Geometric analysis of the b-dependent effects of Rician signal noise on diffusion tensor imaging estimates and determining an optimal b value

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Aberrant structural-functional coupling in adult cannabis users

Cellular studies indicate that endocannabinoid type-1 retrograde signaling plays a major role in synaptic plasticity. Disruption of these processes by delta-9-tetrahydrocannabinol (THC) could produce alterations either in structural and functional brain connectivity or in their association in cannabis (CB) users. Graph theoretic structural and functional networks were generated with diffusion tensor imaging and resting-state functional imaging in 37 current CB users and 31 healthy non-users. The primary outcome measures were coupling between structural and functional connectivity, global network characteristics, association between the coupling and network properties, and measures of rich-club organization. Structural-functional (SC-FC) coupling was globally preserved showing a positive association in current CB users. However, the users had disrupted associations between SC-FC coupling and network topological characteristics, most perturbed for shorter connections implying region-specific disruption by CB use. Rich-club analysis revealed impaired SC-FC coupling in the hippocampus and caudate of users. This study provides evidence of the abnormal SC-FC association in CB users. The effect was predominant in shorter connections of the brain network, suggesting that the impact of CB use or predispositional factors may be most apparent in local interconnections. Notably, the hippocampus and caudate specifically showed aberrant structural and functional coupling. These structures have high CB1 receptor density and may also be associated with changes in learning and habit formation that occur with chronic cannabis use.

Prenatal Maternal Cortisol Has Sex-Specific Associations with Child Brain Network Properties

Elevated maternal cortisol concentrations have the potential to alter fetal development in a sex-specific manner. Female brains are known to show adaptive behavioral and anatomical flexibility in response to early-life exposure to cortisol, but it is not known how these sex-specific effects manifest at the whole-brain structural networks. A prospective longitudinal study of 49 mother child dyads was conducted with serial assessments of maternal cortisol levels from 15 to 37 gestational weeks. We modeled the structural network of typically developing children (aged 6-9 years) and examined its global connectome properties, rich-club organization, and modular architecture. Network segregation was susceptible only for girls to variations in exposure to maternal cortisol during pregnancy. Girls generated more connections than boys to maintain topologically capable and efficient neural circuits, and this increase in neural cost was associated with higher levels of internalizing problems. Maternal cortisol concentrations at 31 gestational weeks gestation were most strongly associated with altered neural connectivity in girls, suggesting a sensitive period for the maternal cortisol-offspring brain associations. Our data suggest that girls exhibit an adaptive response by increasing the neural network connectivity necessary for maintaining homeostasis and efficient brain function across the lifespan.

The effects of noise in cardiac diffusion tensor imaging and the benefits of averaging complex data

There is growing interest in cardiac diffusion tensor imaging (cDTI), but, unlike other diffusion MRI applications, there has been little investigation of the effects of noise on the parameters typically derived. One method of mitigating noise floor effects when there are multiple image averages, as in cDTI, is to average the complex rather than the magnitude data, but the phase contains contributions from bulk motion, which must be removed first. The effects of noise on the mean diffusivity (MD), fractional anisotropy (FA), helical angle (HA) and absolute secondary eigenvector angle (E2A) were simulated with various diffusion weightings (b values). The effect of averaging complex versus magnitude images was investigated. In vivo cDTI was performed in 10 healthy subjects with b = 500, 1000, 1500 and 2000 s/mm(2). A technique for removing the motion-induced component of the image phase present in vivo was implemented by subtracting a low-resolution copy of the phase from the original images before averaging the complex images. MD, FA, E2A and the transmural gradient in HA were compared for un-averaged, magnitude- and complex-averaged reconstructions. Simulations demonstrated an over-estimation of FA and MD at low b values and an under-estimation at high b values. The transition is relatively signal-to-noise ratio (SNR) independent and occurs at a higher b value for FA (b = 1000-1250 s/mm(2)) than MD (b ≈ 250 s/mm(2)). E2A is under-estimated at low and high b values with a transition at b ≈ 1000 s/mm(2), whereas the bias in HA is comparatively small. The under-estimation of FA and MD at high b values is caused by noise floor effects, which can be mitigated by averaging the complex data. Understanding the parameters of interest and the effects of noise informs the selection of the optimal b values. When complex data are available, they should be used to maximise the benefit from the acquisition of multiple averages. The combination of complex data is also a valuable step towards segmented acquisitions.

Simple noise reduction for diffusion weighted images

Our purpose in this study was to reduce the noise in order to improve the SNR of Dw images with high b-value by using two correction schemes. This study was performed with use of phantoms made from water and sucrose at different concentrations, which were 10, 30, and 50 weight percent (wt%). In noise reduction for Dw imaging of the phantoms, we compared two correction schemes that are based on the Rician distribution and the Gaussian distribution. The highest error values for each concentration with use of the Rician distribution scheme were 7.3 % for 10 wt%, 2.4 % for 30 wt%, and 0.1 % for 50 wt%. The highest error values for each concentration with use of the Gaussian distribution scheme were 20.3 % for 10 wt%, 11.6 % for 30 wt%, and 3.4 % for 50 wt%. In Dw imaging, the noise reduction makes it possible to apply the correction scheme of Rician distribution.

Acquisition strategy to reduce cerebrospinal fluid partial volume effects for improved DTI tractography

PURPOSE

An acquisition method that does not increase scan time or specific absorption rate is investigated for reducing the deleterious effects of cerebrospinal fluid (CSF) partial volume effects on diffusion tensor imaging (DTI) tractography. It is based on using a shorter repetition time (TR) by means of slice acquisition re-ordering to reduce the signal of long T1 CSF and a non-zero minimum diffusion weighting (b-value) to attenuate rapidly diffusing CSF signal with respect to brain tissue.

METHODS

A target reduction of the CSF/brain signal ratio from 3.5 to 0.8 required a TR of 2.5 s and minimum b-value of 425 s/mm(2) . This was evaluated at 4.7 Tesla in eight healthy young adults for tractography of the fornix, which has considerable CSF contamination and is difficult to track from standard DTI.

RESULTS

This method effectively reduced CSF signal relative to brain and yielded more robust tractography, increased tract volume, increased fractional anisotropy, and decreased mean diffusivity in the fornix relative to standard DTI.

CONCLUSION

CSF partial volume effects in DTI can be mitigated in acquisition through reduced TR and non-zero minimum diffusion weighting. The lack of RF absorption rate or scan time increases is attractive over other CSF suppression methods such as inversion recovery.

In vivo assessment of optimal b-value range for perfusion-insensitive apparent diffusion coefficient imaging

PURPOSE

To assess the optimal b-values range for perfusion-insensitive apparent diffusion coefficient (ADC) imaging of abdominal organs using short-duration DW-MRI acquisitions with currently available ADC estimation methods.

METHODS

DW-MRI data of 15 subjects were acquired with eight b-values in the range of 5-800 s∕mm(2). The reference-standard, a perfusion insensitive, ADC value (ADC(IVIM)), was computed using an intravoxel incoherent motion (IVIM) model with all acquired diffusion-weighted images. Simulated DW-MRI data was generated using an IVIM model with b-values in the range of 0-1200 s∕mm(2). Monoexponential ADC estimates were calculated using: (1) Two-point estimator (ADC(2)); (2) least squares three-point (ADC(3)) estimator and; (3) Rician noise model estimator (ADC(R)). The authors found the optimal b-values for perfusion-insensitive ADC calculations by minimizing the relative root mean square error (RRMS) between the ADC(IVIM) and the monoexponential ADC values for each estimation method and organ.

RESULTS

Low b-value = 300 s∕mm(2) and high b-value = 1200 s∕mm(2) minimized the RRMS between the estimated ADC and the reference-standard ADC(IVIM) to less than 5% using the ADC(3) estimator. By considering only the in vivo DW-MRI data, the combination of low b-value = 270 s∕mm(2) and high b-value of 800 s∕mm(2) minimized the RRMS between the estimated ADC and the reference-standard ADC(IVIM) to <7% using the ADC(3) estimator. For all estimators, the RRMS between the estimated ADC and the reference standard ADC correlated strongly with the perfusion-fraction parameter of the IVIM model (r = [0.78-0.83], p ≤ 0.003).

CONCLUSIONS

The perfusion compartment in DW-MRI signal decay correlates strongly with the RRMS in ADC estimates from short-duration DW-MRI. The impact of the perfusion compartment on ADC estimations depends, however, on the choice of b-values and estimation method utilized. Likewise, perfusion-related errors can be reduced to <7% by carefully selecting the b-values used for ADC calculations and method of estimation.

Improving DTI tractography by including diagonal tract propagation

Tractography algorithms have been developed to reconstruct likely WM pathways in the brain from diffusion tensor imaging (DTI) data. In this study, an elegant and simple means for improving existing tractography algorithms is proposed by allowing tracts to propagate through diagonal trajectories between voxels, instead of only rectilinearly to their facewise neighbors. A series of tests (using both real and simulated data sets) are utilized to show several benefits of this new approach. First, the inclusion of diagonal tract propagation decreases the dependence of an algorithm on the arbitrary orientation of coordinate axes and therefore reduces numerical errors associated with that bias (which are also demonstrated here). Moreover, both quantitatively and qualitatively, including diagonals decreases overall noise sensitivity of results and leads to significantly greater efficiency in scanning protocols; that is, the obtained tracts converge much more quickly (i.e., in a smaller amount of scanning time) to those of data sets with high SNR and spatial resolution. Importantly, the inclusion of diagonal propagation adds essentially no appreciable time of calculation or computational costs to standard methods. This study focuses on the widely-used streamline tracking method, FACT (fiber assessment by continuous tracking), and the modified method is termed "FACTID" (FACT including diagonals).