Fast and robust estimation of diffusional kurtosis imaging (DKI) parameters by general closed-form expressions and their extensions

Utility of MRI in Quantifying Tissue Injury in Cervical Spondylotic Myelopathy

Cervical spondylotic myelopathy (CSM) is a progressive disease that worsens over time if untreated. However, the rate of progression can vary among individuals and may be influenced by various factors, such as the age of the patients, underlying conditions, and the severity and location of the spinal cord compression. Early diagnosis and prompt treatment can help slow the progression of CSM and improve symptoms. There has been an increased use of magnetic resonance imaging (MRI) methods in diagnosing and managing CSM. MRI methods provide detailed images and quantitative structural and functional data of the cervical spinal cord and brain, allowing for an accurate evaluation of the extent and location of tissue injury. This review aims to provide an understanding of the use of MRI methods in interrogating functional and structural changes in the central nervous system in CSM. Further, we identified several challenges hindering the clinical utility of these neuroimaging methods.

Optimization of scan parameters to reduce acquisition time for RESOLVE-based diffusion kurtosis imaging (DKI) in nasopharyngeal carcinoma (NPC)

OBJECTIVE

To shorten acquisition time of readout segmentation of long variable echo trains (RESOLVE)-based diffusion kurtosis imaging (DKI) via Readout Partial Fourier (RPF) and b-value combinations.

METHODS

The RESOLVE-based DKI images of 38 patients with nasopharyngeal carcinoma (NPC) were prospectively enrolled. For RESOLVE-based DKI images with 5/8 RPF and without RPF, objective and subjective evaluations of image quality were performed. A total of nine groups with different b-value combinations were simulated, and the influence of different b-value combinations for RESOLVE-RPF-based DKI sequences was assessed using the intraclass correlation coefficient (ICC).

RESULTS

The mean values of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in DKI images without RPF were higher than those with 5/8 RPF (252.9 ± 77.7 vs 247.3 ± 85.5 and 5.8 ± 2.8 vs 5.4 ± 2.3, respectively), but not significantly (p = 0.460 and p = 0.180, respectively). In comparing the ICCs between nine groups of different b-value combinations in RESOLVE-RPF-based DKI, group (200, 800, 2000 s/mm2), group (200, 400, 800, 2000 s/mm2) and group (200, 800, 1500, 2000 s/mm2) were not significantly different (p > 0.001) and showed excellent agreement (0.81-1.00) with that of group (200, 400, 800, 1500, 2000 s/mm2). Using b-value optimization and RPF technology, the group with RPF (200, 400, 800, 2000 s/mm2) showed a 56% reduced scanning compared with the group without RPF (200, 400, 800, 1500, 2000 s/mm2; 3 min 46 s vs 8 min 31 s, respectively).

CONCLUSION

DKI with RPF did not significantly affect image quality, but both RPF and different b-value combinations can affect the scanning time. The combination of RPF and b-value optimization can ensure the stability of DKI parameters and reduce the scanning time by 56%.

ADVANCES IN KNOWLEDGE

This work is to optimize scan parameters, e.g. RPF and b-value combinations, to reduce acquisition time for RESOLVE-based DKI in NPC. To our knowledge, the effect of RESOLVE-RPF and b-value combinations on DKI has not been reported.

[Evaluation of Diffusional Kurtosis Inference Using Synthetic q-space Learning and Bias Correction]

PURPOSE

In synthetic q-space learning (synQSL), which uses deep learning to infer the diffusional kurtosis (K), a bias that depends on the noise level added to the synthetic training data occurs. The purpose of this study was to evaluate K inference using synQSL and bias correction.

METHODS

Using the synthetic test data and the real image data, K was inferred by synQSL, and bias correction was performed. Then, those results were compared with K inferred by fitting by the least-squares fitting (LSF) method. At this time, the noise level of the training data was set to 3 types, the noise level of the synthesis test data was set to 5 types, and the number of excitation (NEX) of the real image data was set to 4 types. Robustness of inference was evaluated by the outlier rate, which is the ratio of K outliers to the whole brain. We also evaluated the root mean square error (RMSE) of the inferred K.

RESULTS

The outlier rate inferred by synQSL without correction was significantly lower in the test data of each noise level than that by the LSF method and was further reduced by correction. In addition, the RMSE of NEX 1 with NEX 4 as the correct answer based on the real image data had the smallest correction result of K by synQSL.

CONCLUSION

Inferring K using synQSL and bias correction is a robust and small error method compared to that using the LSF method.

Recent Advances in Parameter Inference for Diffusion MRI Signal Models

In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.

Diffusional kurtosis imaging in evaluation of microstructural changes of spinal cord in cervical spondylotic myelopathy feasibility study

To explore the value of diffusion kurtosis imaging in the changes of spinal cord microstructures in patients with early cervical spondylotic myelopathy.Twenty nine patients with cervical myelopathy were selected in this study. All images were acquired on a 3.0 T MR scanner (Skyra, Siemens Medical Systems, Germany). The imaging parameters for diffusion kurtosis imaging were as follows: repetition time/echo time, 3000/91 ms; averages, 2; slice thickness/gap, 3/0.3 mm; number of slices, 17; field of view, 230 × 230 mm; Voxel size, 0.4 × 0.4 × 3.0 mm; 3 b-values (0, 1000, and 2000 s/mm) with diffusion encoding in 20 directions for each b-value. Values for fractional anisotropy, mean diffusivity, and mean diffusional kurtosis (MK) were calculated and compared between unaffected and affected spinal cords.In all patients MK was significantly lower in normal appearing spinal cords adjacent to the affected cervical spinal cords than in normal cervical spinal cords (0.862 ± 0.051 vs 0.976 ± 0.0924, P < .0001), but the difference of fractional anisotropy and apparent diffusion coefficient was no significant (P > .05). The affected cervical spinal cords had lower MK (0.716 ± 0.0753), FA and higher apparent diffusion coefficient than normal cervical spinal cords (P < .001).MK values in the cervical spinal cord may reflect microstructural changes of spinal cord damage in cervical myelopathy, and it could potentially provide more information that obtained with conventional diffusion metrics.

Diffusional Kurtosis Imaging in Idiopathic Normal Pressure Hydrocephalus: Correlation with Severity of Cognitive Impairment

PURPOSE

Diffusional kurtosis imaging (DKI) is an emerging technique that describes diffusion of water molecules in terms of deviation from Gaussian distribution. This study investigated correlations between DKI metrics and cognitive function in patients with idiopathic normal pressure hydrocephalus (iNPH).

MATERIALS AND METHODS

DKI was performed in 29 iNPH patients and 14 age-matched controls. Mini-mental state examination (MMSE), frontal assessment battery (FAB), and trail making test A (TMT-A) were used as cognitive measures. Tract-based spatial statistics (TBSS) analyses were performed to investigate the between-group differences and correlations with the cognitive measures of the diffusion metrics, including mean kurtosis (MK), fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD).

RESULTS

In iNPH patients, FA and MK identified positive correlations with cognitive function in similar regions, predominantly in the frontal lobes (P < 0.05, corrected for multiple comparisons). The frontoparietal subcortical white matter showed significant correlations with FAB and TMT-A across more extensive areas in MK analyses than in FA. ADC, AD, and RD analyses showed no significant correlations with MMSE and FAB, while negative correlation with TMT-A was observed in the limited portion of the frontal deep white matter.

CONCLUSION

Both FA and MK correlated well with cognitive impairment in iNPH. The observed differences between FA and MK results suggest DKI may play a complementary role to conventional FA and ADC analyses, especially for evaluation of the subcortical white matter.

Time Course of Diffusion Kurtosis in Cerebral Infarctions of Transient Middle Cerebral Artery Occlusion Rat Model

OBJECTIVE

To evaluate the relationship between fiber bundle direction and changes in diffusion kurtosis, we evaluated the apparent diffusion kurtosis coefficients (AKCs) that were perpendicular to and parallel to the principal diffusion tensor eigenvector.

MATERIALS AND METHODS

Adult male Wistar rats were subjected to 30 or 60 minutes of middle cerebral artery occlusion and imaged with a 7T Magnetic Resonance Imager System (Varian MRI System 7T/210: Agilent Technologies, CA). Diffusion kurtosis images were obtained before middle cerebral artery (MCA) reperfusion and 3, 6, and 24 hours after reperfusion to generate the apparent diffusion coefficient (ADC), fractional anisotropy (FA), mean apparent diffusion kurtosis coefficient (mAKC), AKC axial to the eigenvector (axAKC), and AKC radial to the eigenvector (radAKC) images. The time course of the region/normal ratio was evaluated for the above parameters in the caudoputamen and white matter.

RESULTS

Relative FA and relative ADC values decreased 3 hours after MCA reperfusion and remained decreased until 24 hours. Relative mAKC, axAKC, and radAKC values were increased 3 hours after MCA reperfusion, peaked after 6 hours, and slightly decreased after 24 hours. In the white matter, axAKC showed larger changes than radAKC.

CONCLUSION

The time course of the diffusion kurtosis value showed earlier pseudonormalization than the ADC value of the lesions. For white matter lesions, the increase in axAKC was larger than that in radAKC, suggesting that the tissue changes after infarction mainly produce reduced diffusivity along the fibers and lead to increased inhomogeneity of the diffusion.

Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis

Objectives

A new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.

Methods

Fourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm²) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).

Results

Error-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.

Conclusion

eDKI might be useful for assessing DKI in clinical settings.

Key Points

• A method to practically estimate axial/radial DKI from limited data was developed.

• The high robustness of the proposed method can greatly improve map images.

• The accuracy of the proposed method was high.

• Axial/radial K maps can be calculated from limited diffusion-encoding directions.

• The proposed method might be useful for assessing DKI in clinical settings.

Kurtosis analysis of neural diffusion organization

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.