Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Kim, E; Cho, Hyun Hae; Cho, Seung Hyun; Park, Byung-Geon; Hong, Joo-Heon; Shin, Kyung Min; Hwang, Mun Kyung; You, Sun Kyoung; Lee, So Mi

doi:10.3174/ajnr.a7664

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Measurement of image SNR by two readers also indicated that TSE-DL had improved SNR compared with TSE-SD. These results are consistent with previous studies [ 13 , 18 , 19 ]. For instance, Bash et al [ 13 ] enrolled 61 patients undergoing lumbar spine MRI and found that the SNR of fast DL imaging sequences was better than that of standard sequences.…”

Section: Discussionsupporting

confidence: 94%

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

Tang,

Hong,

et al. 2024

Eur Radiol Exp

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Background We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies. Methods This prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used. Results Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081). Conclusions TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes. Relevance statement Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies. Key points • Lumbar spine MRI with deep learning reconstruction has broad application prospects. • Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality. • When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies. Graphical Abstract

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Section: Discussionsupporting

confidence: 94%

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

Tang,

Hong,

et al. 2024

Eur Radiol Exp

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“…To our knowledge, this is the first application of the evaluation of AIR DLR in both MLGE and PSLGE with different NR levels. Our results are consistent with those of previous studies in which AIR DLR obtained better image quality compared to the conventional reconstruction method (23–27). The subtracted images also showed that DLR is effective in reducing noise and truncating artifacts.…”

Section: Discussionsupporting

confidence: 93%

Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction

Yang,

Wang,

Wang

et al. 2023

Acta Radiol

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Background Deep learning (DL)-based methods have been used to improve the imaging quality of magnetic resonance imaging (MRI) by denoising. Purpose To assess the effects of DL-based MR reconstruction (DLR) method on late gadolinium enhancement (LGE) image quality. Material and Methods A total of 85 patients who underwent cardiovascular magnetic resonance (CMR) examination, including LGE imaging using conventional construction and DLR with varying levels of noise reduction (NR) levels, were included. Both magnitude LGE (MLGE) and phase-sensitive LGE (PSLGE) images were reviewed independently by double-blinded observers who used a 5-point Likert scale for multiple measures regarding image quality. Meanwhile, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness of images were calculated and compared between conventional LGE imaging and DLR LGE imaging. Results Both MLGE and PSLGE with DLR at 50% and 75% noise reduction levels received significantly higher scores than conventional imaging for overall imaging quality (all P < 0.01). In addition, the SNR, CNR, and edge sharpness of all DLR LGE imaging are higher than conventional imaging (all P < 0.01). The highest subjective score and best image quality is obtained when the DLR noise reduction level is at 75%. Conclusion DLR reduced image noise while improving image contrast and sharpness in the cardiovascular LGE imaging.

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“…The visual evaluation of myelin development based on image signal intensity in conventional MRI is imprecise, and the quantitative evaluation of myelin development using diffusion tensor imaging, magnetic resonance spectroscopy, and other imaging techniques requires a long scanning time. SyMRI can quantitatively analyze the T1, T2, and PD values of the whole brain in a single scanning session, which provides a new approach to the detection and monitoring of myelin developmental abnormalities [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Quantitative assessment of preoperative brain development in pediatric congenital heart disease patients by synthetic MRI

Xu,

Ma,

Zhang

et al. 2024

Insights Imaging

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Objectives This study investigated the quantitative assessment and application of Synthetic MRI (SyMRI) for preoperative brain development in children with congenital heart disease (CHD). Methods Forty-three CHD patients aged 2–24 months were prospectively included in the observation group, and 43 healthy infants were included in the control group. The SyMRI scans were processed by postprocessing software to obtain T1, T2, and PD maps. The values of T1, T2, and PD in different brain regions were compared with the scores of the five ability areas of the Gesell Development Scale by Pearson correlation analysis. Results In the observation group, the T1 values of the posterior limb of the internal capsule (PLIC), Optic radiation (PTR), cerebral peduncle, centrum semiovale, occipital white matter, temporal white matter, and dentate nucleus were greater than those in the control group. In the observation group, the T2 values of the PLIC, PTR, frontal white matter, occipital white matter, temporal white matter, and dentate nucleus were greater than those in the control group. Pearson correlation analysis revealed that the observation group had significantly lower Development Scale scores. In the observation group, the T2 value of the splenium of the corpus callosum was significantly positively correlated with the personal social behavior score. The AUCs for diagnosing preoperative brain developmental abnormalities in children with CHD using T1 values of the temporal white matter and dentate nucleus were both greater than 0.60. Conclusions Quantitative assessment using SyMRI can aid in the early detection of preoperative brain development abnormalities in children with CHD. Critical relevance statement T1 and T2 relaxation values from SyMRI can be considered as a quantitative imaging marker to detect abnormalities, allowing for early clinical evaluation and timely intervention, thereby reducing neurodevelopmental disorders in these children. Key Points T1 and T2 relaxation values by SyMRI are related to myelin development. Evaluated development quotient markers were lower in the observation compared to the control group. SyMRI can act as a reference indicator for brain development in CHD children. Graphical Abstract

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Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Cited by 7 publications

References 21 publications

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction

Quantitative assessment of preoperative brain development in pediatric congenital heart disease patients by synthetic MRI

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