Francesco Grussu^{1}, Kinga Bernatowicz^{1}, Ignasi Barba^{2}, Marco Palombo^{3}, Juan Francisco Corral^{4,5}, Marta Vidorreta^{6}, Xavier Merino^{4,5}, Richard Mast^{4,5}, Núria Roson^{4,5}, Nahúm Calvo‐Malvar^{4,7}, Manuel Escobar Amores^{4,5}, Josep R. Garcia-Bennett^{7}, and Raquel Perez-Lopez^{1,5}

^{1}Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ^{2}NMR Lab, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ^{3}Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, ^{4}Institut de Diagnòstic per la Imatge (IDI), Catalonia, Spain, ^{5}Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain, ^{6}Siemens Healthineers, Madrid, Spain, ^{7}Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain

Innovative liver Diffusion-Weighted (DW) MRI aims to increase sensitivity and biological specificity of routine DW imaging, but may feature lower reproducibility due to longer scan times and acquisition of highly DW images. We assess inter-scanner reproducibility and variability of metrics from two novel approaches, T2-Intra-Voxel Incoherent Motion-Kurtosis (T2-IVIM-Kurtosis) and Diffusion-Relaxation Hepatic Imaging via Generalised Assessment of DiffusiOn Simulations (DR-HIGADOS), in two 1.5T scanners (Siemens Avanto; Philips Ingenia). Both methods are reproducible across scanners. Cellularity, intra-cellular diffusivity and vascular fraction show the highest measurement variability, implying that larger cohorts may be required in studies that focus on these indices.

A 34 healthy male was scanned three times on each of a 1.5T Siemens Avanto (scanner 1) and 1.5T Philips Ingenia (scanner 2) systems, following informed written consent. DW scans featuring acquisition of multiple b-values, echo times and diffusion times were acquired (Table 1).

Scans were denoised

T2-IVIM-Kurtosis and DR-HIGADOS metrics were computed. Table 2 reports a description of all metrics.

$$ s(b,TE) \,\,=\,\, s_0\,\left(f_V e^{-bD_V \,-\,\frac{TE}{T_{2V}} } \,\,+\,\, (1-f_V ) e^{-bD_T \,+\,\frac{1}{6}(bD_T)^2K_T \,-\,\frac{TE}{T_{2T}} }\right)\,\,[Eq.1]$$

unifying the T2-IVIM

A linear regression model

$$m \,\,=\,\, \alpha \,+\, \beta\,\,(n-1)\,\,[Eq.2]$$

was fitted for all MRI metrics. Above, $$$m$$$ indicates the ROI median of a metric; $$$n$$$ is the scanner index ($$$n$$$= {1,2} for scanner {1,2}); $$$\alpha$$$ is the metric value in scanner 1; $$$\alpha + \beta$$$ is the metric value in scanner 2; $$$\beta$$$ is the inter-scanner difference. Additionally, a Coefficient of Variation (CoV)

$$CoV \,\,=\,\ 100 \,\, \frac{IQR}{ \frac{\alpha}{2} \,+\,\frac{\,\,\alpha + \beta}{2}\,\,},\,[Eq.3]\,\,$$

estimating a metric total variability, was computed. In Eq. 3, IQR indicates the interquartile range of a metric across scanners and sessions.

Table 3 reports figures of inter-scanner differences and total variability (CoV) for all metrics. No statistically significant inter-scanner differences are seen (no p-values smaller than 0.05 are obtained for $$$\beta$$$). In future, we plan to recruit additional volunteers to increase the statistical power of our inter-scanner regression analysis.

Regarding CoV, all metrics apart from vascular fraction $$$f_V$$$, intra-cellular diffusivity $$$D_0$$$ and cellularity $$$C$$$ exhibit CoV smaller than 15% (58.5% for $$$f_V$$$, 20.1% for $$$D_0$$$, 25.4% for $$$C$$$). The high CoV for $$$f_V$$$ may result from the limited number of perfusion-sensitive (

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DOI: https://doi.org/10.58530/2022/1722