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Abstract
Short tau inversion recovery (STIR) sequences are frequently used in magnetic resonance imaging (MRI) of the spine. However, STIR sequences require a significant amount of scanning time. The purpose of the present study was to generate virtual STIR (vSTIR) images from non-contrast, non-fat-suppressed T1- and T2-weighted images using a conditional generative adversarial network (cGAN). The training dataset comprised 612 studies from 514 patients, and the validation dataset comprised 141 studies from 133 patients. For validation, 100 original STIR and respective vSTIR series were presented to six senior radiologists (blinded for the STIR type) in independent A/B-testing sessions. Additionally, for 141 real or vSTIR sequences, the testers were required to produce a structured report of 15 different findings. In the A/B-test, most testers could not reliably identify the real STIR (mean error of tester 1–6: 41%; 44%; 58%; 48%; 39%; 45%). In the evaluation of the structured reports, vSTIR was equivalent to real STIR in 13 of 15 categories. In the category of the number of STIR hyperintense vertebral bodies (p = 0.08) and in the diagnosis of bone metastases (p = 0.055), the vSTIR was only slightly insignificantly equivalent. By virtually generating STIR images of diagnostic quality from T1- and T2-weighted images using a cGAN, one can shorten examination times and increase throughput.
Highlights
The spine is one of the body regions that is the most frequently examined in magnetic resonance imaging (MRI)
The spine is one of the body regions that is the most frequently examined in MRI
In 34% of the cases, the raters disagreed, while in 41% of the cases, a majority chose the rSTIR, but in 25% of the cases, they chose the virtual STIR (vSTIR)
Summary
The spine is one of the body regions that is the most frequently examined in MRI. Reasons for MRI are mainly back pain, sensitivity impairments, and paralysis [1,2]. The STIR contrasts are useful in the diagnosis of acute pathologies, such as inflammation or acute vertebral fractures. In the example of a vertebral body fracture, STIR is used to detect a vertebral edema and often enables a therapy-relevant differentiation between new and old fractures. Apart from that, the STIR sequence can lead to the decision of whether a contrast agent administration is required [3]. This is especially important considering the continuously increasing number of MRI examinations worldwide [4]. The introduction of new techniques based on deep learning has enabled advances in image processing that were previously widely considered impossible. It could be demonstrated that GANs are highly effective in CT denoising [6] and in inserting virtual contrast media in non-contrast MRI [7]
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