Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction

Chazen, J. Levi; Tan, Ek Tsoon; Fiore, Jake; Nguyen, Joseph; Sun, Shangpeng; Sneag, Darryl B.

doi:10.1007/s00256-022-04268-2

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…With its high spatial resolution and ability to provide multiplanar reformations, 3D imaging enables precise evaluation of degenerative spinal conditions. [39][40][41] Recent research has shown that incorporating AI-enhanced DL reconstruction with Dixon imaging can significantly reduce the scan time for 3D imaging of the lumbar spine by 54%, as demonstrated in a study by Chazen et al 42 Despite the encouraging findings, further studies are needed to validate the effectiveness of 3D imaging with AI reconstruction as a routine and valuable option in future spine imaging protocols.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic evaluation of deep learning accelerated lumbar spine MRI

Awan,

Goncalves Filho,

Tabari

et al. 2024

Neuroradiol J

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Background and Purpose Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. Methods We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. Results No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50–0.76). Conclusion Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.

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Section: Discussionmentioning

confidence: 99%

Diagnostic evaluation of deep learning accelerated lumbar spine MRI

Awan,

Goncalves Filho,

Tabari

et al. 2024

Neuroradiol J

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“…AIR TM Recon DL is a deep-learning-based reconstruction method for improving image sharpness by removing truncation artifacts while jointly denoising the image to improve its quality. 16 , 17 , 18 It applies a convolutional neural network (CNN) in an image-reconstruction pipeline using raw k-space data to generate high-fidelity images. The CNN is trained in a supervised manner to generate high-resolution data with minimal ringing artifacts and very low noise levels.…”

Section: Methodsmentioning

confidence: 99%

“…16 The AIR TM Recon DL, which was originally designed for 2D imaging, was extended to 3D to reduce noise and ringing in all three directions, thus improving both SNR and spatial resolution. 17 , 18 The vendor-provided AIR TM Recon DL 3D prototype was applied offline to the raw k-space data following image acquisition. The prototype DLR allowed for tunable noise-reduction levels (25%, 50%, and 75%, with higher levels corresponding to greater denoising).…”

Section: Methodsmentioning

confidence: 99%

LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn’s disease: utility in noise reduction and image quality improvement

Son¹,

Lee²,

Lee³

et al. 2023

dir

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PURPOSE This study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms of image quality. METHODS A total of 35 patients who underwent MRE for Crohn’s disease between August 2021 and February 2022 were included in this retrospective study. The enteric phase CE-T1W MRE images of each patient were reconstructed with conventional reconstruction and no image filter (original), with conventional reconstruction and image filter (filtered), and with a prototype version of AIR TM Recon DL 3D (DLR), which were then reformatted into the axial plane to generate six image sets per patient. Two radiologists independently assessed the images for overall image quality, contrast, sharpness, presence of motion artifacts, blurring, and synthetic appearance for qualitative analysis, and the signal-to-noise ratio (SNR) was measured for quantitative analysis. RESULTS The mean scores of the DLR image set with respect to overall image quality, contrast, sharpness, motion artifacts, and blurring in the coronal and axial images were significantly superior to those of both the filtered and original images ( P < 0.001). However, the DLR images showed a significantly more synthetic appearance than the other two images ( P < 0.05). There was no statistically significant difference in all scores between the original and filtered images ( P > 0.05). In the quantitative analysis, the SNR was significantly increased in the order of original, filtered, and DLR images ( P < 0.001). CONCLUSION Using DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR.

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