Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower qual...

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Autores principales:	Joshua R. Harper, Venkateswararao Cherukuri, Tom O’Reilly, Mingzhao Yu, Edith Mbabazi-Kabachelor, Ronald Mulando, Kevin N. Sheth, Andrew G. Webb, Benjamin C. Warf, Abhaya V. Kulkarni, Vishal Monga, Steven J. Schiff
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Low field MRI Image quality Deep learning Risk assessment Hydrocephalus treatment planning Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/7bee984ac6794442a7c5a4c0b4bfc7c0
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spelling	oai:doaj.org-article:7bee984ac6794442a7c5a4c0b4bfc7c02021-12-04T04:34:01ZAssessing the utility of low resolution brain imaging: treatment of infant hydrocephalus2213-158210.1016/j.nicl.2021.102896https://doaj.org/article/7bee984ac6794442a7c5a4c0b4bfc7c02021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2213158221003405https://doaj.org/toc/2213-1582As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.Joshua R. HarperVenkateswararao CherukuriTom O’ReillyMingzhao YuEdith Mbabazi-KabachelorRonald MulandoKevin N. ShethAndrew G. WebbBenjamin C. WarfAbhaya V. KulkarniVishal MongaSteven J. SchiffElsevierarticleLow field MRIImage qualityDeep learningRisk assessmentHydrocephalus treatment planningComputer applications to medicine. Medical informaticsR858-859.7Neurology. Diseases of the nervous systemRC346-429ENNeuroImage: Clinical, Vol 32, Iss , Pp 102896- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Low field MRI Image quality Deep learning Risk assessment Hydrocephalus treatment planning Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Low field MRI Image quality Deep learning Risk assessment Hydrocephalus treatment planning Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429 Joshua R. Harper Venkateswararao Cherukuri Tom O’Reilly Mingzhao Yu Edith Mbabazi-Kabachelor Ronald Mulando Kevin N. Sheth Andrew G. Webb Benjamin C. Warf Abhaya V. Kulkarni Vishal Monga Steven J. Schiff Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
description	As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.
format	article
author	Joshua R. Harper Venkateswararao Cherukuri Tom O’Reilly Mingzhao Yu Edith Mbabazi-Kabachelor Ronald Mulando Kevin N. Sheth Andrew G. Webb Benjamin C. Warf Abhaya V. Kulkarni Vishal Monga Steven J. Schiff
author_facet	Joshua R. Harper Venkateswararao Cherukuri Tom O’Reilly Mingzhao Yu Edith Mbabazi-Kabachelor Ronald Mulando Kevin N. Sheth Andrew G. Webb Benjamin C. Warf Abhaya V. Kulkarni Vishal Monga Steven J. Schiff
author_sort	Joshua R. Harper
title	Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
title_short	Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
title_full	Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
title_fullStr	Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
title_full_unstemmed	Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
title_sort	assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7bee984ac6794442a7c5a4c0b4bfc7c0
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Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus

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