Optimised diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain

Diffusion-weighted Imaging of the Chest: A Primer for Radiologists

Diffusion-weighted imaging (DWI) is a fundamental sequence not only in neuroimaging but also in oncologic imaging and has emerging applications for MRI evaluation of the chest. DWI can be used in clinical practice to enhance lesion conspicuity, tissue characterization, and treatment response. While the spatial resolution of DWI is in the order of millimeters, changes in diffusion can be measured on the micrometer scale. As such, DWI sequences can provide important functional information to MRI evaluation of the chest but require careful optimization of acquisition parameters, notably selection of b values, application of parallel imaging, fat saturation, and motion correction techniques. Along with assessment of morphologic and other functional features, evaluation of DWI signal attenuation and apparent diffusion coefficient maps can aid in tissue characterization. DWI is a noninvasive noncontrast acquisition with an inherent quantitative nature and excellent reproducibility. The outstanding contrast-to-noise ratio provided by DWI can be used to improve detection of pulmonary, mediastinal, and pleural lesions, to identify the benign nature of complex cysts, to characterize the solid portions of cystic lesions, and to classify chest lesions as benign or malignant. DWI has several advantages over fluorine 18 (18F)-fluorodeoxyglucose PET/CT in the assessment, TNM staging, and treatment monitoring of lung cancer and other thoracic neoplasms with conventional or more recently developed therapies. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

Correlation between diffusion tensor indices and fascicular morphometric parameters of peripheral nerve

Introduction: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that measures the anisotropy of water diffusion. Clinical magnetic resonance imaging scanners enable visualization of the structural integrity of larger axonal bundles in the central nervous system and smaller structures like peripheral nerves; however, their resolution for the depiction of nerve fascicular morphology is limited. Accordingly, high-field strength MRI and strong magnetic field gradients are needed to depict the fascicular pattern. The study aimed to quantify diffusion tensor indices with high-field strength MRI within different anatomical compartments of the median nerve and determine if they correlate with nerve structure at the fascicular level. Methods: Three-dimensional pulsed gradient spin-echo (PGSE) imaging sequence in 19 different gradient directions and b value 1,150 s/mm² was performed on a 9.4T wide-bore vertical superconducting magnet. Nine-millimeter-long segments of five median nerve samples were obtained from fresh cadavers and acquired in sixteen 0.625 mm thick slices. Each nerve sample had the fascicles, perineurium, and interfascicular epineurium segmented. The diffusion tensor was calculated from the region-average diffusion-weighted signals for all diffusion gradient directions. Subsequently, correlations between diffusion tensor indices of segmentations and nerve structure at the fascicular level (number of fascicles, fascicular ratio, and cross-sectional area of fascicles or nerve) were assessed. The acquired diffusion tensor imaging data was employed for display with trajectories and diffusion ellipsoids. Results: The nerve fascicles proved to be the most anisotropic nerve compartment with fractional anisotropy 0.44 ± 0.05. In the interfascicular epineurium, the diffusion was more prominent in orthogonal directions with fractional anisotropy 0.13 ± 0.02. Diffusion tensor indices within the fascicles and perineurium differed significantly between the subjects (p < 0.0001); however, there were no differences within the interfascicular epineurium (p ≥ 0.37). There were no correlations between diffusion tensor indices and nerve structure at the fascicular level (p ≥ 0.29). Conclusion: High-field strength MRI enabled the depiction of the anisotropic diffusion within the fascicles and perineurium. Diffusion tensor indices of the peripheral nerve did not correlate with nerve structure at the fascicular level. Future studies should investigate the relationship between diffusion tensor indices at the fascicular level and axon- and myelin-related parameters.

Evaluating diffusion dispersion across an extended range of b-values and frequencies: Exploiting gap-filled OGSE shapes, strong gradients, and spiral readouts

Purpose

To address the long echo times and relatively weak diffusion sensitization that typically limit oscillating gradient spin‐echo (OGSE) experiments, an OGSE implementation combining spiral readouts, gap‐filled oscillating gradient shapes providing stronger diffusion encoding, and a high‐performance gradient system is developed here and utilized to investigate the tradeoff between b‐value and maximum OGSE frequency in measurements of diffusion dispersion (i.e., the frequency dependence of diffusivity) in the in vivo human brain. In addition, to assess the effects of the marginal flow sensitivity introduced by these OGSE waveforms, flow‐compensated variants are devised for experimental comparison.

Methods

Using DTI sequences, OGSE acquisitions were performed on three volunteers at b‐values of 300, 500, and 1000 s/mm² and frequencies up to 125, 100, and 75 Hz, respectively; scans were performed for gap‐filled oscillating gradient shapes with and without flow sensitivity. Pulsed gradient spin‐echo DTI acquisitions were also performed at each b‐value. Upon reconstruction, mean diffusivity (MD) maps and maps of the diffusion dispersion rate were computed.

Results

The power law diffusion dispersion model was found to fit best to MD measurements acquired at b = 1000 s/mm² despite the associated reduction of the spectral range; this observation was consistent with Monte Carlo simulations. Furthermore, diffusion dispersion rates without flow sensitivity were slightly higher than flow‐sensitive measurements.

Conclusion

The presented OGSE implementation provided an improved depiction of diffusion dispersion and demonstrated the advantages of measuring dispersion at higher b‐values rather than higher frequencies within the regimes employed in this study.

Mapping gradient nonlinearity and miscalibration using diffusion-weighted MR images of a uniform isotropic phantom

PURPOSE

To use diffusion measurements to map the spatial dependence of the magnetic field produced by the gradient coils of an MRI scanner with sufficient accuracy to correct errors in quantitative diffusion MRI (DMRI) caused by gradient nonlinearity and gradient amplifier miscalibration.

THEORY AND METHODS

The field produced by the gradient coils is expanded in regular solid harmonics. The expansion coefficients are found by fitting a model to a minimum set of diffusion-weighted images of an isotropic diffusion phantom. The accuracy of the resulting gradient coil field maps is evaluated by using them to compute corrected b-matrices that are then used to process a multi-shell diffusion tensor imaging (DTI) dataset with 32 diffusion directions per shell.

RESULTS

The method substantially reduces both the spatial inhomogeneity of the computed mean diffusivities (MD) and the computed values of the fractional anisotropy (FA), as well as virtually eliminating any artifactual directional bias in the tensor field secondary to gradient nonlinearity. When a small scaling miscalibration was purposely introduced in the x, y, and z, the method accurately detected the amount of miscalibration on each gradient axis.

CONCLUSION

The method presented detects and corrects the effects of gradient nonlinearity and gradient gain miscalibration using a simple isotropic diffusion phantom. The correction would improve the accuracy of DMRI measurements in the brain and other organs for both DTI and higher order diffusion analysis. In particular, it would allow calibration of MRI systems, improving data harmony in multicenter studies.

Data-driven separation of MRI signal components for tissue characterization

PURPOSE

MRI 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSION

The 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.

Diffusion MRI in Peripheral Nerves: Optimized b Values and the Role of Non-Gaussian Diffusion

Background Diffusion-weighted imaging (DWI) provides specific in vivo information about tissue microstructure, which is increasingly recognized for various applications outside the central nervous system. However, standard sequence parameters are commonly adopted from optimized central nervous system protocols, thus potentially neglecting differences in tissue-specific diffusional behavior. Purpose To characterize the optimal tissue-specific diffusion imaging weighting scheme over the b domain in peripheral nerves under physiologic and pathologic conditions. Materials and Methods In this prospective cross-sectional study, 3-T MR neurography of the sciatic nerve was performed in healthy volunteers (n = 16) and participants with type 2 diabetes (n = 12). For DWI, 16 b values in the range of 0-1500 sec/mm² were acquired in axial and radial diffusion directions of the nerve. With a region of interest-based approach, diffusion-weighted signal behavior as a function of b was estimated using standard monoexponential, biexponential, and kurtosis fitting. Goodness of fit was assessed to determine the optimal b value for two-point DWI/diffusion tensor imaging (DTI). Results Non-Gaussian diffusional behavior was observed beyond b values of 600 sec/mm² in the axial and 800 sec/mm² in the radial diffusion direction in both participants with diabetes and healthy volunteers. Accordingly, the biexponential and kurtosis models achieved a better curve fit compared with the standard monoexponential model (Akaike information criterion >99.9% in all models), but the kurtosis model was preferred in the majority of cases. Significant differences between healthy volunteers and participants with diabetes were found in the kurtosis-derived parameters D_k and K. The results suggest an upper bound b value of approximately 700 sec/mm² for optimal standard DWI/DTI in peripheral nerve applications. Conclusion In MR neurography, an ideal standard diffusion-weighted imaging/diffusion tensor imaging protocol with b = 700 sec/mm² is suggested. This is substantially lower than in the central nervous system due to early-occurring non-Gaussian diffusion behavior and emphasizes the need for tissue-specific b value optimization. Including higher b values, kurtosis-derived parameters may represent promising novel imaging markers of peripheral nerve disease. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Jang and Du in this issue.

Diffusion MR Imaging with T2-based Water Suppression (T2wsup-dMRI)

Purpose: This study proposes and assesses a new diffusion MRI (dMRI) technique to solve problems related to the quantification of parameter maps (apparent diffusion coefficient [ADC] or mean diffusivity [MD], fractional anisotropy [FA]) and misdrawing of fiber tractography (FT) due to cerebrospinal fluid (CSF)-partial volume effects (PVEs) for brain tissues by combining with the T2-based water suppression (T2wsup) technique.

Methods: T2wsup–diffusion-weighted imaging (DWI) images were obtained by subtracting those images from the acquired multi-b value (b) DWI images after correcting the signal intensities of multiecho time (TE) images using long TE water signal-dominant images. Quantitative parameter maps and FT were obtained from minimum data points and were compared with those using the standard (without wsup) DWI method, and partly compared with those obtained using other alternative DWI methods of applying fluid attenuation inversion recovery (FLAIR), non-b-zero (NBZ) by theoretical or noise-added simulation and MR images.

Results: In the T2wsup-dMRI method, the hyperintense artifacts due to CSF-PVEs in MRI data were dramatically suppressed even at lower b (≲ 500 s/mm²) while keeping the tissue SNR. The quantitative parameter map values became precisely close to the pure tissue values precisely even in water (CSF) PVE voxels in healthy brain tissues (T2 ≲ 100 ms). Furthermore, the fiber tracts were correctly connected, particularly at the fornix in closest contact to the CSF.

Conclusion: Solving the problem of CSF-PVE in the current dMRI technique using our proposed T2wsup-dMRI technique is easy, with higher SNR than those obtained with FLAIR or NBZ methods when applying to healthy brain tissues. The proposed T2wsup–dMRI could be useful in clinical settings, although further optimization of the pulse sequence and processing techniques and clinical assessments are required, particularly for long T2 lesions.

Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation

Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter μFA imaging were a maximum b-value of 2000 s/mm² and a ratio of STE to LTE tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct regression approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct regression approach showed strong linear correlations with the gamma model (ρ = 0.97 and ρ = 0.90) but mean biases of -0.11 and - 0.02 relative to the gamma model were also observed, respectively. All three μFA measurements showed good test-retest reliability (ρ ≥ 0.79 and bias = 0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution μFA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-min scan. Accordingly, our results introduce an optimization procedure that has enabled nearly full brain μFA in only several minutes.

Apparent diffusion coefficient measurement using thin-slice diffusion-weighted magnetic resonance imaging: assessment of measurement errors and repeatability

We investigated the measurement error and repeatability of the apparent diffusion coefficient (ADC) obtained using thin-slice imaging. Diffusion-weighted images of an ice-water phantom were acquired using 1.5-T and 3.0-T scanners with 1-, 3-, and 5-mm thickness. ADC maps were generated at b = 0 and 1000 mm²/s using five consecutive scans. Measurement errors were assessed with accuracy and precision. Repeatability was assessed using the within-subject coefficient of variation. The ADC accuracy of both scanners agreed with the ADC of water at 0 °C. At 1-mm, precisions were 2.9% and 8.4% for the 3.0-T and 1.5-T scanners, respectively. The repeatabilities of 1-mm thickness were 1.3% and 3.4% in the 3.0-T and 1.5-T scanners, respectively. The 3.0-T scanner showed acceptable measurement errors and moderate repeatability compared with Quantitative Imaging Biomarkers Alliance recommendation. A 3.0-T scanner can be used for reliable ADC measurement, even with a 1-mm thickness at a reasonable scan time.

An optimal acquisition and post-processing pipeline for hybrid IVIM-DKI in head and neck

Purpose

To optimize the diffusion‐weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region.

Methods

Optimized diffusion‐weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér‐Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose.

The effects of (1) b value optimization and number of b values, (2) registration type (none vs. intervolume vs. intra‐ and intervolume registration), and (3) manual swallowing artifact rejection on the parameter precision were assessed.

Results

The SD was higher in the reference set for perfusion fraction, diffusion coefficient, and kurtosis by a factor of 1.7, 1.5, and 2.3 compared to the optimized set, respectively. A smaller SD (factor 0.7) was seen in pseudodiffusion coefficient. The sets containing 15, 20, and 30 b values had comparable repeatability in all parameters, except pseudodiffusion coefficient, for which set size 30 was worse. Equal repeatability for the registration approaches was seen in all parameters of interest. Swallowing artifact rejection removed the bias when present.

Conclusion

To achieve optimal hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region, b value optimization and swallowing artifact image rejection are beneficial. The optimized set of 15 b values yielded the optimal protocol efficiency, with a precision comparable to larger b value sets and a 50% reduction in scan time.

Determination of optimized set of b-values for Apparent Diffusion Coefficient mapping in liver Diffusion-Weighted MRI

In this manuscript we derive the Cramér-Rao Lower Bound (CRLB) of the monoexponential diffusion-weighted signal model under a realistic noise assumption, and propose a formulation to obtain optimized sets of b-values that maximize the noise performance of the Apparent Diffusion Coefficient (ADC) maps given a target ADC and a signal-to-noise ratio. Therefore, for various sets of parameters (S₀ and ADC), signal-to-noise ratios (SNR) and noise distribution, we computed optimized sets of b-values using CRLB-based analysis in two different ways: (i) through a greedy algorithm where b-values from a pool of candidates were added iteratively to the set, and (ii) through a two b-value search algorithm were all two b-value combinations of the pool of candidates were tested. Further, optimized sets of b-values were computed from synthetic data, phantoms, and in-vivo liver diffusion-weighted imaging (DWI) experiments to validate the CRLB-based analysis. The optimized sets of b-values obtained through the proposed CRLB-based analysis showed good agreement with the optimized sets obtained experimentally from synthetic, phantoms, and in-vivo liver data. The variance of the ADC maps decreased when employing the optimized set of b-values compared to various sets of b-values proposed in the literature for in-vivo liver DWI, although differences of notable magnitude between noise models and optimization strategies were not found. In addition, the higher b-values decreased for lower SNR under the Rician noise distribution. Optimization of the set b-values is critical to maximize the noise performance (i.e., maximize the precision and minimize the variance) of the estimated ADC maps in diffusion-weighted MRI. Hence, the proposed approach may help to optimize and standardize liver diffusion-weighted MRI acquisitions by computing optimized set of b-values for a given set of parameters.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Diffusion dispersion imaging: Mapping oscillating gradient spin-echo frequency dependence in the human brain

PURPOSE

Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency.

THEORY AND METHODS

Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power-law frequency dependence of ADC, the "diffusion dispersion." Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion.

RESULTS

For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high-quality, full-brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min.

CONCLUSIONS

This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full-brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling.

Effect of intravoxel incoherent motion on diffusion parameters in normal brain

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ ​= ​0.35, p ​= ​3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

Optimization of b-value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting

PURPOSE

Intravoxel incoherent motion (IVIM) modeling for estimation of the diffusion coefficient (D) and perfusion fraction (f) is increasingly popular, but no consensus on standard protocols exists. This study provides a framework for optimization of b-value schemes for reduced estimation uncertainty of D and f from segmented model fitting.

THEORY

Analytical expressions for uncertainties of D and f from segmented model fitting were derived as Cramer-Rao lower bounds (CRLBs).

METHODS

Optimized b-value schemes were obtained for 3 to 12 acquisitions and in the limit of infinitely many acquisitions through constrained minimization of the CRLBs, with b-values constrained to be 0 or 200 to 800 s/mm² . The optimized b-value scheme with eight acquisitions was compared with b-values linearly distributed in the allowed range using simulations and in vivo liver data from seven healthy volunteers.

RESULTS

All optimized b-value schemes contained exactly three unique b-values regardless of the total number of acquisitions (0, 200, and 800 s/mm² ) with repeated acquisitions distributed approximately as 1:2:2. Compared with linearly distributed b-values, the variability of estimates of D and f was reduced by approximately 30% as seen both in simulations and in repeated in vivo measurements.

CONCLUSION

The uncertainty of IVIM D and f estimates can be reduced by the use of optimized b-value schemes.

Utility of Multi-Parametric Quantitative Magnetic Resonance Imaging for Characterization and Radiotherapy Response Assessment in Soft-Tissue Sarcomas and Correlation With Histopathology

Purpose: To evaluate repeatability of quantitative multi-parametric MRI in retroperitoneal sarcomas, assess parameter changes with radiotherapy, and correlate pre-operative values with histopathological findings in the surgical specimens. Materials and Methods: Thirty patients with retroperitoneal sarcoma were imaged at baseline, of whom 27 also underwent a second baseline examination for repeatability assessment. 14/30 patients were treated with pre-operative radiotherapy and were imaged again after completing radiotherapy (50.4 Gy in 28 daily fractions, over 5.5 weeks). The following parameter estimates were assessed in the whole tumor volume at baseline and following radiotherapy: apparent diffusion coefficient (ADC), parameters of the intra-voxel incoherent motion model of diffusion-weighted MRI (D, f, D*), transverse relaxation rate, fat fraction, and enhancing fraction after gadolinium-based contrast injection. Correlation was evaluated between pre-operative quantitative parameters and histopathological assessments of cellularity and fat fraction in post-surgical specimens (ClinicalTrials.gov, registration number NCT01902667). Results: Upper and lower 95% limits of agreement were 7.1 and -6.6%, respectively for median ADC at baseline. Median ADC increased significantly post-radiotherapy. Pre-operative ADC and D were negatively correlated with cellularity (r = -0.42, p = 0.01, 95% confidence interval (CI) -0.22 to -0.59 for ADC; r = -0.45, p = 0.005, 95% CI -0.25 to -0.62 for D), and fat fraction from Dixon MRI showed strong correlation with histopathological assessment of fat fraction (r = 0.79, p = 10-7, 95% CI 0.69-0.86). Conclusion: Fat fraction on MRI corresponded to fat content on histology and therefore contributes to lesion characterization. Measurement repeatability was excellent for ADC; this parameter increased significantly post-radiotherapy even in disease categorized as stable by size criteria, and corresponded to cellularity on histology. ADC can be utilized for characterizing and assessing response in heterogeneous retroperitoneal sarcomas.

Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances

This review discusses ex vivo diffusion magnetic resonance imaging (dMRI) as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain. We review the challenges posed by the properties of post-mortem tissue, and discuss state-of-the-art tissue preparation methods and recent advances in pulse sequences and acquisition techniques to tackle these. We then review recent ex vivo dMRI studies of the human brain, highlighting the validation of white matter orientation estimates and the atlasing and mapping of large subcortical structures. We also give particular emphasis to the delineation of layered gray matter structure with ex vivo dMRI, as this application illustrates the strength of its mesoscale resolution over large fields of view. We end with a discussion and outlook on future and potential directions of the field.

Direct comparison between apparent diffusion coefficient and macromolecular proton fraction as quantitative biomarkers of the human fetal brain maturation

BACKGROUND

Apparent diffusion coefficient (ADC) is known as a quantitative biomarker of prenatal brain maturation. Fast macromolecular proton fraction (MPF) mapping is an emerging method for quantitative assessment of myelination that was recently adapted to fetal MRI.

PURPOSE

To compare the capability of ADC and MPF to quantify the normal fetal brain development.

STUDY TYPE

Prospective.

POPULATION

Forty-two human fetuses in utero (gestational age [GA] = 27.7 ± 6.0, range 20-38 weeks).

FIELD STRENGTH/SEQUENCE

1.5 T; diffusion-weighted single-shot echo-planar spin-echo with five b-values for ADC mapping; spoiled multishot echo-planar gradient-echo with T₁ , proton density, and magnetization transfer contrast weightings for single-point MPF mapping.

ASSESSMENT

Two operators measured ADC and MPF in the medulla, pons, cerebellum, thalamus, and frontal, occipital, and temporal cerebral white matter (WM).

STATISTICAL TESTS

Mixed repeated-measures analysis of variance (ANOVA) with the factors of pregnancy trimester and brain structure; Pearson correlation coefficient (r); Hotelling-Williams test to compare strengths of correlations.

RESULTS

From the 2^nd to 3^rd trimester, ADC significantly decreased in the thalamus and cerebellum (P < 0.005). MPF significantly increased in the medulla, pons, thalamus, and cerebellum (P < 0.005). Cerebral WM had significantly higher ADC and lower MPF compared with the medulla and pons in both trimesters. MPF (r range 0.83, 0.89, P < 0.001) and ADC (r range -0.43, -0.75, P ≤ 0.004) significantly correlated with GA and each other (r range -0.32, -0.60, P ≤ 0.04) in the medulla, pons, thalamus, and cerebellum. No significant correlations or distinctions between regions and trimesters were observed for cerebral WM (P range 0.1-0.75). Correlations with GA were significantly stronger for MPF compared with ADC in the medulla, pons, and cerebellum (Hotelling-Williams test, P < 0.003) and similar in the thalamus. Structure-averaged MPF and ADC values strongly correlated (r = 0.95, P < 0.001).

DATA CONCLUSION

MPF and ADC demonstrated qualitatively similar but quantitatively different spatiotemporal patterns. MPF appeared more sensitive to changes in the brain structures with prenatal onset of myelination.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:52-61.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

White matter structural integrity and transcranial Doppler blood flow pulsatility in normal aging

Cerebrovascular diseases underlie many forms of age-related cognitive impairment and the mechanism linking the two is hypothesized to involve adverse changes in white matter (WM) integrity. Despite being systemic, small vessel disease does not uniformly affect WM. We performed voxel-wise analysis of MRI images to examine the association between fractional anisotropy (FA) - a diffusion tensor measure of WM structural integrity - and pulsatility index (PI) - a transcranial Doppler ultrasound measure of abnormal arterial flow - in adults over the age of 70years who were free of stroke and dementia. We demonstrate that the relation of PI to microstructural changes in WM is artery specific and regional. We identified spatial clusters of significant correlations between elevated PI and reduced FA which cannot be explained by aging, supporting a vascular hypothesis of WM injury. These areas are not limited to the vascular territories of the vessels where PI is assessed, suggesting that the linkage between PI and FA is not likely a function of perfusion per se, but is consistent with injury caused by mechanical wave emanating from pulsating vessel walls.

Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives

For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology.

KEY POINTS

• Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.

Measuring Cerebral Hypoperfusion Induced by Hyperventilation Challenge With Intravoxel Incoherent Motion Magnetic Resonance Imaging in Healthy Volunteers

OBJECTIVES

The aim of this study was to demonstrate the feasibility to assess cerebral hypoperfusion with a hyperventilation (HV) challenge protocol using intravoxel incoherent motion (IVIM) magnetic resonance imaging.

MATERIALS AND METHODS

Magnetic resonance imaging experiments were performed on 10 healthy volunteers at 1.5 T, with a diffusion IVIM magnetic resonance imaging protocol using a set of b-values optimized by Cramer-Rao Lower Bound analysis. Hypoperfusion was induced by an HV maneuver. Measurements were performed in normoventilation and HV conditions. Biexponential curve fitting was used to obtain the perfusion fraction (f), pseudodiffusion coefficient (D*), and the product fD* in gray matter (GM) regions of interest (ROIs). Regional cerebral blood flow in the same ROIs was also assessed with arterial spin labeling.

RESULTS

The HV challenge led to a diminution of IVIM perfusion-related parameters, with a decrease of f and fD* in the cerebellum (P = 0.03 for f; P = 0.01 for fD*), thalamus GM (P = 0.09 for f; P = 0.01 for fD*), and lenticular nuclei (P = 0.03 for f; P = 0.02 for fD*). Mean GM cerebral blood flow (in mL/100 g tissue/min) measured with arterial spin labeling averaged over all ROIs also decreased (normoventilation: 42.7 ± 4.1 vs HV: 33.2 ± 2.2, P = 0.004) during the HV challenge.

CONCLUSIONS

The optimized IVIM protocol proposed in the current study allows for measurements of cerebral hypoperfusion that might be of great interest for pathologies diagnosis such as ischemic stroke.

White Matter Correlates of Auditory Comprehension Outcomes in Chronic Post-Stroke Aphasia

Neuroimaging studies have shown that speech comprehension involves a number of widely distributed regions within the frontal and temporal lobes. We aimed to examine the differential contributions of white matter connectivity to auditory word and sentence comprehension in chronic post-stroke aphasia. Structural and diffusion MRI data were acquired on 40 patients with chronic post-stroke aphasia. A battery of auditory word and sentence comprehension tests were administered to all the patients. Tract-based spatial statistics were used to identify areas in which white matter integrity related to specific comprehension deficits. Relevant tracts were reconstructed using probabilistic tractography in healthy older participants, and the mean values of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of the entire tracts were examined in relation to comprehension scores. Anterior temporal white matter integrity loss and involvement of the uncinate fasciculus related to word-level comprehension deficits (R_FA = 0.408, P = 0.012; R_MD = −0.429, P = 0.008; R_AD = −0.424, P = 0.009; R_RD = −0.439, P = 0.007). Posterior temporal white matter integrity loss and involvement of the inferior longitudinal fasciculus related to sentence-level comprehension deficits (R_FA = 0.382, P = 0.02; R_MD = −0.461, P = 0.004; R_AD = −0.457, P = 0.004; R_RD = −0.453, P = 0.005). Loss of white matter integrity in the inferior fronto-occipital fasciculus related to both word- and sentence-level comprehension (word-level scores: R_FA = 0.41, P = 0.012; R_MD = −0.447, P = 0.006; R_AD = −0.489, P = 0.002; R_RD = −0.432, P = 0.008; sentence-level scores: R_FA = 0.409, P = 0.012; R_MD = −0.413, P = 0.011; R_AD = −0.408, P = 0.012; R_RD = −0.413, P = 0.011). Lesion overlap, but not white matter integrity, in the arcuate fasciculus related to sentence-level comprehension deficits. These findings suggest that word-level comprehension outcomes in chronic post-stroke aphasia rely primarily on anterior temporal lobe pathways, whereas sentence-level comprehension relies on more widespread pathways including the posterior temporal lobe.

Optimal Experiment Design for Monoexponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging

The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range of b-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.

Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians

The development of magnetic resonance imaging (MRI) for use in medical investigation has provided a huge forward leap in the field of diagnosis, particularly with avoidance of exposure to potentially dangerous ionizing radiation. With decreasing costs and better availability, the use of MRI is becoming ever more pervasive throughout clinical practice. Understanding the principles underlying this imaging modality and its multiple applications can be used to appreciate the benefits and limitations of its use, further informing clinical decision-making. In this article, the principles of MRI are reviewed, with further discussion of specific clinical applications such as parallel, diffusion-weighted, and magnetization transfer imaging. MR spectroscopy is also considered, with an overview of key metabolites and how they may be interpreted. Finally, a brief view on how the use of MRI will change over the coming years is presented.

Diffusion-weighted imaging of prostate cancer: effect of b-value distribution on repeatability and cancer characterization

PURPOSE

To evaluate the effect of b-value distribution on the repeatability and Gleason score (GS) prediction of prostate cancer (PCa).

METHODS

Fifty PCa patients underwent two repeated 3T diffusion-weighted imaging (DWI) examinations using 12 b values in the range from 0 to 2000s/mm(2) and diffusion time of 20.3ms. Mean signal intensities of regions of interest, placed in PCa using whole mount prostatectomy sections as the reference, were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. In total, 4083 different b-value combinations consisting of 2 to 12 b values were evaluated. Repeatability was assessed by intraclass correlation coefficient, ICC(3,1), and coefficient of repeatability (CoR). Areas under receiver operating characteristic curve (AUCs) for PCa characterization were estimated while the correlation of the fitted values with GS groups (3+3, 3+4, >3+4) was evaluated by using the Spearman correlation coefficient (ρ).

RESULTS

The parameters of monoexponential, kurtosis, and stretched exponential models estimated using only 4-5, 5-7, 5-7 b values, respectively, had similar ICC(3,1), CoR, AUC, and ρ values as the parameters estimated using all 12 b values. Optimized b-value distributions demonstrated improved ICC(3,1) and CoR values but failed to improve AUC and ρ values. The parameters of biexponential model demonstrated the worst repeatability and diagnostic performance.

CONCLUSION

B-value distribution influences mainly the repeatability of DWI-derived parameters rather than the diagnostic performance.

Diffusion weighted magnetic resonance imaging and its recent trend-a survey

Since its inception in 1985, diffusion weighted magnetic resonance imaging has been evolving and is becoming instrumental in diagnosis and investigation of tissue functions in various organs including brain, cartilage, and liver. Even though brain related pathology and/or investigation remains as the main application, diffusion weighted magnetic resonance imaging (DWI) is becoming a standard in oncology and in several other applications. This review article provides a brief introduction of diffusion weighted magnetic resonance imaging, challenges involved and recent advancements.

Between-Scanner and Between-Visit Variation in Normal White Matter Apparent Diffusion Coefficient Values in the Setting of a Multi-Center Clinical Trial

PURPOSE

To study the between-scanner variation and the between-visit reproducibility of brain apparent diffusion coefficient (ADC) measurements in the setting of a multi-center chemotherapy clinical trial for glioblastoma multiforme.

METHODS AND MATERIALS

ADC maps of 52 patients at six sites were calculated in-house from diffusion-weighted images obtained by seven individual scanner models of two vendors. The median and coefficient of variation (CV) of normal brain white matter ADC values from a defined region of interest were used to evaluate the differences among scanner models, vendors, magnetic fields, as well as successive visits. All patients participating in this study signed institutional review board approved informed consent. Data acquisition was performed in compliance with all applicable Health Insurance Portability and Accountability Act regulations. The study spanned from August 1, 2006, to January 29, 2008.

RESULTS

For baseline median ADC, no difference was observed between the different scanner models, different vendors, and different magnetic field strength. For baseline ADC CV, a significant difference was found between different scanner models (p = 0.0002). No between-scanner difference was observed in ADC changes between two visits. For between-visit reproducibility, significant difference was seen between the ADC values measured at two successive visits for the whole patient group.

CONCLUSION

The CVs varied significantly between scanners, presumably due to image noise. Consistent scanner parameter setup can improve reproducibility of the ADC measurements between visits.

Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations

Glucose hypometabolism and gray matter atrophy are well known consequences of Alzheimer's disease (AD). Studies using these measures have shown that the earliest clinical stages, in which memory impairment is a relatively isolated feature, are associated with degeneration in an apparently remote group of areas—mesial temporal lobe (MTL), diencephalic structures such as anterior thalamus and mammillary bodies, and posterior cingulate. These sites are thought to be strongly anatomically inter-connected via a limbic-diencephalic network. Diffusion tensor imaging or DTI—an imaging technique capable of probing white matter tissue microstructure—has recently confirmed degeneration of the white matter connections of the limbic-diencephalic network in AD by way of an unbiased analysis strategy known as tract-based spatial statistics (TBSS). The present review contextualizes the relevance of these findings, in which the fornix is likely to play a fundamental role in linking MTL and diencephalon. An interesting by-product of this work has been in showing that alterations in diffusion behavior are complex in AD—while early studies tended to focus on fractional anisotropy, recent work has highlighted that this measure is not the most sensitive to early changes. Finally, this review will discuss in detail several technical aspects of DTI both in terms of image acquisition and TBSS analysis as both of these factors have important implications to ensure reliable observations are made that inform understanding of neurodegenerative diseases.

Diffusion-weighted magnetic resonance imaging of the pancreas: diagnostic benefit from an intravoxel incoherent motion model-based 3 b-value analysis

OBJECTIVES

The objective of this study was to evaluate the diagnostic benefit of an intravoxel incoherent motion (IVIM) model-based characterization of pancreatic masses from diffusion-weighted imaging (DWI) with 3 b values.

MATERIALS AND METHODS

This retrospective study had an approval from the institutional review board, and informed patient consent was waived. The 1.5-T DWI data of 42 patients with or without pancreatic disease, acquired by a respiratory-gated spin-echo echo-planar imaging sequence with 3 b values (0, 50, 800 s/mm²), were retrospectively analyzed. The IVIM-related parameters D', which is the apparent diffusion coefficient [ADC(50,800)], and f', as well as ADC(0,50), and conventional ADC(0,800) were calculated voxelwise. Regions of interest were analyzed in pancreatic adenocarcinomas (CAs, n = 12), neuroendocrine pancreatic tumors (NETs, n = 9), and chronic pancreatitis (CPs, n = 11), not affected tissue of each pathologic group, and in the head, body, and tail of the healthy pancreas (n = 10).

RESULTS

By ADC(0,800) and D', CAs could hardly be distinguished from neuroendocrine pancreatic tumors and chronic pancreatitis. However, CAs revealed very low ADC(0,50) and f' values, which differed significantly from all other groups. In the healthy pancreas, ADC(0,800) and D' values were significantly higher for the head than for the body and tail, but no significant differences were found for ADC(0,50) and f'.

CONCLUSIONS

The determination of IVIM-based microcirculation-sensitive parameter maps from DWI with 3 b values significantly improved the discrimination of CAs from NETs, CPs, and the healthy tissue.

Diffusion-weighted imaging of the liver: techniques and applications

Diffusion-weighted imaging (DWI) is a technique that assesses the cellularity, tortuosity of the extracellular/extravascular space, and cell membrane density based on differences in water proton mobility in tissues. The strength of the diffusion weighting is reflected by the b value. DWI using several b values enables the quantification of the apparent diffusion coefficient. DWI is increasingly used in liver imaging for multiple reasons: it can add useful qualitative and quantitative information to conventional imaging sequences; it is acquired relatively quickly; it is easily incorporated into existing clinical protocols; and it is a noncontrast technique.

Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm(2): simulation and repeatability study

PURPOSE

To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations.

METHODS

Simulations aiming to minimize estimation accuracy error were performed. Ten PCa patients underwent in total four repeated 3-tesla DWI examinations using 12 equally spaced b values (0-2000 s/mm(2) ). Normalized mean signal intensities of regions-of-interest placed in normal tissue and PCa were fitted. In total, 210 different b-value combinations consisting of six b values, 0 and 100 s/mm(2) included in every b-value distribution, were evaluated in terms of accuracy and repeatability.

RESULTS

The simulations and in vivo DWI data suggest the optimal b-value distribution for the monoexponential model consists of four to five equally distributed b values in the range of 0 to 1200 s/mm(2) . The parameters of the stretched exponential and kurtosis models are best estimated using five to seven b values in the ranges of 300 to 700 and close to 2000 s/mm(2) , in addition to low b value. B-value distribution consisting of eight to 10 b values in the ranges of 0 to 100, 800 to 1200, and 1800 to 2000 s/mm(2) is the preferred method for estimation of the biexponential model parameters of PCa DWI.

CONCLUSION

The optimized b-value distributions demonstrated improved estimation accuracy and repeatability of DWI signal decay-derived parameters.

Quantitative evaluation of computed high B value diffusion-weighted magnetic resonance imaging of the prostate

OBJECTIVES

Computed diffusion-weighted magnetic resonance imaging (cDWI) refers to the synthesizing of arbitrary b value diffusion-weighted images (DWI) from a set of measured b value images by voxelwise fitting. The objectives of this study were to quantitatively analyze the noise and the contrast-to-noise ratio (CNR) in cDWI as a function of b value by numerical simulations and by measurements in patients with prostate cancer and to compare cDWI to directly measured DWI at a b value of 1400 s/mm2.

MATERIALS AND METHODS

Numerical simulations were performed to assess image noise and CNR in both cDWI and regular DWI. Forty-two patients with prostate cancer (age, 51-73 years; prostate specific antigen level, 0.5-30 ng/mL; and biopsy Gleason score, 6-9) received 2 DWI examinations at 3.0 T (one with b values of 100, 500, and 1400 s/mm2 and another with b values of 0, 100, 400, and 800 s/mm2) to create cDWI images at arbitrary b values, both with and without incorporating a b value of 0 s/mm2 in their calculation. Regions of interest were drawn to compare the scan time adjusted CNR (CNR eff) between cDWI and directly measured DWI at b = 1400 s/mm2 on tumor-suspicious lesions and normal-appearing regions.

RESULTS

In the numerical simulations, noise depended strongly on the b value, the diffusion coefficient, and the signal intensity at a b value of 0 s/mm2 in cDWI but not in regular DWI. The CNR between simulated tumor and normal regions showed a continuous increase with increasing b value. Both these findings were also observed in tumor-suspicious and normal-appearing regions in in vivo data. In vivo prostate DWI at a b value of 1400 s/mm2 showed a similar CNR eff between the tumor-suspicious regions and the normal-appearing tissue in cDWI as in the directly measured DWI (P = 0.395).

CONCLUSIONS

The CNR eff between tumor-suspicious and normal-appearing prostate tissue in DWI images at a b value of 1400 s/mm2 is comparable in cDWI and directly measured DWI. Computed DWI at even higher b values, calculated from measured images with b values between 0 and 800 s/mm2, yields higher CNR eff than measured DWI, which may be of clinical aid in the management of prostate cancer.

Efficient and precise calculation of the b-matrix elements in diffusion-weighted imaging pulse sequences

Precise NMR diffusion measurements require detailed knowledge of the cumulative dephasing effect caused by the numerous gradient pulses present in most NMR pulse sequences. This effect, which ultimately manifests itself as the diffusion-related NMR signal attenuation, is usually described by the b-value or the b-matrix in the case of multidirectional diffusion weighting, the latter being common in diffusion-weighted NMR imaging. Neglecting some of the gradient pulses introduces an error in the calculated diffusion coefficient reaching in some cases 100% of the expected value. Therefore, ensuring the b-matrix calculation includes all the known gradient pulses leads to significant error reduction. Calculation of the b-matrix for simple gradient waveforms is rather straightforward, yet it grows cumbersome when complexly shaped and/or numerous gradient pulses are introduced. Making three broad assumptions about the gradient pulse arrangement in a sequence results in an efficient framework for calculation of b-matrices as well providing some insight into optimal gradient pulse placement. The framework allows accounting for the diffusion-sensitising effect of complexly shaped gradient waveforms with modest computational time and power. This is achieved by using the b-matrix elements of the simple unmodified pulse sequence and minimising the integration of the complexly shaped gradient waveform in the modified sequence. Such re-evaluation of the b-matrix elements retains all the analytical relevance of the straightforward approach, yet at least halves the amount of symbolic integration required. The application of the framework is demonstrated with the evaluation of the expression describing the diffusion-sensitizing effect, caused by different bipolar gradient pulse modules.

Proton diffusion tensor spectroscopy of metabolites in human muscle in vivo

PURPOSE

To study the apparent diffusivity and its directionality for metabolites of skeletal muscle in humans in vivo by (1) H magnetic resonance spectroscopy.

METHODS

The diffusion tensors were determined on a 3 Tesla MR system using optimized acquisition and processing methods including an adapted STEAM sequence with orientation-dependent diffusion weighting, pulse-triggering with individually adapted delays, eddy-current correction schemes, median filtering, and simultaneous prior-knowledge fitting of all related spectra.

RESULTS

The average apparent diffusivities, as well as the fractional anisotropies of taurine (ADCav=0.74 × 10(-3) s/mm(2) , FA=0.46), creatine (ADCav =0.41 × 10(-3) s/mm(2) , FA=0.33), trimethylammonium compounds (ADCav =0.48 × 10(-3) s/mm(2) , FA=0.34), carnosine (ADCav =0.46 × 10(-3) s/mm(2) , FA=0.47), and water (ADCav=1.5 × 10(-3) s/mm(2) , FA=0.36) were estimated. The diffusivities of most metabolites and water were significantly different from each other. Diffusion was found to be anisotropic and the diffusion tensors showed tensor correlation coefficients close to 1 and were hence found to be essentially coaligned. The magnitudes of apparent metabolite diffusivities were largely ordered according to molecular weight, with taurine as the smallest molecule diffusing fastest, both along and across the fiber direction.

CONCLUSION

Diffusivities, directional dependence of diffusion and fractional anisotropies of (1) H MRS-visible muscle metabolites were presented. It was shown that metabolites share diffusion directionality with water and have similar fractional anisotropies, hinting at similar diffusion barriers.

High-resolution reduced field of view diffusion tensor imaging using spatially selective RF pulses

PURPOSE

Diffusion tensor imaging (DTI) plays a vital role in identifying white matter fiber bundles. Achievable imaging resolution and imaging time demands remain the major challenges in detecting small fiber bundles with current clinical DTI sequences.

METHODS

A novel reduced field of view ultra-high-resolution DTI technique named eZOOM (elliptically refocused zonally oblique multislice) was developed. A small circular disk was imaged using spatially selective radiofrequency (RF) pulses, reducing the imaging matrix size. The frequency profile of the spectral-spatial refocusing RF pulse provided intrinsic fat suppression, eliminating the need for fat saturation pulses.

RESULTS

Multislice DTI at a resolution of 0.35 × 0.35 mm in a celery fiber phantom was successfully performed by scanning an 8-cm field of view at 3T. An adequate diffusion-to-noise ratio (DNR >20) was achieved for a 25-min acquisition using a direct-sampling RF receiver. Human subjects (n = 7) were scanned at resolutions of 0.47 × 0.47 mm having a DNR <20 within a 75-min scanning time, requiring further enhancements to increase the signal-to-noise ratio.

CONCLUSIONS

The new eZOOM-DTI method offers multislice DTI at ultra-high imaging resolutions substantially exceeding those available with current echo-planar DTI techniques. Parallel and fast spin echo methods can be combined with eZOOM to improve SNR and DNR in humans.

Comparison of PET-CT and magnetic resonance diffusion weighted imaging with body suppression (DWIBS) for initial staging of malignant lymphomas

OBJECTIVE

To evaluate the clinical impact of diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) in staging of malignant lymphoma.

METHODS

Twenty-three patients with proven malignant lymphomas were prospectively enrolled. DWIBS (b=0, 1000 s/mm(2)) examinations and PET-CT were performed respectively on an Intera 1.5 T unit and a Gyroscan PET-CT scan (Philips Medical system, Best, the Netherland). The criteria for positive node involvement were a size over 10 mm or an apparent diffusion coefficient (ADC) value under 0.7510(-3) mm(2)/s for nodes under 10mm. For extranodal analysis, a high or heterogeneous signal on DWIBS was considered as positive. In cases of discordance, the reference standard for each region or organ was established at 6 months after the diagnosis according to all available clinical, biological information, as well as histological evidence or follow-up to prove or disprove the presence of disease.

RESULTS

DWIBS and PET-CT results were congruent in 333 node regions on the 345 areas analyzed, with excellent agreement (κ=0.97, P<0.0001). From 433 organs analyzed (one patient had splenectomy) extranodal disease was detected in 22 organs on DWIBS. The two imaging techniques agreed on 430 organs (κ=0.99, P<0.0001). Finally, Ann Arbor stages based on DWIBS and those of PET/CT were in agreement for 23 patients.

CONCLUSIONS

For malignant lymphoma in a pre-therapeutic context, agreement between diffusion-weighted whole-body imaging and PET/CT is high for Ann Arbor staging.

Intravoxel incoherent motion model-based liver lesion characterisation from three b-value diffusion-weighted MRI

OBJECTIVE

To evaluate intravoxel incoherent motion (IVIM) model-based liver lesion characterisation from three b-value diffusion-weighted imaging (DWI).

METHODS

The 1.5-T DWI data from a respiratory gated spin-echo echo-planar magnetic resonance imaging sequence (b = 0, 50, 800 s/mm(2)) were retrospectively analysed in 38 patients with different liver lesions. Conventional apparent diffusion coefficient ADC = ADC(0,800) as well as IVIM-based parameters D' = ADC(50,800), ADC_low = ADC(0,50), and f' were calculated voxel-wise. Sixty-one regions of interest in hepatocellular carcinomas (HCCs, n = 24), haemangiomas (HEMs, n = 11), focal nodular hyperplasias (FNHs, n = 11), and healthy liver tissue (REFs, n = 15) were analysed. Group differences were investigated using Student's t-test and receiver-operating characteristic (ROC) analysis.

RESULTS

Mean values ± standard deviations of ADC, D', ADC_low (in 10(-5) mm(2)/s), and f' (in %) for REFs/FNHs/HEMs/HCCs were 130 ± 11/143 ± 27/168 ± 16/113 ± 25, 104 ± 12/123 ± 25/162 ± 18/102 ± 23, 518 ± 66/437 ± 97/268 ± 69/283 ± 120, and 18 ± 3/14 ± 4/6 ± 3/9 ± 5, respectively. Differences between lesions and REFs were more significant for IVIM-based parameters than for conventional ADC. ROC analysis showed the best discriminability between HCCs and FNHs for ADC_low and f' and between HEMs and FNHs or HCCs for D'.

CONCLUSION

Three instead of two b-value DWI enables a numerically stable and voxel-wise IVIM-based analysis for improved liver lesion characterisation with tolerable acquisition time.

KEY POINTS

• Quantitative analysis of diffusion-weighted MRI helps liver lesion characterisation. • Analysis of intravoxel incoherent motion is superior to apparent diffusion coefficient determination. • Only three b-values enable separation of diffusion and microcirculation effects. • The method presented is numerically stable, with voxel-wise results and short acquisition times.

Effect of B-value in revealing postinfarct myocardial microstructural remodeling using MR diffusion tensor imaging

Nonmonoexponential diffusion behavior has been previously reported to exist in some biological tissues, making quantification of diffusion tensor imaging (DTI) indices dependent on diffusion sensitivity of b-value. This study aims to investigate the effect of b-value in revealing postinfarct myocardial microstructural remodeling in ex vivo hearts. DTI scans were performed on heart samples 1, 3, 5, and 7 days after infarction induction as well as intact controls with b-values of 500 to 2500s/mm(2). DTI indices, including fractional anisotropy (FA), and mean and directional diffusivities, were measured in infarct, adjacent and remote regions with zero and each non-zero b-values respectively using conventional DTI analysis. Experimental results showed that these DTI indices decreased gradually with b-values in all regions and groups. Optimal b-values were found to vary with targeted DTI indices, and could strengthen DTI ability in revealing myocardium degradation with using conventional DTI approach. Specifically, FA showed the most sensitive detection of fiber integrity degradation at moderate b-values (≈1500 to 2000s/mm(2)), and the greatest ability of mean and directional diffusivities in monitoring diffusivity alteration occurred at relatively small b-values (≤1500s/mm(2)) during the necrotic and fibrotic phases. These findings may provide useful information for DTI protocol parameter optimization in assessing heart microstructures at other pathological or in vivo states in the future.

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.

In vivo investigation of restricted diffusion in the human brain with optimized oscillating diffusion gradient encoding

PURPOSE

Previous studies in phantoms and animals using animal MR systems have shown promising results in using oscillating gradient spin echo (OGSE) diffusion acquisition to depict microstructure information. The OGSE approach has also been shown to be a sensitive biomarker of tumor treatment response and white matter-related diseases. Translating these studies to a human MR scanner faces multiple challenges due to the much weaker gradient system. The goals of this study are to optimize the OGSE acquisition for a human MR system and investigate its applicability in the in vivo human brain.

METHODS

An analytical analysis of the OGSE modulation spectrum was provided. Based on this analysis and thorough simulation experiments, the OGSE acquisition was optimized in terms of diffusion waveform shape, waveform timing, and sequence timing-to achieve higher diffusion sensitivity and better sampling of the diffusion spectrum.

RESULTS

The trapezoid-cosine waveform was found to be the optimal OGSE waveform. At the three employed peak encoding frequencies of 18 Hz, 44 Hz, and 63 Hz, the waveform polarity for the least blurry sampling of the diffusion spectrum was 90+/180-, 90+/180+, and 90+/180+, respectively. For the highest diffusion-to-noise ratio at 63 Hz, the b-value was 200 s/mm(2) and the echo time was 116 ms. Using the optimized sequence, a frequency dependence of the measured apparent diffusion coefficients was observed in white matter-dominant regions such as the corpus callosum.

CONCLUSION

The obtained results demonstrate, for the first time, the potential of using an OGSE acquisition for investigating microstructure information on a human MR system.

Optimizing the functional diffusion map using Monte Carlo simulations

PURPOSE

To optimize the diagnostic accuracy of the functional diffusion map for monitoring tumor treatment response in cancer patients.

MATERIALS AND METHODS

Using Monte Carlo simulations, measurement precision of the apparent diffusion coefficient (ADC), and particularly accuracy of threshold determination from healthy reference tissue, are evaluated by investigating the repeatability limit of the ADC as a function of different degrees of diffusion weighting of the sequence. Phantom and in-vivo experiments are performed to verify and illustrate the results of the simulations.

RESULTS

While diagnostic accuracy of the functional diffusion map is hardly diminished by differing values of the T(2) relaxation time in tumor and reference tissue, it is shown to be impaired by differing ADCs, resulting in erroneously determined segmentation thresholds. This problem can be addressed by decreasing the maximum b-factor and increasing the number of signal averages at the maximum b-factor or, alternatively, the number of b-factors while favoring schemes with higher b-factors. Phantom experiments confirm the results of the simulations. In-vivo data are presented to illustrate the effect of sequence optimization on the diagnostic accuracy of the functional diffusion map.

CONCLUSION

The present work demonstrates that the diagnostic accuracy of the functional diffusion map can be impaired by inaccurate segmentation thresholds and derives means for its optimization that will increase the fidelity of future clinical studies.