The effect of a post-scan processing denoising system on image quality and morphometric analysis

Kanemaru, Noriko; Takao, Hidemasa; Amemiya, Shonan; Abe, Osamu

doi:10.1016/j.neurad.2021.11.007

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…In fact, the 3D iterative imaging enhancement applied in this study has been utilized in previous brain MRI and even CT studies without the DL module. 25 , 26 On the other hand, the advantage of discerning anatomical features through extensive training on large datasets is still reserved in DL post-processing techniques. 40 Such approaches are increasingly reported to show the ability to surpass traditional image enhancement approaches, which typically sacrifice structural clarity for noise reduction.…”

Section: Discussionmentioning

confidence: 99%

“… 23 This image enhancement method was developed from the non-local means method, 24 and was previously applied in brain MRI and low-dose CT studies. 25 , 26 Convergence: Ideally, the separated real MR image should be smooth, while the noise map should follow Gaussian distribution or at least with degraded structural details. 24 , 27 Therefore, convergence criteria including patch similarity and noise variation were applied.…”

Section: Methodsmentioning

confidence: 99%

“… 23 This image enhancement method was developed from the non-local means method, 24 and was previously applied in brain MRI and low-dose CT studies. 25 , 26 …”

Section: Methodsmentioning

confidence: 99%

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Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Yao,

Jia,

Pan

et al. 2024

JMDH

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This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s). Patients and Methods: Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa. Results: Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL -3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement. Conclusion:The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Yao,

Jia,

Pan

et al. 2024

JMDH

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Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

Nagaraj,

Dillman,

Tkach

et al. 2024

Neuroradiology

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Purpose To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction. Materials and methods This prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed. Results AI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5. Conclusions AI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.

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Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters

Rebsamen¹,

Capiglioni²,

Hoepner³

et al. 2024

Journal of Neuroradiology

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The effect of a post-scan processing denoising system on image quality and morphometric analysis

Cited by 6 publications

References 24 publications

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters

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