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Generation of Synthetic CT Images From MRI for Treatment Planning and Patient Positioning Using a 3-Channel U-Net Trained on Sagittal Images

Gupta, Dinank ; Kim, Michelle ; Vineberg, Karen A. ; Balter, James M.

Frontiers in oncology, 2019-09, Vol.9, p.964-964 [Periódico revisado por pares]

Frontiers Research Foundation

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  • Título:
    Generation of Synthetic CT Images From MRI for Treatment Planning and Patient Positioning Using a 3-Channel U-Net Trained on Sagittal Images
  • Autor: Gupta, Dinank ; Kim, Michelle ; Vineberg, Karen A. ; Balter, James M.
  • Assuntos: Cancer ; Care and treatment ; CT imaging ; deep learning ; Health planning ; Magnetic resonance imaging ; Methods ; MRCT ; MRI ; Oncology ; Patients ; Positioning ; radiation oncology ; Radiotherapy ; synthetic CT
  • É parte de: Frontiers in oncology, 2019-09, Vol.9, p.964-964
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Reviewed by: Ravi S. Hegde, Indian Institute of Technology Gandhinagar, India; Juan Gabriel Avina-Cervantes, University of Guanajuato, Mexico
    This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology
    Edited by: Jing Cai, Hong Kong Polytechnic University, Hong Kong
  • Descrição: A novel deep learning architecture was explored to create synthetic CT (MRCT) images that preserve soft tissue contrast necessary for support of patient positioning in Radiation therapy. A U-Net architecture was applied to learn the correspondence between input T1-weighted MRI and spatially aligned corresponding CT images. The network was trained on sagittal images, taking advantage of the left-right symmetry of the brain to increase the amount of training data for similar anatomic positions. The output CT images were divided into three channels, representing Hounsfield Unit (HU) ranges of voxels containing air, soft tissue, and bone, respectively, and simultaneously trained using a combined Mean Absolute Error (MAE) and Mean Squared Error (MSE) loss function equally weighted for each channel. Training on 9192 image pairs yielded resulting synthetic CT images on 13 test patients with MAE of 17.6+/−3.4 HU (range 14–26.5 HU) in soft tissue. Varying the amount of training data demonstrated a general decrease in MAE values with more data, with the lack of a plateau indicating that additional training data could further improve correspondence between MRCT and CT tissue intensities. Treatment plans optimized on MRCT-derived density grids using this network for 7 radiosurgical targets had doses recalculated using the corresponding CT-derived density grids, yielding a systematic mean target dose difference of 2.3% due to the lack of the immobilization mask on the MRCT images, and a standard deviation of 0.1%, indicating the consistency of this correctable difference. Alignment of MRCT and cone beam CT (CBCT) images used for patient positioning demonstrated excellent preservation of dominant soft tissue features, and alignment comparisons of treatment planning CT scans to CBCT images vs. MRCT to CBCT alignment demonstrated differences of −0.1 (σ 0.2) mm, −0.1 (σ 0.3) mm, and −0.2 (σ 0.3) mm about the left-right, anterior-posterior and cranial-caudal axes, respectively.
  • Editor: Frontiers Research Foundation
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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