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    <title language="eng">Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network</title>
    <parentTitle language="eng">Physics in Medicine &amp; Biology</parentTitle>
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The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. &lt;jats:italic&gt;Main results&lt;\/jats:italic&gt;. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were\u2014(a) mean absolute error (MAE) of 70 \u00b1 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 \u00b1 2.8 dB; structural similarity metric (SSIM) of 0.95 \u00b1 0.02; and (d) Dice coefficient of the body region of 0.984 \u00b1 0. &lt;jats:italic&gt;Significance&lt;\/jats:italic&gt;. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. 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    <author>
      <first_name>Sandeep</first_name>
      <last_name>Kaushik</last_name>
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      <first_name>Mikael</first_name>
      <last_name>Bylund</last_name>
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      <first_name>Cristina</first_name>
      <last_name>Cozzini</last_name>
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      <first_name>Dattesh</first_name>
      <last_name>Shanbhag</last_name>
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      <first_name>Steven F.</first_name>
      <last_name>Petit</last_name>
    </author>
    <author>
      <first_name>Jonathan J.</first_name>
      <last_name>Wyatt</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Bhairav</first_name>
      <last_name>Mehta</last_name>
    </author>
    <author>
      <first_name>Vikas</first_name>
      <last_name>Chauhan</last_name>
    </author>
    <author>
      <first_name>Kesavadas</first_name>
      <last_name>Chandrasekharan</last_name>
    </author>
    <author>
      <first_name>Joakim</first_name>
      <last_name>Jonsson</last_name>
    </author>
    <author>
      <first_name>Tufve</first_name>
      <last_name>Nyholm</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Wiesinger</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
</export-example>
