Evidence map›Paper›PMID 28295413›Full record

ArticleMedical physics2017

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

Cesare Jenkins, Lei Xing, Amy Yu

Open access · bronzeAbstract read
In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  4. Evaluation of optical 3D scanning system for radiotherapy use.Journal of medical radiation sciences · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Cesare JenkinsDepartments of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Lei XingDepartments of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Amy YuDepartments of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
Stanford University · US

Funding

DASSIM-RT and Compressed Sensing-Based Inverse PlanningR01CA176553 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2014 to 2018
$2.4M
Computational Tools for Next Generation Volumetric Cone Beam Computed TomographyR01EB016777 · NIBIB · STANFORD UNIVERSITY · PI XING, LEI · 2013 to 2016
$2.1M
Computational Tools for Adaptive Radiation TherapyR01CA133474 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2009 to 2012
$960k
Real-time 3D Imaging Guided ARTR43TR001285 · NCATS · XYKEN, LLC · PI YI, STEVEN · 2015 to 2016
$651k
NCATS NIH HHS R43 TR001285NCI NIH HHS R01 CA133474NCI NIH HHS R01 CA176553NIBIB NIH HHS R01 EB016777
6 · The paper itself

Abstract

purposeA 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.

methodsFor 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

resultsA 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

conclusionsThe 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.

Indexed as

Phantoms, ImagingRadiometryTomography, X-Ray ComputedHumanscameraCT simulationextended field-of-viewobesetreatment planning

Identifiers

PMID28295413
PMCPMC5462446
OpenAlexW2594466573

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.