Deep Learning De-Noising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study Article Swipe
Mahmud Mossa‐Basha
,
Chengcheng Zhu
,
Tanya Pandhi
,
Steve Mendoza
,
Javid Azadbakht
,
Ahmed Safwat
,
Dean Homen
,
Carlos Zamora
,
Dinesh Kumar Gnanasekaran
,
Ruiyue Peng
,
Steven Cen
,
Vinay Duddalwar
,
Jeffry R. Alger
,
Danny J.J. Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17615/4q4a-7d18
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17615/4q4a-7d18
BACKGROUND AND PURPOSE: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies. MATERIALS AND METHODS: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k-space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k-space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation. RESULTS: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k-space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k-space-weighted image average and RED-CNN denoising (P < .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k-space-weighted image average and then standard CTP images. CONCLUSIONS: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17615/4q4a-7d18
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403241178
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403241178Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17615/4q4a-7d18Digital Object Identifier
- Title
-
Deep Learning De-Noising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-08Full publication date if available
- Authors
-
Mahmud Mossa‐Basha, Chengcheng Zhu, Tanya Pandhi, Steve Mendoza, Javid Azadbakht, Ahmed Safwat, Dean Homen, Carlos Zamora, Dinesh Kumar Gnanasekaran, Ruiyue Peng, Steven Cen, Vinay Duddalwar, Jeffry R. Alger, Danny J.J. WangList of authors in order
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https://doi.org/10.17615/4q4a-7d18Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.17615/4q4a-7d18Direct OA link when available
- Concepts
-
Dosing, Contrast (vision), Image quality, Artificial intelligence, Perfusion, Computer science, Nuclear medicine, Medicine, Perfusion scanning, Image (mathematics), Radiology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.image | 22, 50, 160, 173, 189, 223, 264, 286, 298, 321 |
| abstract_inverted_index.lower | 17, 250 |
| abstract_inverted_index.maps, | 234 |
| abstract_inverted_index.media | 35, 95, 112, 131, 142, 204, 256, 304, 313 |
| abstract_inverted_index.rated | 275 |
| abstract_inverted_index.study | 30 |
| abstract_inverted_index.swine | 74 |
| abstract_inverted_index.using | 158 |
| abstract_inverted_index.which | 258 |
| abstract_inverted_index.while | 20 |
| abstract_inverted_index.x-ray | 86 |
| abstract_inverted_index.Eleven | 125 |
| abstract_inverted_index.Twelve | 73 |
| abstract_inverted_index.animal | 178, 195 |
| abstract_inverted_index.before | 106 |
| abstract_inverted_index.change | 116 |
| abstract_inverted_index.doses. | 207 |
| abstract_inverted_index.dosing | 19 |
| abstract_inverted_index.during | 108 |
| abstract_inverted_index.effect | 58, 214 |
| abstract_inverted_index.images | 164, 176, 197, 247 |
| abstract_inverted_index.impact | 43 |
| abstract_inverted_index.neural | 150 |
| abstract_inverted_index.recent | 4 |
| abstract_inverted_index.waste, | 13 |
| abstract_inverted_index.RED-CNN | 226, 236, 267, 276 |
| abstract_inverted_index.average | 161, 174, 224, 265, 287 |
| abstract_inverted_index.dosages | 132, 143 |
| abstract_inverted_index.greater | 213 |
| abstract_inverted_index.highest | 240 |
| abstract_inverted_index.images. | 292 |
| abstract_inverted_index.improve | 297 |
| abstract_inverted_index.needed. | 28 |
| abstract_inverted_index.network | 151 |
| abstract_inverted_index.quality | 23, 51, 299, 322 |
| abstract_inverted_index.reduced | 32, 60, 128, 253 |
| abstract_inverted_index.studies | 181 |
| abstract_inverted_index.target. | 167 |
| abstract_inverted_index.through | 24 |
| abstract_inverted_index.trained | 154 |
| abstract_inverted_index.</i>< | 270 |
| abstract_inverted_index.Clinical | 102 |
| abstract_inverted_index.Contrast | 208 |
| abstract_inverted_index.METHODS: | 72 |
| abstract_inverted_index.PURPOSE: | 2 |
| abstract_inverted_index.RESULTS: | 191 |
| abstract_inverted_index.addition | 317 |
| abstract_inverted_index.clinical | 55, 245 |
| abstract_inverted_index.compared | 31, 183 |
| abstract_inverted_index.contrast | 6, 18, 34, 94, 111, 130, 141, 203, 255, 303, 312 |
| abstract_inverted_index.controls | 136 |
| abstract_inverted_index.denoised | 277 |
| abstract_inverted_index.filtered | 162 |
| abstract_inverted_index.followed | 283 |
| abstract_inverted_index.improved | 229, 260 |
| abstract_inverted_index.learning | 46 |
| abstract_inverted_index.patients | 126 |
| abstract_inverted_index.protocol | 115 |
| abstract_inverted_index.quality, | 282 |
| abstract_inverted_index.readings | 273 |
| abstract_inverted_index.reducing | 12 |
| abstract_inverted_index.residual | 147 |
| abstract_inverted_index.shortage | 113 |
| abstract_inverted_index.standard | 37, 139, 290, 310 |
| abstract_inverted_index.studies. | 56, 69 |
| abstract_inverted_index.(RED-CNN) | 152 |
| abstract_inverted_index.MATERIALS | 70 |
| abstract_inverted_index.decreased | 198 |
| abstract_inverted_index.denoising | 47, 157, 227, 268 |
| abstract_inverted_index.different | 85 |
| abstract_inverted_index.generally | 249 |
| abstract_inverted_index.improving | 320 |
| abstract_inverted_index.included. | 124, 145 |
| abstract_inverted_index.iodinated | 5, 33, 93, 110, 129, 140, 202, 254, 302, 311 |
| abstract_inverted_index.performed | 80, 105 |
| abstract_inverted_index.protocols | 15 |
| abstract_inverted_index.reduction | 210 |
| abstract_inverted_index.resulting | 237 |
| abstract_inverted_index.shortages | 7 |
| abstract_inverted_index.standard, | 169 |
| abstract_inverted_index.underwent | 75 |
| abstract_inverted_index.BACKGROUND | 0 |
| abstract_inverted_index.artificial | 25 |
| abstract_inverted_index.developing | 14 |
| abstract_inverted_index.protocols, | 306 |
| abstract_inverted_index.reduction. | 219 |
| abstract_inverted_index.reductions | 200 |
| abstract_inverted_index.Considering | 3 |
| abstract_inverted_index.Qualitative | 272 |
| abstract_inverted_index.approximate | 309 |
| abstract_inverted_index.evaluation. | 190 |
| abstract_inverted_index.maintaining | 21 |
| abstract_inverted_index.potentially | 319 |
| abstract_inverted_index.preclinical | 53, 68 |
| abstract_inverted_index.qualitative | 188 |
| abstract_inverted_index.CONCLUSIONS: | 293 |
| abstract_inverted_index.acquisitions | 104 |
| abstract_inverted_index.combinations | 82 |
| abstract_inverted_index.consistently | 274 |
| abstract_inverted_index.examinations | 78 |
| abstract_inverted_index.intelligence | 26 |
| abstract_inverted_index.investigated | 66 |
| abstract_inverted_index.quantitative | 185 |
| abstract_inverted_index.acquisitions, | 40 |
| abstract_inverted_index.acquisitions. | 326 |
| abstract_inverted_index.convolutional | 149 |
| abstract_inverted_index.progressively | 228 |
| abstract_inverted_index.noise-filtered | 175 |
| abstract_inverted_index.Noise-filtering | 220 |
| abstract_inverted_index.encoder-decoder | 148 |
| abstract_inverted_index.retrospectively | 123 |
| abstract_inverted_index.RED-CNN-denoised, | 170 |
| abstract_inverted_index.learning-denoising | 295 |
| abstract_inverted_index.milliampere-second | 206, 218, 325 |
| abstract_inverted_index.<i>k-</i>space-weighted | 159, 172, 222, 263, 285 |
| abstract_inverted_index.propensity-score-matched | 135 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile.value | 0.20067371 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |