DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT. Article Swipe
Md Ashequr Rahman
,
Zitong Yu
,
Richard Laforest
,
Craig K. Abbey
,
Barry A. Siegel
,
Abhinav K. Jha
·
YOU?
·
· 2023
· Open Access
·
YOU?
·
· 2023
· Open Access
·
There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.
Related Topics
Concepts
Artificial intelligence
Computer science
Noise reduction
Observer (physics)
Pattern recognition (psychology)
Computer vision
Metric (unit)
Fidelity
Receiver operating characteristic
Similarity (geometry)
Myocardial perfusion imaging
Perfusion
Image (mathematics)
Machine learning
Medicine
Radiology
Economics
Operations management
Physics
Telecommunications
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/37332570
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381158296
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381158296Canonical identifier for this work in OpenAlex
- Title
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DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-26Full publication date if available
- Authors
-
Md Ashequr Rahman, Zitong Yu, Richard Laforest, Craig K. Abbey, Barry A. Siegel, Abhinav K. JhaList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/37332570Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/10274935Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Noise reduction, Observer (physics), Pattern recognition (psychology), Computer vision, Metric (unit), Fidelity, Receiver operating characteristic, Similarity (geometry), Myocardial perfusion imaging, Perfusion, Image (mathematics), Machine learning, Medicine, Radiology, Economics, Operations management, Physics, Telecommunications, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.such | 23 |
| abstract_inverted_index.task | 34, 94 |
| abstract_inverted_index.that | 24, 81, 213, 217 |
| abstract_inverted_index.this | 41 |
| abstract_inverted_index.time | 22 |
| abstract_inverted_index.upon | 45 |
| abstract_inverted_index.used | 169 |
| abstract_inverted_index.were | 176 |
| abstract_inverted_index.with | 103, 152, 166, 178 |
| abstract_inverted_index.(MPI) | 12 |
| abstract_inverted_index.12.5% | 128 |
| abstract_inverted_index.338). | 118 |
| abstract_inverted_index.SPECT | 13, 68 |
| abstract_inverted_index.There | 0 |
| abstract_inverted_index.based | 181 |
| abstract_inverted_index.build | 44 |
| abstract_inverted_index.curve | 148 |
| abstract_inverted_index.error | 203 |
| abstract_inverted_index.human | 55 |
| abstract_inverted_index.index | 207 |
| abstract_inverted_index.lower | 17 |
| abstract_inverted_index.need, | 42 |
| abstract_inverted_index.noise | 225 |
| abstract_inverted_index.study | 102 |
| abstract_inverted_index.tasks | 221 |
| abstract_inverted_index.type. | 187 |
| abstract_inverted_index.under | 143 |
| abstract_inverted_index.using | 99, 132, 141, 199 |
| abstract_inverted_index.while | 73, 222 |
| abstract_inverted_index.(AUC). | 149 |
| abstract_inverted_index.6.25%, | 127 |
| abstract_inverted_index.DEMIST | 91, 153, 189, 214, 242 |
| abstract_inverted_index.Images | 150 |
| abstract_inverted_index.SPECT. | 249 |
| abstract_inverted_index.across | 113 |
| abstract_inverted_index.and/or | 20 |
| abstract_inverted_index.assist | 218 |
| abstract_inverted_index.defect | 186 |
| abstract_inverted_index.higher | 156 |
| abstract_inverted_index.images | 14, 27, 69, 162, 164, 196, 246 |
| abstract_inverted_index.levels | 125 |
| abstract_inverted_index.strong | 235 |
| abstract_inverted_index.system | 57 |
| abstract_inverted_index.tasks. | 87 |
| abstract_inverted_index.theory | 49 |
| abstract_inverted_index.visual | 56, 191 |
| abstract_inverted_index.Similar | 174 |
| abstract_inverted_index.address | 40 |
| abstract_inverted_index.defects | 98 |
| abstract_inverted_index.denoise | 244 |
| abstract_inverted_index.further | 238 |
| abstract_inverted_index.imaging | 11 |
| abstract_inverted_index.improve | 28 |
| abstract_inverted_index.method. | 173 |
| abstract_inverted_index.methods | 6 |
| abstract_inverted_index.metric. | 208 |
| abstract_inverted_index.patient | 183 |
| abstract_inverted_index.process | 8 |
| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.provide | 234 |
| abstract_inverted_index.results | 175, 233 |
| abstract_inverted_index.squared | 202 |
| abstract_inverted_index.studies | 112 |
| abstract_inverted_index.yielded | 154 |
| abstract_inverted_index.DL-based | 171 |
| abstract_inverted_index.acquired | 15 |
| abstract_inverted_index.analysis | 180, 211 |
| abstract_inverted_index.approach | 64 |
| abstract_inverted_index.clinical | 33, 105, 239 |
| abstract_inverted_index.commonly | 168 |
| abstract_inverted_index.compared | 158 |
| abstract_inverted_index.concepts | 46 |
| abstract_inverted_index.defects. | 38 |
| abstract_inverted_index.denoised | 151, 165 |
| abstract_inverted_index.designed | 77 |
| abstract_inverted_index.evidence | 236 |
| abstract_inverted_index.features | 80, 216 |
| abstract_inverted_index.fidelity | 192 |
| abstract_inverted_index.improved | 190, 229 |
| abstract_inverted_index.low-dose | 124, 161, 195 |
| abstract_inverted_index.observed | 177 |
| abstract_inverted_index.observer | 29, 83, 230 |
| abstract_inverted_index.patients | 108 |
| abstract_inverted_index.preserve | 79 |
| abstract_inverted_index.receiver | 145 |
| abstract_inverted_index.revealed | 212 |
| abstract_inverted_index.scanners | 115 |
| abstract_inverted_index.(DEMIST). | 70 |
| abstract_inverted_index.Detection | 61 |
| abstract_inverted_index.Hotelling | 136 |
| abstract_inverted_index.approach, | 72 |
| abstract_inverted_index.denoising | 66, 172 |
| abstract_inverted_index.detecting | 36, 96 |
| abstract_inverted_index.detection | 86, 220 |
| abstract_inverted_index.evaluated | 90 |
| abstract_inverted_index.important | 3 |
| abstract_inverted_index.improving | 223 |
| abstract_inverted_index.influence | 82 |
| abstract_inverted_index.low-count | 245 |
| abstract_inverted_index.observer. | 137 |
| abstract_inverted_index.operating | 146 |
| abstract_inverted_index.performed | 122 |
| abstract_inverted_index.perfusion | 10, 37, 97 |
| abstract_inverted_index.preserved | 215 |
| abstract_inverted_index.processed | 26 |
| abstract_inverted_index.radiation | 18 |
| abstract_inverted_index.resulting | 227 |
| abstract_inverted_index.underwent | 110 |
| abstract_inverted_index.anonymized | 104 |
| abstract_inverted_index.denoising, | 75 |
| abstract_inverted_index.evaluation | 120, 240 |
| abstract_inverted_index.myocardial | 9 |
| abstract_inverted_index.performing | 74 |
| abstract_inverted_index.quantified | 140, 198 |
| abstract_inverted_index.similarity | 206 |
| abstract_inverted_index.stratified | 179 |
| abstract_inverted_index.structural | 205 |
| abstract_inverted_index.Performance | 138 |
| abstract_inverted_index.acquisition | 21 |
| abstract_inverted_index.channelized | 135 |
| abstract_inverted_index.objectively | 89 |
| abstract_inverted_index.performance | 30, 84 |
| abstract_inverted_index.properties, | 226 |
| abstract_inverted_index.mathematical | 210 |
| abstract_inverted_index.performance. | 231 |
| abstract_inverted_index.Additionally, | 188 |
| abstract_inverted_index.corresponding | 160 |
| abstract_inverted_index.retrospective | 101 |
| abstract_inverted_index.significantly | 155 |
| abstract_inverted_index.task-agnostic | 170 |
| abstract_inverted_index.task-specific | 62 |
| abstract_inverted_index.understanding | 52 |
| abstract_inverted_index.model-observer | 48 |
| abstract_inverted_index.anthropomorphic | 134 |
| abstract_inverted_index.characteristics | 147 |
| abstract_inverted_index.deep-learning-based | 63 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |