Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol

Magnetic resonance imaging (MRI) of the knee is among the most commonly performed MRI examinations and requires about 15 min of acquisition time. Reference standards for knee MRI are proton density (PD)– and T1–weighted turbo spin-echo (TSE) sequences due to the excellent tissue contrast and high in-plane spatial resolution with good assessment of meniscal, ligamentous, and cartilaginous injuries [1‐3].

Due to their anisotropic voxel size, two-dimensional (2D)–TSE sequences require the acquisition of different image planes separately, which is time consuming [4, 5]. One approach to accelerate MRI of the knee is three-dimensional (3D)–TSE techniques, generating isotropic data sets of higher spatial resolution to create virtually any image plane from a single parental data set [4, 6]. Although technical developments can provide accelerated imaging, mainly based on parallel imaging (PI) acceleration, the acquisition time of a high-quality isotropic data set with 3D–TSE requires still around 5 to 10 min [4, 7]. Besides, with increasing acceleration in PI, the signal-to-noise ratio (SNR) decreases rapidly, while residual artifacts are generally increased which limits the achievable speed [8].

Another innovative technique, which is commonly used to accelerate MRI, is compressed sensing (CS), in which only a reduced set of data points is required. SNR is preserved better than by PI only, but CS tends to oversimplify image content, resulting in residual blurring and loss of realistic image textures.

The latest promising approaches to overcome this drawback are deep-learning (DL) algorithms. These feature trainable components in contrast to a priori assumptions on sparsity and promise higher acceleration factors while simultaneously increasing SNR and preserving high image quality [9, 10]. With regard to MRI of the knee, a recently published study using retrospectively undersampled data showed that DL images perform interchangeably with standard clinical images for the detection of internal derangement of the knee [8]. Furthermore, retrospectively undersampled, DL–accelerated images were rated with higher image quality than standard imaging and allowed an acceleration of the standard images [8]. There have been other technical developments of the DL reconstruction recently, but so far, there has been no prospective clinical study at both 1.5 and 3 T.

Our hypothesis was that TSE_DL can produce similar image quality that is comparable to clinically used segmented sequences while significantly reducing the acquisition time. Therefore, we implemented TSE_DL at 1.5 and 3 T in a prospective study to assess diagnostic performance compared to standard imaging sequences in routine clinical practice.

Among 72 eligible participants, a final sample of 60 participants (84%, mean age 44 ± 17; range 18–85 (years); 29 males, 31 females) were prospectively included in this study. Thirty examinations were performed on 1.5 T and 30 examinations on 3 T regardless of diagnosis, current treatment, first examination, or follow-up (Table 1 and Fig. 1).

In this study, we investigated the feasibility and performance of a deep-learning-based reconstruction for 2D–TSE sequences (TSE_DL) compared to standard 2D-TSE sequences concerning overall image quality items and the diagnosis of internal derangement of the knee at 1.5 T and 3 T. TSE_DL enables a robust and reliable acquisition of images in clinical routine practice, providing even higher overall image quality and equal diagnostic performance compared to TSE_S in a short acquisition time.

The current clinical standard for MRI examinations of the knee is a multi-plane 2D-TSE sequence, which is, due to its multiple planes and contrasts, time consuming, with an acquisition time of about 15 min. Several approaches have been made to accelerate knee imaging, especially promising 3D sequences such as 3D-TSE or 3D-SPACE [4, 7, 17] with the ability to create any imaging plane and slice thickness from a single volume. Regardless, the inverse relationship between acquisition time and image quality leads to relatively long acquisition times of about 10 min for small voxel sizes of (0.5 mm)³ [4, 7]. Small voxel sizes are needed to ensure the visibility of fine anatomic details and interplanar uniformity of reconstructions. Although several studies indicate the equality or even superiority of 3D sequences [7, 17‐19], this technique has not yet been widely adopted in clinical practice and most study protocols consisted exclusively of PD-weighted images [7].

For the current standard 2D-TSE imaging of the knee, other acceleration techniques have been used, such as PI, CS, and simultaneous multi-slice [20‐24]. Diagnostic equivalence can be obtained when using acceleration factors up to twofold. However, PI and simultaneous multi-slice may suffer from reduced SNR, noise enhancement, aliasing, and reconstruction artifacts, especially if higher acceleration factors are used [25, 26]. The immense potential of AI-based reconstruction techniques, such as deep learning, to accelerate MRI while maintaining or even improving the image quality, had been shown in several studies [8, 27‐31]. According to these, in our study, TSE_DL enabled an improvement of the overall image quality and significantly reduced the extent of noise, especially for images acquired at 1.5 T. The acquisition time of a knee MRI can be reduced to 6:11 min using TSE_DL compared to 11:56 min for our standard protocol using TSE_S. Even though the extent of general artifacts showed no difference between TSE_S and TSE_DL, banding artifacts in images acquired at 1.5 T were present, which have been observed with multiple, different deep-learning reconstruction techniques [16]. They have been correlated to the Cartesian sampling scheme with integrated reference scans and are particularly strong in low signal-to-noise regions of the reconstructed image. As such, images acquired at 1.5 T and image contrasts with fat suppression are known to be more prone to banding artifacts (Figs. 7, 8 and 9). Coincidently, our PD protocols employed spectral fat suppression and therefore were more affected by banding artifacts. Recent approaches have shown promising results to reduce such banding artifacts [16]. However, although banding artifacts are present in TSE_DL and need to be reduced in further developments of the used network, they do not affect the diagnostic confidence of TSE_DL.

Concerning the detection of internal derangement, there was no substantial difference between the TSE_S and TSE_DL sequences. Although intra- and inter-reader agreement for the presence of cartilage defects showed lower κ values, it would not have led to any change in therapy of the participants, and can be explained by the subjective reading, what is already described in literature [32].

With regard to the acquisition time of the MRI, in addition to the acceleration of the data acquisition, there is also another advantage compared to previously used acceleration techniques such as CS: Up to now, acceleration techniques suffered from long post-processing times and the need of high computational resources [33, 34]. The deep-learning approach stands out, due to the fact that most of the computational work has been done in advance during training of the network; thus, the reconstruction time of deep-learning-based sequences is very low.

Our findings should be interpreted within the context of the study’s limitations. First, while all readers were blinded to the shown sequences, the characteristic differences in the appearance allowed readers to recognize the reconstruction technique. Therefore, personal preferences may have influenced the study results. Second, in this study, just one network was used to reconstruct the undersampled image data, and this network was trained on various anatomic regions. Further improvements of the used first network have already been done and should be evaluated in further studies, especially with regard to the extent of banding artifacts at images of 1.5-T scanners. Third, all examinations were performed on MRI scanners produced by a single vendor. Further studies on multiple-vendor scanners are needed evaluating the performance of this network also with regard to other anatomic regions to entirely assess the generalizability of this technique.

In conclusion, our study indicates that TSE_DL is clinically feasible, providing even better image quality in a shorter acquisition time. Dependent on its ability to accurately reconstruct meniscus and ligament tears, TSE_DL yields comparable diagnostic performance for internal knee derangement to standard TSE.