Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits Article Swipe
Luka C. Liebrand
,
Dimitrios Karkalousos
,
Émilie Poirion
,
Bart J. Emmer
,
Stefan D. Roosendaal
,
Henk A. Marquering
,
Charles B.L.M. Majoie
,
Julien Savatovsky
,
Matthan W.A. Caan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10334-024-01200-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10334-024-01200-8
Objective To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed. Materials and Methods Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3 T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in terms of self-referenced quality metrics, image resolution, patient groups and reconstruction time. In ten scans, the contrast ratio (CR) was determined between lesions and white matter. The effect of acceleration factor was assessed in a publicly available fully sampled dataset, since ground truth data are not available in prospectively accelerated clinical scans. Specifically, 451 FLAIR scans, including scans with white matter lesions, were adopted from the FastMRI database to evaluate structural similarity (SSIM) and the CR of lesions and white matter on ranging acceleration factors from four-fold up to 12-fold. Results Interventional neuroradiologists significantly preferred the CIRIM for imaging artifacts, anatomic conspicuity, and contrast. One rater significantly preferred the CIRIM in terms of sharpness and diagnostic confidence. The pediatric neuroradiologist preferred CS for imaging artifacts and sharpness. Compared to CS, the CIRIM reconstructions significantly improved in terms of imaging artifacts and anatomic conspicuity (p < 0.01) for higher resolution scans while yielding a 28% higher SNR (p = 0.001) and a 5.8% lower CR (p = 0.04). There were no differences between patient groups. Additionally, CIRIM was five times faster than CS was. An increasing acceleration factor did not lead to changes in CR (p = 0.92), but led to lower SSIM (p = 0.002). Discussion Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10334-024-01200-8
- OA Status
- hybrid
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402045365
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402045365Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10334-024-01200-8Digital Object Identifier
- Title
-
Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficitsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-30Full publication date if available
- Authors
-
Luka C. Liebrand, Dimitrios Karkalousos, Émilie Poirion, Bart J. Emmer, Stefan D. Roosendaal, Henk A. Marquering, Charles B.L.M. Majoie, Julien Savatovsky, Matthan W.A. CaanList of authors in order
- Landing page
-
https://doi.org/10.1007/s10334-024-01200-8Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1007/s10334-024-01200-8Direct OA link when available
- Concepts
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Fluid-attenuated inversion recovery, Magnetic resonance imaging, Medicine, Neuroimaging, Computer science, Neuroscience, Radiology, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.via | 62 |
| abstract_inverted_index.was | 69, 125, 137, 282 |
| abstract_inverted_index.< | 250 |
| abstract_inverted_index.(CR) | 124 |
| abstract_inverted_index.(CS) | 6 |
| abstract_inverted_index.5.8% | 267 |
| abstract_inverted_index.MRI. | 57 |
| abstract_inverted_index.SSIM | 307 |
| abstract_inverted_index.also | 103 |
| abstract_inverted_index.data | 149 |
| abstract_inverted_index.five | 283 |
| abstract_inverted_index.from | 45, 170, 191 |
| abstract_inverted_index.high | 325 |
| abstract_inverted_index.lead | 295 |
| abstract_inverted_index.than | 286 |
| abstract_inverted_index.was. | 288 |
| abstract_inverted_index.were | 43, 59, 102, 168, 274 |
| abstract_inverted_index.when | 24 |
| abstract_inverted_index.with | 16, 30, 51, 164, 313, 338 |
| abstract_inverted_index.0.01) | 251 |
| abstract_inverted_index.CIRIM | 202, 215, 237, 281 |
| abstract_inverted_index.FLAIR | 160 |
| abstract_inverted_index.Image | 67 |
| abstract_inverted_index.There | 273 |
| abstract_inverted_index.fully | 143 |
| abstract_inverted_index.image | 19, 111, 334 |
| abstract_inverted_index.lower | 268, 306 |
| abstract_inverted_index.range | 321 |
| abstract_inverted_index.rater | 211 |
| abstract_inverted_index.ratio | 123 |
| abstract_inverted_index.scans | 27, 163, 255 |
| abstract_inverted_index.since | 146 |
| abstract_inverted_index.terms | 106, 217, 242, 332 |
| abstract_inverted_index.time. | 117 |
| abstract_inverted_index.times | 23, 284 |
| abstract_inverted_index.truth | 148 |
| abstract_inverted_index.while | 256 |
| abstract_inverted_index.white | 130, 165, 185 |
| abstract_inverted_index.(SSIM) | 178 |
| abstract_inverted_index.0.001) | 264 |
| abstract_inverted_index.0.04). | 272 |
| abstract_inverted_index.0.92), | 302 |
| abstract_inverted_index.CIRIM. | 66 |
| abstract_inverted_index.Images | 58 |
| abstract_inverted_index.cohort | 47 |
| abstract_inverted_index.effect | 133 |
| abstract_inverted_index.factor | 136, 292 |
| abstract_inverted_index.faster | 285 |
| abstract_inverted_index.ground | 147 |
| abstract_inverted_index.groups | 114 |
| abstract_inverted_index.higher | 253, 260 |
| abstract_inverted_index.images | 42 |
| abstract_inverted_index.manner | 76 |
| abstract_inverted_index.matter | 166, 186 |
| abstract_inverted_index.modest | 340 |
| abstract_inverted_index.scans, | 120, 161 |
| abstract_inverted_index.scans. | 157 |
| abstract_inverted_index.times. | 347 |
| abstract_inverted_index.(CIRIM) | 15 |
| abstract_inverted_index.0.002). | 310 |
| abstract_inverted_index.12-fold | 25 |
| abstract_inverted_index.FastMRI | 172 |
| abstract_inverted_index.Methods | 37 |
| abstract_inverted_index.Results | 196 |
| abstract_inverted_index.adopted | 169 |
| abstract_inverted_index.between | 127, 277 |
| abstract_inverted_index.blinded | 73 |
| abstract_inverted_index.changes | 297 |
| abstract_inverted_index.compare | 3 |
| abstract_inverted_index.factors | 190 |
| abstract_inverted_index.groups. | 279 |
| abstract_inverted_index.imaging | 88, 204, 229, 244 |
| abstract_inverted_index.lesions | 128, 183 |
| abstract_inverted_index.matter. | 131 |
| abstract_inverted_index.methods | 101 |
| abstract_inverted_index.offline | 61 |
| abstract_inverted_index.patient | 113, 278 |
| abstract_inverted_index.quality | 20, 68, 109 |
| abstract_inverted_index.ranging | 188 |
| abstract_inverted_index.reduced | 345 |
| abstract_inverted_index.respect | 17 |
| abstract_inverted_index.sampled | 144 |
| abstract_inverted_index.sensing | 5 |
| abstract_inverted_index.spatial | 91 |
| abstract_inverted_index.undergo | 317 |
| abstract_inverted_index.12-fold. | 195 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Cascades | 9 |
| abstract_inverted_index.Compared | 233 |
| abstract_inverted_index.Machines | 14 |
| abstract_inverted_index.Patients | 312 |
| abstract_inverted_index.T2-FLAIR | 41 |
| abstract_inverted_index.anatomic | 94, 206, 247 |
| abstract_inverted_index.assessed | 70, 138 |
| abstract_inverted_index.clinical | 156 |
| abstract_inverted_index.compared | 104 |
| abstract_inverted_index.contrast | 122, 343 |
| abstract_inverted_index.database | 173 |
| abstract_inverted_index.dataset, | 145 |
| abstract_inverted_index.deficits | 32, 53, 315 |
| abstract_inverted_index.evaluate | 175 |
| abstract_inverted_index.improved | 240 |
| abstract_inverted_index.lesions, | 167 |
| abstract_inverted_index.metrics, | 110 |
| abstract_inverted_index.moderate | 323 |
| abstract_inverted_index.obtained | 44 |
| abstract_inverted_index.patients | 29, 50 |
| abstract_inverted_index.publicly | 141 |
| abstract_inverted_index.yielding | 257 |
| abstract_inverted_index.Inference | 13 |
| abstract_inverted_index.Materials | 35 |
| abstract_inverted_index.Objective | 1 |
| abstract_inverted_index.Recurrent | 12 |
| abstract_inverted_index.artifacts | 230, 245 |
| abstract_inverted_index.available | 142, 152 |
| abstract_inverted_index.contrast. | 99, 209 |
| abstract_inverted_index.denoising | 337 |
| abstract_inverted_index.efficient | 336 |
| abstract_inverted_index.four-fold | 192 |
| abstract_inverted_index.including | 162 |
| abstract_inverted_index.pediatric | 85, 224 |
| abstract_inverted_index.perceived | 90 |
| abstract_inverted_index.preferred | 200, 213, 226 |
| abstract_inverted_index.reduction | 341 |
| abstract_inverted_index.sharpness | 219 |
| abstract_inverted_index.Discussion | 311 |
| abstract_inverted_index.artifacts, | 89, 205 |
| abstract_inverted_index.compressed | 4 |
| abstract_inverted_index.determined | 126 |
| abstract_inverted_index.diagnostic | 96, 221 |
| abstract_inverted_index.increasing | 290 |
| abstract_inverted_index.randomized | 75 |
| abstract_inverted_index.resolution | 92, 254 |
| abstract_inverted_index.sharpness. | 232 |
| abstract_inverted_index.similarity | 177 |
| abstract_inverted_index.structural | 176 |
| abstract_inverted_index.Twelve-fold | 38 |
| abstract_inverted_index.accelerated | 26, 39, 155 |
| abstract_inverted_index.confidence, | 97 |
| abstract_inverted_index.confidence. | 222 |
| abstract_inverted_index.conspicuity | 248 |
| abstract_inverted_index.differences | 276 |
| abstract_inverted_index.experienced | 79, 84 |
| abstract_inverted_index.outperforms | 329 |
| abstract_inverted_index.resolution, | 112, 335 |
| abstract_inverted_index.(sharpness), | 93 |
| abstract_inverted_index.acceleration | 135, 189, 291 |
| abstract_inverted_index.conspicuity, | 95, 207 |
| abstract_inverted_index.neurological | 31, 52, 314 |
| abstract_inverted_index.Additionally, | 280 |
| abstract_inverted_index.Independently | 11 |
| abstract_inverted_index.Specifically, | 158 |
| abstract_inverted_index.acceleration. | 326 |
| abstract_inverted_index.prospectively | 154 |
| abstract_inverted_index.reconstructed | 60 |
| abstract_inverted_index.significantly | 199, 212, 239 |
| abstract_inverted_index.Interventional | 197 |
| abstract_inverted_index.interventional | 80 |
| abstract_inverted_index.reconstructed. | 34 |
| abstract_inverted_index.reconstruction | 22, 116, 328, 346 |
| abstract_inverted_index.reconstructions | 238 |
| abstract_inverted_index.self-referenced | 108 |
| abstract_inverted_index.neuroradiologist | 86, 225 |
| abstract_inverted_index.neuroradiologists | 81, 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.26345001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |