Assessment of the link between quantitative biexponential diffusion-weighted imaging and contrast-enhanced MRI in the liver

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids

OBJECTIVES

No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weighted (CE-T1w) scans is inhibited by safety concerns. The objective of this study was to develop and test a deep learning-based method for translation of diffusion-weighted imaging (DWI) into synthetic CE-T1w scans, for monitoring MR-HIFU treatment progression.

METHODS

The algorithm was retrospectively trained and validated on data from 33 and 20 patients respectively who underwent an MR-HIFU treatment of uterine fibroids between June 2017 and January 2019. Postablation synthetic CE-T1w images were generated by a deep learning network trained on paired DWI and reference CE-T1w scans acquired during the treatment procedure. Quantitative analysis included calculation of the Dice coefficient of NPVs delineated on synthetic and reference CE-T1w scans. Four MR-HIFU radiologists assessed the outcome of MR-HIFU treatments and NPV ratio based on the synthetic and reference CE-T1w scans.

RESULTS

Dice coefficient of NPVs was 71% (± 22%). The mean difference in NPV ratio was 1.4% (± 22%) and not statistically significant (p = 0.79). Absolute agreement of the radiologists on technical treatment success on synthetic and reference CE-T1w scans was 83%. NPV ratio estimations on synthetic and reference CE-T1w scans were not significantly different (p = 0.27).

CONCLUSIONS

Deep learning-based synthetic CE-T1w scans derived from intraprocedural DWI allow gadolinium-free visualization of the predicted NPV, and can potentially be used for repeated gadolinium-free monitoring of treatment progression during MR-HIFU therapy for uterine fibroids.

KEY POINTS

• Synthetic CE-T1w scans can be derived from diffusion-weighted imaging using deep learning. • Synthetic CE-T1w scans may be used for visualization of the NPV without using a contrast agent directly after MR-HIFU ablations of uterine fibroids.

Intravoxel incoherent motion imaging has the possibility to detect liver abnormalities in young Fontan patients with good hemodynamics

INTRODUCTION

Liver fibrosis and cirrhosis are one of the critical complications in Fontan patients. However, there are no well-established non-invasive and quantitative techniques for evaluating liver abnormalities in Fontan patients. Intravoxel incoherent motion diffusion-weighted imaging with MRI is a non-invasive and quantitative method to evaluate capillary network perfusion and molecular diffusion. The objective of this study is to assess the feasibility of intravoxel incoherent motion imaging in evaluating liver abnormalities in Fontan children.

MATERIALS AND METHODS

Five consecutive Fontan patients and four age-matched healthy volunteers were included. Fontan patients were 12.8 ± 1.5 years old at the time of MRI scan. Intravoxel incoherent motion imaging parameters (D, D*, and f values) within the right hepatic lobe were compared. Laboratory test, ultrasonography, and cardiac MRI were also conducted in the Fontan patients. Results of cardiac catheterization conducted within one year of the intravoxel incoherent motion imaging were also examined.

RESULTS

In Fontan patients, laboratory test and liver ultrasonography showed almost normal liver condition. Cardiac catheter and MRI showed good Fontan circulation. Cardiac index was 2.61 ± 0.23 L/min/m2. Intravoxel incoherent motion imaging parameters D, D*, and f values were lower in Fontan patients compared with controls (D: 1.1 ± 0.0 versus 1.3 ± 0.2 × 10-3 mm2/second (p = 0.04), D*: 30.8 ± 24.8 versus 113.2 ± 25.6 × 10-3 mm2/second (p < 0.01), and f: 13.2 ± 3.1 versus 22.4 ± 2.4% (p < 0.01), respectively).

CONCLUSIONS

Intravoxel incoherent motion imaging is feasible for evaluating liver abnormalities in children with Fontan circulation.

Effect of flow-encoding strength on intravoxel incoherent motion in the liver

PURPOSE

To study the impact of variable flow-encoding strength on intravoxel incoherent motion (IVIM) liver imaging of diffusion and perfusion.

THEORY

Signal attenuation in DWI arises from (1) intravoxel microvascular blood flow, which depends on the flow-encoding strength α (first gradient moment) of the diffusion-encoding waveform, and (2) intravoxel spin diffusion, which depends on the b-value of the diffusion-encoding gradient waveforms α and b-value. Both are linked to the diffusion-encoding gradient waveform and conventionally are not independently controlled.

METHODS

In this work a convex optimization framework was used to generate gradient waveforms with independent α and b-value. Thirty-six unique α and b-value sample points from 5 different gradient waveforms were used to reconstruct perfusion fraction (f), coefficient of diffusion (D), and blood velocity standard deviation (V_b ) maps using a recently proposed IVIM model. Faster acquisition strategies were evaluated with 1000 random subsampling strategies of 16, 8, and 4 α and b-value. Among the subsampled reconstructions, the sampling schemes that minimized the difference with the fully sampled reconstruction were reported.

RESULTS

Healthy volunteers (N = 9) were imaged on a 3T scanner. Liver perfusion and diffusion estimates using the fully sampled IVIM method were f = 0.19 ± 0.06, D = 1.15 ± 0.15 × 10^-3 mm² /s, and V_b = 5.22 ± 3.86 mm/s. No statistical differences were found between the fully sampled and 2-times undersampled reconstruction (f = 0.2 ± 0.07, D = 1.19 ± 0.15 × 10^-3 mm² /s, V_b = 5.79 ± 3.43 mm/s); 4-times undersampled (f = 0.2 ± 0.06, D = 1.15 ± 0.17 × 10^-3 mm² /s, V_b = 4.66 ± 3.61 mm/s), or 8-times undersampled ( f = 0.2 ± 0.06, D = 1.23 ± 0.22 × 10^-3 mm² /s, V_b = 4.99 ± 3.82 mm/s) approaches.

CONCLUSION

We demonstrate the IVIM signal's dependence on the b-value, the diffusion-encoding time and the flow-encoding strength and observe in vivo the ballistic regime signature of microperfusion in the liver. This work also demonstrates that using an IVIM model and sampling scheme matched to the ballistic regime, pixel-wise IVIM parameter maps are possible when sampling as few as 4 IVIM signals.

Comparison of microvascular perfusion evaluation among IVIM-DWI, CT perfusion imaging and histological microvessel density in rabbit liver VX2 tumors

OBJECT

To explore microcirculation features with intravoxel incoherent motion (IVIM) and to compare IVIM with CT perfusion imaging (CTPI) and microvessel density (MVD).

MATERIALS AND METHODS

Hepatic CTPI and IVIM were performed in 16 rabbit liver VX2 tumor models. Hepatic arterial perfusion (HAP), hepatic arterial perfusion index (HPI), Blood flow (BF), and blood volume (BV) from CTPI were measured. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) from IVIM were measured. MVD was counted with CD34 stain. The microcirculation features with IVIM were compared with CTPI parameters and MVD.

RESULTS

Strong linear correlations were found between D value (0.89±0.21×10^-3mm²/s) and HAP (15.83±6.97ml/min/100mg) (r=0.755, P=0.001) and between f value (12.64±6.66%) and BV (9.74±5.04ml/100mg) (r=0.693, P=0.004). Moderate linear correlations were observed between ADC (1.07±0.32×10^-3mm²/s) and HAP (r=0.538, P=0.039), respectively; and between D value and MVD (9.31±2.57 vessels at 400×magnification) (r=0.509, P=0.044). No correlations were found between D* (119.90±37.67×10^-3mm²/s) and HAP, HPI (68.34±12.91%), BF (4.95±2.16ml/min/100mg), BV.

CONCLUSION

IVIM parameters can characterize microcirculation to certain extent and separate it from pure water molecular diffusion. There is fair correlation between D or ADC value and CTPI parameters or MVD, but no correlation between D* or f value and CTPI parameters or MVD except f value and BV, which is still unclear and need further clinical studies to validate.