3) was acquired 4 weeks after S2. At each session, we acquired structural T1-weighted, pseudo-continuous arterial spin labelled, and diffusion tensor imaging sequences. We used longitudinal processing streams to create 12 summary MRI metrics, including total gray matter (GM), cortical GM, subcortical GM, white matter (WM), and lateral ventricle volume; mean cortical thickness; total surface area; average gray matter perfusion, and average diffusion tensor metrics along principal white matter pathways. We compared mean MRI values and variance at the old scanner location to multiple sessions at the new location using Bayesian multi-level regression models. K-fold cross validation allowed identification of important predictors. Whole-brain analyses were used to investigate any regional differences. Furthermore, we calculated within-subject coefficient of variation (wsCV), intraclass correlation coefficient (ICC), and dice similarity index (SI) of cortical segmentations across scanner relocation and within-site. Additionally, we estimated sample sizes required to robustly detect a 4% difference between two groups across MRI metrics. RESULTS: All global MRI metrics exhibited little mean difference and small variability (bar cortical gray matter perfusion) both across scanner relocation and within-site repeat. T1- and DTI-derived tissue metrics showed â€‹< â€‹|0.3|% mean difference and <1.2% variance across scanner location and <|0.4|% mean difference and <0.8% variance within the new location, with between-site intraclass correlation coefficient (ICC) â€‹> â€‹0.80 and within-subject coefficient of variation (wsCV) â€‹< â€‹1.4%. Mean cortical gray matter perfusion had the highest between-session variability (6.7% [0.3, 16.7], estimate [95% uncertainty interval]), and hence the smallest ICC (0.71 [0.44,0.92]) and largest wsCV (13.4% [5.4, 18.1]). No global metric exhibited evidence of a meaningful mean difference between scanner locations. However, surface area showed evidence of a mean difference within-site repeat (between S2 and S3). Whole-brain analyses revealed no significant areas of difference between scanner relocation or within-site. For all metrics, we found no support for a systematic difference in variance across relocation sites compared to within-site test-retest reliability. Necessary sample sizes to detect a 4% difference between two independent groups varied from a maximum of n â€‹= â€‹362 per group (cortical gray matter perfusion), to total gray matter volume (n â€‹= â€‹114), average fractional anisotropy (n â€‹= â€‹23), total gray matter volume normalized by intracranial volume (n â€‹= â€‹19), and axial diffusivity (n â€‹= â€‹3 per group). CONCLUSION: Cortical gray matter perfusion was the most variable metric investigated (necessitating large sample sizes to identify group differences), with other metrics showing substantially less variability. Scanner relocation appeared to have a negligible effect on variability of the global MRI metrics tested. This manuscript reports within-site test-retest variability to act as a tool for calculating sample size in future investigations. Our results suggest that when all other parameters are held constant (e.g., sequence parameters and MRI processing), the effect of scanner relocation is indistinguishable from within-site variability, but may need to be considered depending on the question being investigated.

In vivo online monitoring of testosterone-induced neuroplasticity in a female songbird.

Adult neuroplasticity in the song control system of seasonal songbirds is largely driven by photoperiod-induced increases in testosterone. Prior studies of the relationships between testosterone, song performance and neuroplasticity used invasive techniques, which prevent analyzing the dynamic changes over time and often focus on pre-defined regions-of-interest instead of examining the entire brain. Here, we combined (i) in vivo diffusion tensor imaging (DTI) to assess structural neuroplasticity with (ii) repeated monitoring of song and (iii) measures of plasma testosterone concentrations in thirteen female photosensitive starlings (Sturnus vulgaris) who received a testosterone implant for 3 weeks. We observed fast (days) and slower (weeks) effects of testosterone on song behavior and structural neuroplasticity and determined how these effects correlate on a within-subject level, which suggested separate contributions of the song motor and anterior forebrain pathways in the development of song performance. Specifically, the increase in testosterone correlated with a rapid increase of song rate and RA volume, and with changes in Area X microstructure. After implant removal, these variables rapidly reverted to baseline levels. In contrast, the more gradual improvement of song quality was positively correlated with the fractional anisotropy values (DTI metric sensitive to white matter changes) of the HVC-RA tract and of the lamina mesopallialis, which contains fibers connecting the song control nuclei. Thus, we confirmed many of the previously reported testosterone-induced effects, like the increase in song control nuclei volume, but identified for the first time a more global picture of the spatio-temporal changes in brain plasticity.

Validation of mean upper cervical cord area (MUCCA) measurement techniques in multiple sclerosis (MS): High reproducibility and robustness to lesions, but large software and scanner effects.

INTRODUCTION: Atrophy of the spinal cord is known to occur in multiple sclerosis (MS). The mean upper cervical cord area (MUCCA) can be used to measure this atrophy. Currently, several (semi-)automated methods for MUCCA measurement exist, but validation in clinical magnetic resonance (MR) images is lacking. METHODS: Five methods to measure MUCCA (SCT-PropSeg, SCT-DeepSeg, NeuroQLab, Xinapse JIM and ITK-SNAP) were investigated in a predefined upper cervical cord region. First, within-scanner reproducibility and between-scanner robustness were assessed using intra-class correlation coefficient (ICC) and Dice's similarity index (SI) in scan-rescan 3DT1-weighted images (brain, including cervical spine using a head coil) performed on three 3 T MR machines (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) in 21 subjects with MS and 6 healthy controls (dataset A). Second, sensitivity of MUCCA measurement to lesions in the upper cervical cord was assessed with cervical 3D T1-weighted images (3 T GE HDxT using a head-neck-spine coil) in 7 subjects with MS without and 14 subjects with MS with cervical lesions (dataset B), using ICC and SI with manual reference segmentations. RESULTS: In dataset A, MUCCA differed between MR machines (p < 0.001) and methods (p < 0.001) used, but not between scan sessions. With respect to MUCCA values, Xinapse JIM showed the highest within-scanner reproducibility (ICC absolute agreement = 0.995) while Xinapse JIM and SCT-PropSeg showed the highest between-scanner robustness (ICC consistency = 0.981 and 0.976, respectively). Reproducibility of segmentations between scan sessions was highest in Xinapse JIM and SCT-PropSeg segmentations (median SI ≥ 0.921), with a significant main effect of method (p < 0.001), but not of MR machine or subject group. In dataset B, SI with manual outlines did not differ between patients with or without cervical lesions for any of the segmentation methods (p > 0.176). However, there was an effect of method for both volumetric and voxel wise agreement of the segmentations (both p < 0.001). Highest volumetric and voxel wise agreement was obtained with Xinapse JIM (ICC absolute agreement = 0.940 and median SI = 0.962). CONCLUSION: Although MUCCA is highly reproducible within a scanner for each individual measurement method, MUCCA differs between scanners and between methods. Cervical cord lesions do not affect MUCCA measurement performance.

Development of anisotropic phantoms using wood and fiber materials for diffusion tensor imaging and diffusion kurtosis imaging.

OBJECTIVE: Several studies have demonstrated that anisotropic phantoms can be utilized for diffusion magnetic resonance imaging. The purpose of our study was to examine whether wood is suitable as an anisotropic phantom material from the viewpoints of affordability and availability. In the current study, wood was used for restricted diffusion, and fibers were used for hindered diffusion. MATERIALS AND METHODS: Wood and fiber phantoms were made. Diffusion kurtosis images were acquired with three magnetic resonance scanners. Fractional anisotropy, radial diffusivity, axial diffusivity, radial kurtosis and axial kurtosis values were measured. The wood phantom was imaged, and its durability was confirmed. The phantoms were imaged in varying orientations within the magnetic field. The wood was observed using an optical microscope. RESULTS: Ten kinds of wood and the fiber had a diffusion metrics. The wood diffusion metrics suggested low variation over a period of 9 months. Changing the orientation of the phantoms within the magnetic field resulted in changes in diffusion metrics. Observation of wood vessels and fibers was conducted. DISCUSSION: Wood and fibers have anisotropy and are promising as phantom materials. The development of anisotropic phantoms that anyone can use is useful for diffusion magnetic resonance imaging research and clinical applications.

Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems.

Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/µm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.

Investigation of anisotropic fishing line-based phantom as tool in quality control of diffusion tensor imaging.

This work proposes a low-cost, fishing line-based phantom for quality control of diffusion tensor imaging (DTI). The device was applied to investigate the relationship between DTI indexes (DTIi) and imaging acquisition parameters. A Dyneema® fishing line phantom was built with fiber bundles of different thicknesses. DTI acquisitions were performed in a 3T magnetic resonance imaging scanner using an 8-channel and a 32-channel head coil. For each coil, the following acquisition parameters were changed, one at a time: diffusion sensitivity factor (b value), echo time, sensitivity encoding, voxel size, number of signal averages, and number of diffusion gradient directions (NDGD). DTIi including fractional anisotropy, relative anisotropy (RA), linear anisotropy (CL), and planar anisotropy (CP) were calculated for each image; the data were analyzed using the coefficient of variation (CV) and distributions of DTIi values. The 32-channel head coil presented higher CV values for the DTIi RA, CL, and CP when voxel size was changed. Using the phantom, dependences between diffusion-related parameters (b value and NDGD) and DTIi were also observed; the majority of these were for the smaller thickness fiber bundles. The device proved to be useful for the verification of the DTI performance over time.

Evaluation of select biocompatible markers for labelling peripheral nerves on 11.7 T MRI.

BACKGROUND: Peripheral nerve injury is often followed by a highly variable recovery process with respect to both rapidity and efficacy. Identifying post-nerve injury phenomena is key to assessing the merit and timing of surgery as well as to tracking nerve recovery postoperatively. Diffusion Tensor Imaging (DTI) has been investigated in the clinical and research settings as a noninvasive technique to both assess and monitor each patient's unique case of peripheral nerve damage. NEW METHOD: We identify a MRI-suitable marker for tracking the exact site of either nerve injury or coaptation following surgical repair to aid with DTI analysis. RESULTS: Due to artefact and disruption of tractography, silver wire and microvascular clips were not suitable markers. AxoGuard®, 4-0 vicryl suture, and 10-0 polyamide suture, although detectable, did not produce a signal easily distinguished from post-surgical changes. Silicone was easily identifiable and stable in both the acute and delayed time points, exhibited negligible impact on DTI parameters, and possessed geometry to prevent nerve strangulation. COMPARISON WITH EXISTING METHOD: Prior studies have not assessed the efficacy of other markers nor have they assessed silicone for potential artefact with DTI parameter analysis. Furthermore, this work demonstrates the reliability and compatibility of silicone in the delayed postoperative time period and includes its unique imaging appearance on high-resolution 11.7 MRI. CONCLUSION: Semi-cylindrical silicone tubing can be used as a safe, reliable, and readily available radiological marker to visualize and monitor a region of interest on a rodent's peripheral nerve for aiding assessments with diffusion tensor imaging.

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes.

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.

Anatomía microquirúrgica en 3D del tracto corticoespinal y de la vía del lemnisco basada en microdisección de fibras y demostración a través de tractografía / 3D microsurgical anatomy of the cortico-spinal tract and lemniscal pathway based on fiber microdissection and demonstration with tractography

Objetivo: Realizar un estudio anatómico de microdisección de fibras y radiológico mediante tractografía basada en tensor de difusión (DTT) para demostrar tridimensionalmente el tracto corticoespinal y el lemnisco medial. Material y métodos: Bajo visión microscópica y con el uso de instrumental microquirúrgico se disecaron y estudiaron 10 hemisferios cerebrales y 15 troncoencéfalos humanos a través de la técnica de microdisección de fibras. Se obtuvieron imágenes de resonancia magnética cerebrales de 15 sujetos sanos, empleando secuencias potenciadas en difusión para el trazado y reproducción mediante DTT del tracto corticoespinal y la vía del lemnisco. Resultados: Se demostraron y describieron anatómicamente el tracto corticoespinal y lemnisco medial en gran parte de sus trayectorias, identificando las relaciones entre sí y con otros haces de sustancia blanca y núcleos de sustancia gris cercanos, especialmente en el troncoencéfalo, con la correspondiente representación mediante DTT. Conclusiones: Mediante la técnica de microdisección se apreció la disposición, arquitectura y organización topográfica general del tracto corticoespinal y lemnisco medial. Este conocimiento ha aportado una perspectiva anatómica única y profunda que ha favorecido la representación y la correcta interpretación de las imágenes de DTT. Esta información debe ser trasladada a la práctica clínica para favorecer el análisis crítico y exhaustivo por parte del cirujano ante posibles lesiones localizadas en el interior del troncoencéfalo y, en consecuencia, mejorar la indicación y planificación quirúrgica, incluyendo la selección preoperatoria de estrategias óptimas y posibles zonas de abordajes a su interior, alcanzando una técnica microquirúrgica más segura y precisa

Objective: To demonstrate tridimensionally the anatomy of the cortico-spinal tract and the medial lemniscus, based on fiber microdissection and diffusion tensor tractography (DTT). Material and methods: Ten brain hemispheres and brain-stem human specimens were dissected and studied under the operating microscope with microsurgical instruments by applying the fiber microdissection technique. Brain magnetic resonance imaging was obtained from 15 healthy subjects using diffusion-weighted images, in order to reproduce the cortico-spinal tract and the lemniscal pathway on DTT images. Results: The main bundles of the cortico-spinal tract and medial lemniscus were demonstrated and delineated throughout most of their trajectories, noticing their gross anatomical relation to one another and with other white matter tracts and gray matter nuclei the surround them, specially in the brain-stem; together with their corresponding representation on DTT images. Conclusions: Using the fiber microdissection technique we were able to distinguish the disposition, architecture and general topography of the cortico-spinal tract and medial lemniscus. This knowledge has provided a unique and profound anatomical perspective, supporting the correct representation and interpretation of DTT images. This information should be incorporated in the clinical scenario in order to assist surgeons in the detailed and critic analysis of lesions located inside the brain-stem, and therefore, improve the surgical indications and planning, including the preoperative selection of optimal surgical strategies and possible corridors to enter the brainstem, to achieve safer and more precise microsurgical technique

Analysis of variability of fractional anisotropy values at 3T using a novel diffusion tensor imaging phantom.

We employed a novel diffusion tensor imaging phantom to study intra- and interscanner reproducibility on two 3T magnetic resonance (MR) scanners. Using a phantom containing thousands of hollow micron-size tubes in complex arrays, we performed two experiments using a b value of 1000 s/ms2 on two Siemens 3T Trio scanners. First, we performed 12-direction scans. Second, on one scanner, we performed two 64-direction protocols with different repetition times (TRs). We used a one-way analysis of variance to calculate differences between scanners and the Mann-Whitney U test to assess differences between 12-direction and 64-direction data. We calculated the coefficient of variation (CoV) for intrascanner and interscanner data. For 12-direction protocols, mean fractional anisotropy (FA) was 0.3003 for Scanner 1 (four scans) and 0.3094 for Scanner 2 (three scans). Lowest FA value on Scanner 1 was 2.56 standard deviations below the mean of Scanner 2. For 64-direction scans, mean FA was 0.2640 for 4000 ms TR and 0.2582 for 13,200 ms TR scans. For 12-direction scans, within-scanner CoV was 0.0326 for Scanner 1 and 0.0240 for Scanner 2; between-scanner CoV was 0.032. For 64-direction scans, CoV was 0.056 for TR 4000 ms and 0.0533 for TR 13,200 ms. The difference between median FA values of 12-direction and 64-direction scans was statistically significant ( p < 0.001). We found relatively good reproducibility on any single MR scanner. FA values from one scanner were sometimes significantly below the mean FA of another scanner, which has important implications for clinical use of DTI.

Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial.

PURPOSE: To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial. METHODS: DTI metrics (including longitudinal diffusivity [LD], fractional anisotropy [FA], mean diffusivity [MD], and transverse diffusivity [TD]) from pyramidal tracts for healthy controls were calculated from images acquired on twenty-seven 3T MR scanners (Siemens and GE) with 6 different scanner models and 7 different software versions as part of the NN102/SPRINT-MS clinical trial. Each volunteer underwent two scanning sessions on the same scanner. Signal-to-noise ratio (SNR) and signal-to-noise floor ratio (SNFR) were also assessed. RESULTS: DTI metrics showed good scan-rescan repeatability. There were no significant differences between scans and rescans in LD, FA, MD, or TD values. Although the cross-scanner coefficient of variation (CV) values for all DTI metrics were <5.7%, significant differences were observed for LD (p < 3.3e-5) and FA (p < 0.0024) when GE scanners were compared with Siemens scanners. Significant differences were also observed for SNR when comparing GE scanners and Siemens Skyra scanners (p < 1.4e-7) and when comparing Siemens Skyra scanners and TIM Trio scanners (p < 1.0e-10). Analysis of background signal also demonstrated differences between GE and Siemens scanners in terms of signal statistics. The measured signal intensity from a background noise region of interest was significantly higher for GE scanners than for Siemens scanners (p < 1.2e-12). Significant differences were also observed for SNFR when comparing GE scanners and Siemens Skyra scanners (p < 2.5e-11), GE scanners and Siemens Trio scanners (p < 7.5e-11), and Siemens Skyra scanners and TIM Trio scanners (p < 2.5e-9). CONCLUSIONS: The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models.

Magnetic Resonance Imaging in Huntington's Disease.

Magnetic resonance imaging (MRI) is a noninvasive technique used routinely to image the body in both clinical and research settings. Through the manipulation of radio waves and static field gradients, MRI uses the principle of nuclear magnetic resonance to produce images with high spatial resolution, appropriate for the investigation of brain structure and function.

Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner.

OBJECTIVES: Our goal was to investigate the correlation between histopathology and diffusion parameters by utilising the most repeatable region-of-interest (ROI) strategy for diffusion parameters in rectal cancer on a 3T scanner. METHODS: 113 patients underwent DKI-MR and 66 of these patients received surgery without neoadjuvant chemoradiotherapy. Two readers independently measured the parameters using three slice protocols including single slice, three slices and whole-tumour slice (WTS), combined with one of two ROIs, including outline and round ROI. ANOVA, Kruskal-Wallis, a paired sample t-test, interclass correlation coefficient (ICC), Bland-Altman, Student's t-tests, receiver operating characteristic curves and z statistic were used for statistical analysis. RESULTS: There were no significant differences among the three slice protocols in ADC values (p = 0.822, 0.987), K values (p = 0.842, 0.859) and D values (p = 0.917, 0.988) using round and outline ROI, respectively. The ADC and D values derived from outline ROIs were higher than those from round ROIs (all p < 0.001 for ADC, all p < 0.001 for D), while K values derived from outline ROIs were lower than those from round ROIs (p < 0.001, p = 0.001, p < 0.001) using three slice protocols, respectively. The WTS-outline ROI resulted in the best intra- and inter-observer ICC. Utilising the WTS-outline ROI method, the AUC for assessment of well-differentiated tumours was 0.871 by K and 0.809 by ADC; and the AUC for T2 was 0.768 by K. CONCLUSIONS: The most repeatable strategy was the WTS-outline ROI method. In addition to DWI, DKI also have diagnostic value for rectal cancer histopathological characteristics utilising the WTS-outline ROI on a 3T scanner. KEY POINTS: • DKI using a 3T scanner is feasible for assessing rectal cancer. • ROI and slice protocol show considerable influence on DKI parameters. • DKI parameters exhibit excellent repeatability using whole-tumour slice-outline ROI on 3T scanner. • DKI has considerable diagnostic value for the estimation of rectal cancer characteristics.