ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning

Jimeno, Marina Manso; Ravi, Keerthi Sravan; Jin, Zhezhen; Oyekunle, Dotun; Ogbole, Godwin; Geethanath, Sairam

doi:10.1016/j.mri.2022.02.002

Cited by 19 publications

(19 citation statements)

References 24 publications

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“…Deep learning reconstruction methods use neural networks to learn robust transformation mappings from sensor space to the image domain. Image postprocessing has also benefited from deep learning, with applications in super‐resolution, 26–28 segmentation, 29 simulation, 30 denoising, 31 and artifact rejection 32 . However, analytical software development typically lags hardware advances.…”

Section: Hardware and Software Advancesmentioning

confidence: 99%

“…Image postprocessing has also benefited from deep learning, with applications in super‐resolution, 26 , 27 , 28 segmentation, 29 simulation, 30 denoising, 31 and artifact rejection. 32 However, analytical software development typically lags hardware advances. It may take several years for some software commonly used at high‐field to be adapted to low‐field scanners.…”

Section: Hardware and Software Advancesmentioning

confidence: 99%

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Low‐field MRI: Clinical promise and challenges

Arnold

Freeman

Litt

et al. 2022

Magnetic Resonance Imaging

121

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Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world.

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Section: Hardware and Software Advancesmentioning

confidence: 99%

Section: Hardware and Software Advancesmentioning

confidence: 99%

Low‐field MRI: Clinical promise and challenges

Arnold

Freeman

Litt

et al. 2022

Magnetic Resonance Imaging

121

View full text Add to dashboard Cite

show abstract

“…Dice scores were used for evaluating the segmentation results [141]. Grad-CAM was used for visual explanations of the wrap-around model in low-field brain MRI images on two datasets showing an agreement between the radiologists and the models using Cohen's kappa values [154].…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

101

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“…For instance, a deep learning-based tool called ArtifactID has been developed to help radiologists identify and classify artifacts in low resource settings. The researchers have trained classification models with greater than 88% accuracy to identify artifacts in T1 brain images (Manso Jimeno et al, 2022). Deep learning integration would be especially useful in developing countries where a lack of skilled physicians results in scan repetition, increased operating time, increased costs, and occasional misdiagnosis (Manso Jimeno et al, 2022).…”

Section: Efficient Analysis Of Datamentioning

confidence: 99%

“…The researchers have trained classification models with greater than 88% accuracy to identify artifacts in T1 brain images (Manso Jimeno et al, 2022). Deep learning integration would be especially useful in developing countries where a lack of skilled physicians results in scan repetition, increased operating time, increased costs, and occasional misdiagnosis (Manso Jimeno et al, 2022). Overall, AI would greatly improve the productivity and efficiency in clinical settings.…”

Section: Efficient Analysis Of Datamentioning

confidence: 99%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning

Cited by 19 publications

References 24 publications

Low‐field MRI: Clinical promise and challenges

Low‐field MRI: Clinical promise and challenges

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

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