Confounding factors in the generalisation of synthetic CT to diagnostic spine MRI

Clinical Snapshot

Key Findings

• P Value: Significant differences noted but specific values not provided

• Effect Size: Stronger effects observed inside vertebrae compared to surrounding tissues

• Primary Outcome: sCT generation performance degraded with larger MRI slice spacings and varied significantly between orientations

• Nnt Or Sensitivity: Significant correlation between CT noise levels and mean absolute error

• Confidence Interval: Not reported in abstract

Clinical Application

Requires high-resolution 3D MRI sequences and sophisticated deep learning infrastructure Relevant to Australian radiology departments implementing AI-assisted imaging workflows, particularly given Medicare rebates for spinal MRI and potential to reduce CT radiation exposure Patients with spinal pathologies requiring both MRI and CT imaging for orthopaedic treatment planning

Abstract

Objective. Generalisation of synthetic CT (sCT) generation to diagnostic MRI data in the spine faces many challenges, particularly when aiming at accurate visualisation of pathologies for orthopaedic treatment management. In this study we assessed the effect of potential confounding factors on the performance of sCT generation from diagnostic MR images.Approach. Paired spinal diagnostic MR and CT scans from two centres (51 patients) were collected retrospectively, spanning multiple spinal pathologies and regions. Each patient's dataset contained 3D T1-/T2-weighted and 2D T1- and T2-weighted scans. 3D U-Nets were trained per centre on the 3D MR data and CT using a mean absolute error (MAE) loss. The performance was assessed on sCTs derived from original 2D and 3D data and from simulated 2D data with varying orientation (axial/coronal/sagittal) and slice spacing (1.1-6.6 mm). The sCTs were compared to the CTs using MAE, peak signal-to-noise ratio, and dice similarity coefficient.Main results. We identified MR resolution, orientation and contrast as confounding factors for sCT generation from diagnostic MR. Additionally, CT noise and CT-to-MR registration were identified to influence the performance evaluation. Performance degraded with larger slice spacings and significant differences between orientations occurred more often at larger slice spacings. The effect of slice spacing and orientation was stronger inside the vertebrae than outside. While the networks performed better overall on contrasts more similar to the training contrasts, they demonstrated promising performance when trained on 3D T2w images and tested on 2D T1w images. Finally, higher CT noise levels resulted in worse sCT performance metrics, reflected in a significant correlation between noise and MAE.Significance. This work demonstrates that generalisation of sCT generation to diagnostic spine MRI data is hampered primarily by MRI acquisition related aspects including MR resolution, orientation and contrast, which provides guidance for future work on generalisable sCT from diagnostic MR.