Technical Note: Feasibility of translating 3.0T-trained Deep-Learning
  Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy
  Controls
Rupsa Bhattacharjee Zehra Akkaya Johanna Luitjens Pan Su Yang Yang Valentina Pedoia Sharmila Majumdar
In the current study, our purpose is to evaluate the feasibility of applying
deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in
healthy controls scanned at 0.55T and compared with 3.0T. The current study
assesses the performance of standard in-practice bone, and cartilage
segmentation algorithms at 0.55T, both qualitatively and quantitatively, in
terms of comparing segmentation performance, areas of improvement, and
compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial
results demonstrate a usable to good technical feasibility of translating
existing quantitative deep-learning-based image segmentation techniques,
trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition
environment. Especially in terms of segmenting cartilage compartments, the
models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T
low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be
utilized for evaluating knee cartilage thickness and bone segmentations aided
by established DL algorithms trained at higher-field strengths out-of-the-box
initially. This could be utilized at the far-spread point-of-care locations
with a lack of radiologists available to manually segment low-field images, at
least till a decent base of low-field data pool is collated. With further
fine-tuning with manual labeling of low-field data or utilizing synthesized
higher SNR images from low-field images, OA biomarker quantification
performance is potentially guaranteed to be further improved.