Deep learning–based acceleration of Compressed Sense MR imaging of the ankle

The ankle is one of the most complex joints with multiple oblique oriented ligaments and tendons [1‐6]. In the United States, approximately 10,000 people suffer an ankle injury per day, the most frequent type of injury being sprains [7, 8]. Magnetic resonance (MR) imaging is one of the main imaging modalities for the assessment of musculoskeletal disorders on account of its high soft tissue contrast. However, the data acquisition process is inherently slow due to long encoding times [9]. This leads to long scan times and subsequently increased exam costs and reduced patient throughput. Moreover, the image quality is frequently compromised by motion artifacts, since remaining motionless for several minutes is a challenge even for healthy subjects [10].

Developments such as parallel imaging (PI) and, later, compressed sensing (CS) have accelerated MR image acquisition. CS reduces the number of acquired lines in k-space and restores the missing data through an iterative reconstruction algorithm [11, 12]. The combination of CS and PI was shown to reduce MR image acquisition times of the ankle by 20% without compromising diagnostic performance [13, 14]. More recently deep learning–based methods such as convolutional neural networks (CNNs) and generative adversarial networks (GAN) have shown promising results to accelerate the MR imaging data acquisition process [15]. These methods apply deep learning–based reconstruction schemes to create high-quality images from undersampled MR data [9, 16‐21].

The aim of this study was to conduct a validation study to evaluate a compressed sensing artificial intelligence framework (CSAI) combining PI, CS, and deep learning–based artificial intelligence, for additional twofold and threefold acceleration of multi-contrast and multi-planar ankle MR imaging compared to conventional CS imaging and to compare the image quality and diagnostic performance between both techniques. In a secondary analysis, we compared the depiction of anatomical structures using high-resolution images reconstructed with CSAI.

In this study, we implemented an artificial intelligence framework for additional twofold and threefold acceleration of ankle MR imaging compared to conventional CS. The correlation was perfect for CS and CSAI2x and excellent for CS and CSAI3x. No significant differences were found for the depiction of anatomical structures between CS and CSAI2x and the same abnormalities were detected in both sequences. For CSAI3x the depiction was graded significantly lower compared to CS; however, most abnormalities were also detected. The high-resolution axial T2-weighted sequence was found to specifically improve the depiction of tendons, bone and the tibial nerve, with only minimally increased scan time compared to the axial T2-weighted CS reference scan.

Previous studies have used deep learning to accelerate MR imaging. One method is to create high-resolution from low-resolution data [27‐29]. Chaudhari et al used deep learning CNNs to generate thin-slice knee MR images from thicker input slices [27]. Moreover, different deep learning methods have been applied to reconstruct MR images from undersampled MR data [17, 30]. Akçakaya et al used a k-space-based deep learning technique to estimate missing k-space lines from acquired k-space data [17]. Liu et al used a generative adversarial network (GAN) to enforce data consistency for a robust reconstruction of accelerated MR images [30]. In a similar approach to our study, Hammernik et al used an image-space-based technique for reconstruction of accelerated MR images with a so-called variational network [16]. The overall image quality of the variational network reconstructions with an acceleration factor of 4 was graded equal or better compared to conventional PI compressed sensing images, though anatomical structures and pathologies were not separately evaluated [16]. In accordance, we found no significant differences for the depiction of anatomical structures between CS and CSAI2x with an acceleration factor of 4.6–5.3 equating to approximately half the acquisition time of CS only. Moreover, the same abnormalities were detected in both sequences with no significant difference in the recorded diagnostic confidence. These findings suggest that CSAI2x protocols could be implemented in clinical practice without decreasing diagnostic image quality, though further validation studies are warranted to confirm these results.

In addition, we also evaluated the diagnostic image quality for CSAI3x with a threefold acceleration of acquisition times compared to the CS protocol. The depiction and diagnostic confidence for the detection of abnormalities was graded lower for most structures compared to CS. The reduction in diagnostic image quality was caused by increased image blurring, resulting in a reduced sensitivity in the detection of abnormalities. No spurious artifacts, which overlaid or mimicked a pathology, have been observed. However, it should be noted that only one peroneal tendon disorder and one bone abnormality of the talus recorded in CS were not seen in CSAI3x, while all other abnormalities were detected. These findings suggest that CSAI3x could be used in specific cases when the patient is in significant pain or only able to remain motionless for a short time.

Measured values of SNR were higher in CSAI3x, but not in CSAI2x compared to CS. For CSAI3x the higher SNR values were offset by image blurring and a related, significantly worse depiction of anatomical structures and abnormalities. This indicates that SNR and CNR values do not represent the total perceived image quality, when comparing iterative image reconstructions and image denoising. While image details and abnormalities were depicted worse in CSAI3x, no artificial details or abnormalities were observed.

Despite promising results, some limitations are pertinent to this study that should be addressed in further studies. No external standard of reference, such as arthroscopy, was available to serve as ground truth for detected pathologies of the ankle. Therefore, it was not possible to verify the diagnosed pathologies using an external standard of reference. Moreover, our series included only 30 subjects with a limited number of different pathologies and, thus, further studies including more patients are warranted to confirm these results. The aim of this study was to conduct a validation study on an internal data set. However, further studies are warranted to validate these results. Next steps include temporal validation (evaluation on a second data set from the same center) and external validation (evaluation on data from one or more other centers) [31]. To implement CSAI in the clinical workflow, it may also be beneficial to initially introduce these imaging sequences in addition to standard routine imaging protocols. This approach would allow for radiologists to develop a better understanding of CSAI sequences and when to best implement them.

In conclusion, we conducted a validation study from a single institution to evaluate a compressed sensing artificial intelligence framework and found that acquisition times can be reduced by 47% using CSAI without decreasing diagnostic image quality. Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific cases when patients are in significant pain or only able to remain motionless for a short time. This study is the foundation for further validation studies.