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DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.
Uher, Daniel; Drenthen, Gerhard S; Poser, Benedikt A; Hofman, Paul A M; Wagner, Louis G; van Lanen, Rick H G J; Hoeberigs, Christianne M; Colon, Albert J; Schijns, Olaf E M G; Jansen, Jacobus F A; Backes, Walter H.
Afiliación
  • Uher D; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Net
  • Drenthen GS; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands.
  • Poser BA; Faculty of Psychology and Neuroscience (FPN), Maastricht University, the Netherlands.
  • Hofman PAM; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands.
  • Wagner LG; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands.
  • van Lanen RHGJ; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.
  • Hoeberigs CM; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands.
  • Colon AJ; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands; Department of Epileptology, CHU-Martinique, Fort-de-France, France.
  • Schijns OEMG; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastric
  • Jansen JFA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Cent
  • Backes WH; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Mental Health and Neuroscience Institute (MHeNs), Maastricht University, Maastricht, the Netherlands; Cardiovascular Diseases Institute (CARIM), Maastricht University, Maastricht, the Net
Magn Reson Imaging ; 110: 57-68, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38621552
ABSTRACT
BACKGROUND AND

PURPOSE:

Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network.

METHODS:

Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel.

RESULTS:

SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important.

CONCLUSIONS:

The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lóbulo Temporal / Procesamiento de Imagen Asistido por Computador / Imagen por Resonancia Magnética / Redes Neurales de la Computación / Artefactos / Relación Señal-Ruido Límite: Adult / Female / Humans / Male Idioma: En Revista: Magn Reson Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Lóbulo Temporal / Procesamiento de Imagen Asistido por Computador / Imagen por Resonancia Magnética / Redes Neurales de la Computación / Artefactos / Relación Señal-Ruido Límite: Adult / Female / Humans / Male Idioma: En Revista: Magn Reson Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos