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      Using a handheld stereo depth camera to overcome limited field-of-view in simulation imaging for radiation therapy treatment planning

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          Abstract

          Purpose

          A correct body contour is essential for reliable treatment planning in radiation therapy. While modern medical imaging technologies provide highly accurate patient modeling, there are times when a patient’s anatomy cannot be fully captured or there is a lack of easy access to computed tomography (CT) simulation. Here we provide a practical solution to the surface contour truncation problem by using a handheld stereo depth camera (HSDC) to obtain the missing surface anatomy and a surface-surface image registration to stich the surface data into the CT dataset for treatment planning.

          Methods

          For a subject with truncated simulation CT images, a HSDC is used to capture the surface information of the truncated anatomy. A mesh surface model is created using a software tool provided by the camera manufacturer. A surface-to-surface registration technique is used to merge the mesh model with the CT and fill in the missing surface information thereby obtaining a complete surface model of the subject. To evaluate the accuracy of the proposed approach, experiments were performed with the following steps. First, we selected three previously treated patients and fabricated a phantom mimicking each patient using the corresponding CT images and a 3D printer. Second, we removed part of the CT images of each patient to create hypothetical cases with image truncations. Next, a HSDC was used to image the 3D-printed phantoms and the HSDC-derived surface models were registered with the hypothetically truncated CT images. The contours obtained using the approach were then compared with the ground truth contours derived from the original simulation CT without image truncation. The distance between the two contours was calculated in order to evaluate the accuracy of the method. Finally, the dosimetric impact of the approach is assessed by comparing the volume within the 95% isodose line and global maximum dose (D max) computed based on the two surface contours for the breast case that exhibited the largest contour variation in the treated breast.

          Results

          A systematic strategy of using a 3D HSDC to compensate for missing surface information caused by the truncation of CT images was established. Our study showed that the proposed technique was able to reliably provide the full contours for treatment planning in the case of severe CT image truncation(s). The root-mean-square error for the registration between the aligned HDSC surface model and the ground truth data was found to be 2.1 mm. The average distance between the two models was 0.4 ± 1.7 mm (mean ± SD). Maximum deviations occurred in areas of high concavity or when the skin was close to the couch. The breast tissue covered by 95% isodose line decreased by 3% and D max increased by 0.2 % with the use of the HSDC model.

          Conclusions

          The use of HSDC for obtaining missing surface data during simulation has a number of advantages, such as, ease of use, low cost, and no additional ionizing radiation. It may provide a clinically practical solution to deal with the longstanding problem of CT image truncations in radiation therapy treatment planning.

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          Author and article information

          Journal
          0425746
          5648
          Med Phys
          Med Phys
          Medical physics
          0094-2405
          2473-4209
          17 March 2017
          17 April 2017
          May 2017
          01 May 2018
          : 44
          : 5
          : 1857-1864
          Affiliations
          [1 ]Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
          [2 ]Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
          Author notes
          Corresponding Author: Amy Yu, PhD, DABR, Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, amysyu@ 123456stanford.edu
          Article
          PMC5462446 PMC5462446 5462446 nihpa859075
          10.1002/mp.12207
          5462446
          28295413
          2e983219-632f-42a3-8501-bf39cd3a220f
          History
          Categories
          Article

          obese,camera,CT simulation,treatment planning,Extended field of view

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