Fat quantification in dual-layer detector spectral CT: How to handle iron overload, varying tube voltage and radiation dose Indices

OBJECTIVES

Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification.

MATERIAL AND METHODS

Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used.

RESULTS

In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0].

CONCLUSION

For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable.

Validation of 4D flow cardiovascular magnetic resonance in TIPS stent grafts using a 3D-printed flow phantom

BACKGROUND

Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is feasible for portal blood flow evaluation after placement of transjugular intrahepatic portosystemic shunts (TIPS) in patients with liver cirrhosis. However, clinical acceptance of 4D flow CMR in TIPS patients is limited due to the lack of validation studies. The purpose of this study was to validate 4D flow CMR-derived measurements in TIPS stent grafts using a three-dimensional (3D)-printed flow phantom.

METHODS

A translucent flow phantom of the portal vasculature was 3D-printed. The phantom consisted of the superior mesenteric vein and the splenic vein draining into the portal vein, the TIPS-tract, and the hepatic vein. A TIPS stent graft (Gore® Viatorr®) was positioned within the TIPS-tract. Superior mesenteric vein and splenic vein served as inlets for blood-mimicking fluid. 4D flow CMR acquisitions were performed at 3T at preset flow rates of 0.8 to 2.8 l/min using velocity encoding of both 1.0 and 2.0 m/s. Flow rates and velocities were measured at predefined levels in the portal vasculature and within the stent graft. Accuracy of 4D flow CMR was assessed through linear regression with reference measurements obtained by flow sensors and two-dimensional (2D) phase contrast (PC) CMR. Intra- and interobserver agreement were assessed through Bland-Altman analyses.

RESULTS

At a velocity encoding of 2.0 m/s, 4D flow CMR-derived flow rates and velocities showed an excellent correlation with preset flow rates and 2D PC CMR-derived flow velocities at all vascular levels and within the stent graft (all r ≥ 0.958, p ≤ 0.003). At a velocity encoding of 1.0 m/s, aliasing artifacts were present within the stent graft at flow rates ≥ 2.0 l/min. 4D flow CMR-derived measurements revealed high intra- and interobserver agreement.

CONCLUSIONS

The in vitro accuracy and precision of 4D flow CMR is unaffected by the presence of TIPS stent grafts, suggesting that 4D flow CMR may be used to monitor TIPS patency in patients with liver cirrhosis.

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

Temporal dynamics of white and gray matter plasticity during motor skill acquisition: a comparative diffusion tensor imaging and multiparametric mapping analysis

Learning new motor skills relies on neural plasticity within motor and limbic systems. This study uniquely combined diffusion tensor imaging and multiparametric mapping MRI to detail these neuroplasticity processes. We recruited 18 healthy male participants who underwent 960 min of training on a computer-based motion game, while 14 were scanned without training. Diffusion tensor imaging, which quantifies tissue microstructure by measuring the capacity for, and directionality of, water diffusion, revealed mostly linear changes in white matter across the corticospinal-cerebellar-thalamo-hippocampal circuit. These changes related to performance and reflected different responses to upper- and lower-limb training in brain areas with known somatotopic representations. Conversely, quantitative MRI metrics, sensitive to myelination and iron content, demonstrated mostly quadratic changes in gray matter related to performance and reflecting somatotopic representations within the same brain areas. Furthermore, while myelin and iron-sensitive multiparametric mapping MRI was able to describe time lags between different cortical brain systems, diffusion tensor imaging detected time lags within the white matter of the motor systems. These findings suggest that motor skill learning involves distinct phases of white and gray matter plasticity across the sensorimotor network, with the unique combination of diffusion tensor imaging and multiparametric mapping MRI providing complementary insights into the underlying neuroplastic responses.