Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol

In primary care, low back pain is among the most frequent reasons for visiting a general practitioner or physiotherapist [1], and low back pain was highlighted as the single highest cause of years lived with disability [2].

Disc degeneration, Modic changes, and facet joint degeneration are most common and possible causes of low back pain [3‐5] that can be detected by magnetic resonance imaging (MRI) becoming a commonly used diagnostic imaging modality.

MRI offers many advantages in terms of soft tissue resolution and the absence of ionizing radiation. Nevertheless, it is a method impaired by long acquisition times and relying in parameters’ adjustments in order to improve image quality, eventually resulting in longer scan times [6].

In recent years, artificial intelligence (AI) — in particular deep learning (DL) — is gaining ground in many areas of imaging such as image classification, segmentation, denoising, super-resolution, and image synthesis/transformation [7, 8]. After proper training on a high volume of high-quality images considered as ground truth, DL algorithms allow to reconstruct images with higher signal to noise ratio (SNR), improved spatial resolution, and reduced truncation artefacts, resulting in images with high diagnostic quality and reduced acquisition times compared to standard protocols [9‐11].

Among the various DL-based algorithms proposed in recent period, the FDA approved algorithm proposed by GE Healthcare (AIR Recon DL™) uses a deep feed-forward convolutional neural network which reconstructs images with higher SNR, reduces truncation artefacts, and enables higher spatial resolution. Up to now, the pipeline has been trained and optimized on 2D sequences in multiple anatomical regions and for various pulse sequences, contrast weightings, field strengths, and coil configurations [6].

Although many authors have investigated the role of AI application on lumbar spine in different fields such as pathology detection [12, 13] and reporting [14], to the best of our knowledge, the role of AI algorithm on whole routinely MRI lumbar spine protocol acquisition has not been investigated yet.

Thus, the aim of this study is to compare image quality, diagnostic confidence, and scan time between standard protocol and new deep learning reconstruction protocol in lumbar spine MRI.

Results have confirmed the hypothesis of our prospective study: images acquired with DLR algorithm showed a better quantitative and qualitative image quality and a 50.2% lower scan time than standard protocol on lumbar spine MRI. AIR Recon DL™ may provide a faster scanning protocol for lumbar spine MRI study, providing even a better image quality than the standard protocol. To the best of our knowledge, no one have compared DLR algorithm and standard protocol on routinely MRI lumbar spine exams yet.

In the field of musculoskeletal applications, Herrmann J. et al. [18] evaluated the feasibility of a DL MRI Reconstruction for TSE sequences, comprising lumbar spine. Although the study was performed on a 3.0T scanner of a different vendor, results are in accordance with ours in terms of acquisition time reduction and images qualitative assessment. Nevertheless, authors limited their analysis on sagittal TSE T1W and TSE T2W images and did not perform a quantitative analysis in term of SNR and CNR, while our investigation comprised the whole standard acquisition protocol. We believe that quantitative analysis is an added value as it can allow for a better comparison and can reproducibility among different studies. Additionally, our results are in accordance with a recent investigation [19] focused on DLR in lumbar spine, demonstrating higher image quality, higher diagnostic accuracy, and short acquisition time compared to standard MRI protocol. Of note, the study was performed with a MRI equipment of a different manufacturer than ours, therefore DLR might transversally outperform conventional sequences, regardless the vendor, even if a direct comparative study is still lacking.

Additionally, similar results have been obtained by S. Hahn et al. [20], who compared image quality and the diagnostic performances of standard MRI sequences with accelerated sequences without and with AIR Recon DL™ in the identification of tendons lesions on 3.0T shoulder MRI examinations. Their results showed similar subjective image quality, artefacts, and diagnostic performance with standard sequences, obtaining a 67% scan time reduction using accelerated DLR sequences. The availability of arthroscopy as reference standard was an added value of the investigation; however, the lack of quantitative analysis represented a limitation of the study.

In the field of cardiac imaging Velde N. et al. [21] evaluated the influence of AIR Recon DL™ on late gadolinium enhancement image quality on myocardial scar quantification in patients with suspected or known cardiomyopathy. They demonstrated that DLR improved image quality on visual scoring, SNR, and CNR compared to the standard MRI scans. Although their results are obtained on images acquired on 1.5T MRI scanner after injection of contrast medium, they are in agreement with ours. This leads us to underline the potentiality of DLR application on different anatomical districts. Diagnostic performance of AIR Recon DL™ was also investigated by Kim M. et al. [22] in the evaluation of pituitary gland pathology. Their study was conducted comparing 1-mm slice thickness MRI with DLR with 3-mm slice thickness MRI for identifying residual tumour and cavernous sinus invasion in the evaluation of postoperative pituitary adenoma. Results of both quantitative and qualitative analysis showed that the sensitivity of DLR sequences was higher than that of conventional sequences in the identification of residual tumour and cavernous sinus invasion. Unfortunately, no data of acquisition time was reported. Albeit their analysis was conducted on images with different slice thickness than ours, both quantitative and qualitative results are in accordance with ours. This leads us to assume that the algorithm, regardless of the sequence acquisition parameters, can still improve the image quality in both qualitative and quantitative terms.

Regarding clinical impact of the DLR protocol, several advantages might be routinely achieved. For instance, shorter acquisition time helps to improve patient compliance. As demonstrated by Schreiber-Zinaman J. and colleagues on a different anatomical district [23], multiple sequences in abdominal MRI can result in patient dissatisfaction and inefficient resource utilization. This principle can also be applied in lumbar spine district; in fact, shorter examination time translates to higher patient compliance, especially in claustrophobic individuals or in patients who have severe back pain and difficulty in maintaining supine position for a long time. Another related advantage of reduced scan time is the possibility, in case of diagnostic necessity, to add further sequences without significantly affecting acquisition time, scheduling time and patient discomfort.

In addition, considering the reduction in acquisition time, further consideration about costs can be made. Bratke G. and colleagues [24] showed how scanning time reduction is an effective way to increase cost efficiency with MRI. This DLR algorithm, with a total of 50% of time reduction, can also facilitate departmental cost-optimization; further studies tailored on benefit-costs balance are needed to confirm the impact of such important time reduction.

There were some limitations in our study. First, this was a single-centre study with a small number of participants. Second, we only enrolled asymptomatic individuals. As results, only 15 individuals had incidental lumbar pathologies. Third, we did not investigate the AIR Recon DL™ effect on diagnostic performances in a large variety of spine pathology including (e.g. nerve inflammation or damage, severe discitis, trauma) nor on a large sample size.

In conclusion, AIR Recon DL™ images resulted in higher qualitative and quantitative image quality compared with standard reconstruction protocol, with significant shorter acquisition protocol. Therefore, we suggest that DLR protocol can be safely implemented in clinical practice; further multicentre prospective investigations are needed to investigate its impact on diagnostic accuracy.