PMID- 32422577
OWN - NLM
STAT- MEDLINE
DCOM- 20210406
LR  - 20210406
IS  - 1724-191X (Electronic)
IS  - 1120-1797 (Linking)
VI  - 74
DP  - 2020 Jun
TI  - Deep Learning model for markerless tracking in spinal SBRT.
PG  - 66-73
LID - S1120-1797(20)30111-3 [pii]
LID - 10.1016/j.ejmp.2020.04.029 [doi]
AB  - Stereotactic Body Radiation Therapy (SBRT), alternatively termed Stereotactic 
      ABlative Radiotherapy (SABR) or Stereotactic RadioSurgery (SRS), delivers high 
      dose with a sub-millimeter accuracy. It requires meticulous precautions on 
      positioning, as sharp dose gradients near critical neighboring structures (e.g. 
      the spinal cord for spinal tumor treatment) are an important clinical objective 
      to avoid complications such as radiation myelopathy, compression fractures, or 
      radiculopathy. To allow for dose escalation within the target without 
      compromising the dose to critical structures, proper immobilization needs to be 
      combined with (internal) motion monitoring. Metallic fiducials, as applied in 
      prostate, liver or pancreas treatments, are not suitable in clinical practice for 
      spine SBRT. However, the latest advances in Deep Learning (DL) allow for fast 
      localization of the vertebrae as landmarks. Acquiring projection images during 
      treatment delivery allows for instant 2D position verification as well as 
      sequential (delayed) 3D position verification when incorporated in a Digital 
      TomoSynthesis (DTS) or Cone Beam Computed Tomography (CBCT). Upgrading to an 
      instant 3D position verification system could be envisioned with a stereoscopic 
      kilovoltage (kV) imaging setup. This paper describes a fast DL landmark detection 
      model for vertebra (trained in-house) and evaluates its accuracy to detect 2D 
      motion of the vertebrae with the help of projection images acquired during 
      treatment. The introduced motion consists of both translational and rotational 
      variations, which are detected by the DL model with a sub-millimeter accuracy.
CI  - Copyright (c) 2020 Associazione Italiana di Fisica Medica. Published by Elsevier 
      Ltd. All rights reserved.
FAU - Roggen, Toon
AU  - Roggen T
AD  - Varian Medical Systems Imaging Laboratory, Taefernstrasse 7, 5405 Daettwil AG, 
      Switzerland. Electronic address: toon.roggen@varian.com.
FAU - Bobic, Mislav
AU  - Bobic M
AD  - Varian Medical Systems Imaging Laboratory, Taefernstrasse 7, 5405 Daettwil AG, 
      Switzerland.
FAU - Givehchi, Nasim
AU  - Givehchi N
AD  - Varian Medical Systems Imaging Laboratory, Taefernstrasse 7, 5405 Daettwil AG, 
      Switzerland.
FAU - Scheib, Stefan G
AU  - Scheib SG
AD  - Varian Medical Systems Imaging Laboratory, Taefernstrasse 7, 5405 Daettwil AG, 
      Switzerland.
LA  - eng
PT  - Journal Article
DEP - 20200515
PL  - Italy
TA  - Phys Med
JT  - Physica medica : PM : an international journal devoted to the applications of 
      physics to medicine and biology : official journal of the Italian Association of 
      Biomedical Physics (AIFB)
JID - 9302888
SB  - IM
MH  - Cone-Beam Computed Tomography
MH  - *Deep Learning
MH  - *Radiosurgery
MH  - Radiotherapy Planning, Computer-Assisted
MH  - Spine/diagnostic imaging/*radiation effects
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Deep Learning
OT  - Marker-less tracking
OT  - Motion monitoring
OT  - Real-time image analysis
OT  - Stereotactic body radiotherapy
OT  - Stereotactic radiosurgery
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2020/05/19 06:00
MHDA- 2021/04/07 06:00
CRDT- 2020/05/19 06:00
PHST- 2019/10/31 00:00 [received]
PHST- 2020/04/26 00:00 [revised]
PHST- 2020/04/28 00:00 [accepted]
PHST- 2020/05/19 06:00 [pubmed]
PHST- 2021/04/07 06:00 [medline]
PHST- 2020/05/19 06:00 [entrez]
AID - S1120-1797(20)30111-3 [pii]
AID - 10.1016/j.ejmp.2020.04.029 [doi]
PST - ppublish
SO  - Phys Med. 2020 Jun;74:66-73. doi: 10.1016/j.ejmp.2020.04.029. Epub 2020 May 15.