Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study Article Swipe

PDF

Piotr Jarosik , Michał Byra , Marcin Lewandowski , Ziemowit Klimonda ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2205.09533

Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics. In this work, we investigate the feasibility of using convolutional neural networks (CNNs) to directly estimate AC from radio-frequency (RF) ultrasound signals. To develop the CNNs we used RF signals collected from tissue mimicking numerical phantoms for the AC values in a range from 0.1 to 1.5 dB/(MHz*cm). The models were trained based on 1-D patches of RF data. We obtained mean absolute AC estimation errors of 0.08, 0.12, 0.20, 0.25 for the patch lengths: 10 mm, 5 mm, 2 mm and 1 mm, respectively. We explain the performance of the model by visualizing the frequency content associated with convolutional filters. Our study presents that the AC can be calculated using deep learning, and the weights of the CNNs can have physical interpretation.

Related Topics

Convolutional Neural Network

Artificial Intelligence

Concepts

Convolutional neural network Attenuation Attenuation coefficient Ultrasound Medical ultrasound Radio frequency Computer science Correction for attenuation Range (aeronautics) Acoustics Artificial intelligence Pattern recognition (psychology) Physics Materials science Optics Telecommunications Composite material

Metadata

Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2205.09533
PDF: https://arxiv.org/pdf/2205.09533
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4281255691

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4281255691

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2205.09533

Digital Object Identifier
Title: Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2022

Year of publication
Publication date: 2022-05-19

Full publication date if available
Authors: Piotr Jarosik, Michał Byra, Marcin Lewandowski, Ziemowit Klimonda

List of authors in order
Landing page: https://arxiv.org/abs/2205.09533

Publisher landing page
PDF URL: https://arxiv.org/pdf/2205.09533

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2205.09533

Direct OA link when available
Concepts: Convolutional neural network, Attenuation, Attenuation coefficient, Ultrasound, Medical ultrasound, Radio frequency, Computer science, Correction for attenuation, Range (aeronautics), Acoustics, Artificial intelligence, Pattern recognition (psychology), Physics, Materials science, Optics, Telecommunications, Composite material

Top concepts (fields/topics) attached by OpenAlex
Cited by: 0

Total citation count in OpenAlex
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4281255691
doi	https://doi.org/10.48550/arxiv.2205.09533
ids.doi	https://doi.org/10.48550/arxiv.2205.09533
ids.openalex	https://openalex.org/W4281255691
fwci
type	preprint
title	Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
topics[0].id	https://openalex.org/T10727
topics[0].field.id	https://openalex.org/fields/27
topics[0].field.display_name	Medicine
topics[0].score	0.9976000189781189
topics[0].domain.id	https://openalex.org/domains/4
topics[0].domain.display_name	Health Sciences
topics[0].subfield.id	https://openalex.org/subfields/2741
topics[0].subfield.display_name	Radiology, Nuclear Medicine and Imaging
topics[0].display_name	Ultrasound Imaging and Elastography
topics[1].id	https://openalex.org/T10958
topics[1].field.id	https://openalex.org/fields/22
topics[1].field.display_name	Engineering
topics[1].score	0.9887999892234802
topics[1].domain.id	https://openalex.org/domains/3
topics[1].domain.display_name	Physical Sciences
topics[1].subfield.id	https://openalex.org/subfields/2204
topics[1].subfield.display_name	Biomedical Engineering
topics[1].display_name	Ultrasound and Hyperthermia Applications
topics[2].id	https://openalex.org/T11738
topics[2].field.id	https://openalex.org/fields/27
topics[2].field.display_name	Medicine
topics[2].score	0.9840999841690063
topics[2].domain.id	https://openalex.org/domains/4
topics[2].domain.display_name	Health Sciences
topics[2].subfield.id	https://openalex.org/subfields/2706
topics[2].subfield.display_name	Critical Care and Intensive Care Medicine
topics[2].display_name	Ultrasound in Clinical Applications
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C81363708
concepts[0].level	2
concepts[0].score	0.8118594884872437
concepts[0].wikidata	https://www.wikidata.org/wiki/Q17084460
concepts[0].display_name	Convolutional neural network
concepts[1].id	https://openalex.org/C184652730
concepts[1].level	2
concepts[1].score	0.7954086065292358
concepts[1].wikidata	https://www.wikidata.org/wiki/Q2357982
concepts[1].display_name	Attenuation
concepts[2].id	https://openalex.org/C159774933
concepts[2].level	2
concepts[2].score	0.583556592464447
concepts[2].wikidata	https://www.wikidata.org/wiki/Q902086
concepts[2].display_name	Attenuation coefficient
concepts[3].id	https://openalex.org/C143753070
concepts[3].level	2
concepts[3].score	0.5657078623771667
concepts[3].wikidata	https://www.wikidata.org/wiki/Q162564
concepts[3].display_name	Ultrasound
concepts[4].id	https://openalex.org/C2992165143
concepts[4].level	3
concepts[4].score	0.5326473712921143
concepts[4].wikidata	https://www.wikidata.org/wiki/Q234904
concepts[4].display_name	Medical ultrasound
concepts[5].id	https://openalex.org/C74064498
concepts[5].level	2
concepts[5].score	0.5192134976387024
concepts[5].wikidata	https://www.wikidata.org/wiki/Q3396184
concepts[5].display_name	Radio frequency
concepts[6].id	https://openalex.org/C41008148
concepts[6].level	0
concepts[6].score	0.46592944860458374
concepts[6].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[6].display_name	Computer science
concepts[7].id	https://openalex.org/C123688308
concepts[7].level	3
concepts[7].score	0.4433121085166931
concepts[7].wikidata	https://www.wikidata.org/wiki/Q7309537
concepts[7].display_name	Correction for attenuation
concepts[8].id	https://openalex.org/C204323151
concepts[8].level	2
concepts[8].score	0.42328178882598877
concepts[8].wikidata	https://www.wikidata.org/wiki/Q905424
concepts[8].display_name	Range (aeronautics)
concepts[9].id	https://openalex.org/C24890656
concepts[9].level	1
concepts[9].score	0.4195711016654968
concepts[9].wikidata	https://www.wikidata.org/wiki/Q82811
concepts[9].display_name	Acoustics
concepts[10].id	https://openalex.org/C154945302
concepts[10].level	1
concepts[10].score	0.36552944779396057
concepts[10].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[10].display_name	Artificial intelligence
concepts[11].id	https://openalex.org/C153180895
concepts[11].level	2
concepts[11].score	0.3468497693538666
concepts[11].wikidata	https://www.wikidata.org/wiki/Q7148389
concepts[11].display_name	Pattern recognition (psychology)
concepts[12].id	https://openalex.org/C121332964
concepts[12].level	0
concepts[12].score	0.31904473900794983
concepts[12].wikidata	https://www.wikidata.org/wiki/Q413
concepts[12].display_name	Physics
concepts[13].id	https://openalex.org/C192562407
concepts[13].level	0
concepts[13].score	0.29619383811950684
concepts[13].wikidata	https://www.wikidata.org/wiki/Q228736
concepts[13].display_name	Materials science
concepts[14].id	https://openalex.org/C120665830
concepts[14].level	1
concepts[14].score	0.22154685854911804
concepts[14].wikidata	https://www.wikidata.org/wiki/Q14620
concepts[14].display_name	Optics
concepts[15].id	https://openalex.org/C76155785
concepts[15].level	1
concepts[15].score	0.12668120861053467
concepts[15].wikidata	https://www.wikidata.org/wiki/Q418
concepts[15].display_name	Telecommunications
concepts[16].id	https://openalex.org/C159985019
concepts[16].level	1
concepts[16].score	0.0
concepts[16].wikidata	https://www.wikidata.org/wiki/Q181790
concepts[16].display_name	Composite material
keywords[0].id	https://openalex.org/keywords/convolutional-neural-network
keywords[0].score	0.8118594884872437
keywords[0].display_name	Convolutional neural network
keywords[1].id	https://openalex.org/keywords/attenuation
keywords[1].score	0.7954086065292358
keywords[1].display_name	Attenuation
keywords[2].id	https://openalex.org/keywords/attenuation-coefficient
keywords[2].score	0.583556592464447
keywords[2].display_name	Attenuation coefficient
keywords[3].id	https://openalex.org/keywords/ultrasound
keywords[3].score	0.5657078623771667
keywords[3].display_name	Ultrasound
keywords[4].id	https://openalex.org/keywords/medical-ultrasound
keywords[4].score	0.5326473712921143
keywords[4].display_name	Medical ultrasound
keywords[5].id	https://openalex.org/keywords/radio-frequency
keywords[5].score	0.5192134976387024
keywords[5].display_name	Radio frequency
keywords[6].id	https://openalex.org/keywords/computer-science
keywords[6].score	0.46592944860458374
keywords[6].display_name	Computer science
keywords[7].id	https://openalex.org/keywords/correction-for-attenuation
keywords[7].score	0.4433121085166931
keywords[7].display_name	Correction for attenuation
keywords[8].id	https://openalex.org/keywords/range
keywords[8].score	0.42328178882598877
keywords[8].display_name	Range (aeronautics)
keywords[9].id	https://openalex.org/keywords/acoustics
keywords[9].score	0.4195711016654968
keywords[9].display_name	Acoustics
keywords[10].id	https://openalex.org/keywords/artificial-intelligence
keywords[10].score	0.36552944779396057
keywords[10].display_name	Artificial intelligence
keywords[11].id	https://openalex.org/keywords/pattern-recognition
keywords[11].score	0.3468497693538666
keywords[11].display_name	Pattern recognition (psychology)
keywords[12].id	https://openalex.org/keywords/physics
keywords[12].score	0.31904473900794983
keywords[12].display_name	Physics
keywords[13].id	https://openalex.org/keywords/materials-science
keywords[13].score	0.29619383811950684
keywords[13].display_name	Materials science
keywords[14].id	https://openalex.org/keywords/optics
keywords[14].score	0.22154685854911804
keywords[14].display_name	Optics
keywords[15].id	https://openalex.org/keywords/telecommunications
keywords[15].score	0.12668120861053467
keywords[15].display_name	Telecommunications
language	en
locations[0].id	pmh:oai:arXiv.org:2205.09533
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2205.09533
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2205.09533
locations[1].id	doi:10.48550/arxiv.2205.09533
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license	cc-by
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id	https://openalex.org/licenses/cc-by
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2205.09533
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5016281068
authorships[0].author.orcid	https://orcid.org/0000-0001-5198-5012
authorships[0].author.display_name	Piotr Jarosik
authorships[0].author_position	first
authorships[0].raw_author_name	Jarosik, Piotr
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5084262755
authorships[1].author.orcid	https://orcid.org/0000-0002-5759-2516
authorships[1].author.display_name	Michał Byra
authorships[1].author_position	middle
authorships[1].raw_author_name	Byra, Michal
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5014928358
authorships[2].author.orcid	https://orcid.org/0000-0001-8668-8368
authorships[2].author.display_name	Marcin Lewandowski
authorships[2].author_position	middle
authorships[2].raw_author_name	Lewandowski, Marcin
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5047883236
authorships[3].author.orcid	https://orcid.org/0000-0003-1695-7096
authorships[3].author.display_name	Ziemowit Klimonda
authorships[3].author_position	last
authorships[3].raw_author_name	Klimonda, Ziemowit
authorships[3].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2205.09533
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2022-05-23T00:00:00
display_name	Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic.id	https://openalex.org/T10727
primary_topic.field.id	https://openalex.org/fields/27
primary_topic.field.display_name	Medicine
primary_topic.score	0.9976000189781189
primary_topic.domain.id	https://openalex.org/domains/4
primary_topic.domain.display_name	Health Sciences
primary_topic.subfield.id	https://openalex.org/subfields/2741
primary_topic.subfield.display_name	Radiology, Nuclear Medicine and Imaging
primary_topic.display_name	Ultrasound Imaging and Elastography
related_works	https://openalex.org/W2334000142, https://openalex.org/W2919843379, https://openalex.org/W2534086599, https://openalex.org/W2288985746, https://openalex.org/W167525318, https://openalex.org/W1969031584, https://openalex.org/W2539843374, https://openalex.org/W2316653139, https://openalex.org/W1972843425, https://openalex.org/W2539802858
cited_by_count	0
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2205.09533
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2205.09533
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2205.09533
primary_location.id	pmh:oai:arXiv.org:2205.09533
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2205.09533
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2205.09533
publication_date	2022-05-19
publication_year	2022
referenced_works_count	0
abstract_inverted_index.1	100
abstract_inverted_index.2	97
abstract_inverted_index.5	95
abstract_inverted_index.a	4, 59
abstract_inverted_index.10	93
abstract_inverted_index.AC	34, 56, 81, 124
abstract_inverted_index.In	18
abstract_inverted_index.RF	46, 75
abstract_inverted_index.To	40
abstract_inverted_index.We	77, 103
abstract_inverted_index.be	13, 126
abstract_inverted_index.by	110
abstract_inverted_index.in	15, 58
abstract_inverted_index.is	3
abstract_inverted_index.mm	98
abstract_inverted_index.of	7, 25, 74, 84, 107, 134
abstract_inverted_index.on	71
abstract_inverted_index.to	31, 63
abstract_inverted_index.we	21, 44
abstract_inverted_index.0.1	62
abstract_inverted_index.1-D	72
abstract_inverted_index.1.5	64
abstract_inverted_index.Our	119
abstract_inverted_index.The	66
abstract_inverted_index.and	99, 131
abstract_inverted_index.can	12, 125, 137
abstract_inverted_index.for	54, 89
abstract_inverted_index.mm,	94, 96, 101
abstract_inverted_index.the	23, 42, 55, 90, 105, 108, 112, 123, 132, 135
abstract_inverted_index.(AC)	2
abstract_inverted_index.(RF)	37
abstract_inverted_index.0.25	88
abstract_inverted_index.CNNs	43, 136
abstract_inverted_index.deep	129
abstract_inverted_index.from	35, 49, 61
abstract_inverted_index.have	138
abstract_inverted_index.mean	79
abstract_inverted_index.that	122
abstract_inverted_index.this	19
abstract_inverted_index.used	14, 45
abstract_inverted_index.were	68
abstract_inverted_index.with	116
abstract_inverted_index.0.08,	85
abstract_inverted_index.0.12,	86
abstract_inverted_index.0.20,	87
abstract_inverted_index.based	70
abstract_inverted_index.data.	76
abstract_inverted_index.model	109
abstract_inverted_index.patch	91
abstract_inverted_index.range	60
abstract_inverted_index.study	120
abstract_inverted_index.using	26, 128
abstract_inverted_index.which	11
abstract_inverted_index.work,	20
abstract_inverted_index.(CNNs)	30
abstract_inverted_index.errors	83
abstract_inverted_index.models	67
abstract_inverted_index.neural	28
abstract_inverted_index.tissue	8, 50
abstract_inverted_index.values	57
abstract_inverted_index.content	114
abstract_inverted_index.develop	41
abstract_inverted_index.explain	104
abstract_inverted_index.measure	6
abstract_inverted_index.medical	16
abstract_inverted_index.patches	73
abstract_inverted_index.signals	47
abstract_inverted_index.trained	69
abstract_inverted_index.weights	133
abstract_inverted_index.absolute	80
abstract_inverted_index.directly	32
abstract_inverted_index.estimate	33
abstract_inverted_index.filters.	118
abstract_inverted_index.lengths:	92
abstract_inverted_index.networks	29
abstract_inverted_index.obtained	78
abstract_inverted_index.phantoms	53
abstract_inverted_index.physical	139
abstract_inverted_index.presents	121
abstract_inverted_index.signals.	39
abstract_inverted_index.collected	48
abstract_inverted_index.frequency	113
abstract_inverted_index.learning,	130
abstract_inverted_index.mimicking	51
abstract_inverted_index.numerical	52
abstract_inverted_index.acoustical	9
abstract_inverted_index.associated	115
abstract_inverted_index.calculated	127
abstract_inverted_index.estimation	82
abstract_inverted_index.ultrasound	38
abstract_inverted_index.Attenuation	0
abstract_inverted_index.coefficient	1
abstract_inverted_index.feasibility	24
abstract_inverted_index.fundamental	5
abstract_inverted_index.investigate	22
abstract_inverted_index.performance	106
abstract_inverted_index.properties,	10
abstract_inverted_index.visualizing	111
abstract_inverted_index.dB/(MHz*cm).	65
abstract_inverted_index.diagnostics.	17
abstract_inverted_index.convolutional	27, 117
abstract_inverted_index.respectively.	102
abstract_inverted_index.interpretation.	140
abstract_inverted_index.radio-frequency	36
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile