PMID- 32492585
OWN - NLM
STAT- MEDLINE
DCOM- 20210623
LR  - 20210623
IS  - 1361-8423 (Electronic)
IS  - 1361-8415 (Linking)
VI  - 64
DP  - 2020 Aug
TI  - Deep learning-guided estimation of attenuation correction factors from 
      time-of-flight PET emission data.
PG  - 101718
LID - S1361-8415(20)30082-7 [pii]
LID - 10.1016/j.media.2020.101718 [doi]
AB  - PURPOSE: Attenuation correction (AC) is essential for quantitative PET imaging. 
      In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems 
      or dedicated brain PET scanners, an accurate approach for synthetic CT generation 
      is highly desired. In this work, a novel framework is proposed wherein 
      attenuation correction factors (ACF) are estimated from time-of-flight (TOF) PET 
      emission data using deep learning. METHODS: In this approach, referred to as 
      called DL-EM), the different TOF sinogram bins pertinent to the same slice are 
      fed into a multi-input channel deep convolutional network to estimate a single 
      ACF sinogram associated with the same slice. The clinical evaluation of the 
      proposed DL-EM approach consisted of 68 clinical brain TOF PET/CT studies, where 
      CT-based attenuation correction (CTAC) served as reference. A two-tissue class 
      consisting of background-air and soft-tissue segmentation of the TOF PET non-AC 
      images (SEG) as a proxy of the technique used in the clinic was also included in 
      the comparative evaluation. Qualitative and quantitative PET analysis was 
      performed through SUV bias maps quantification in 63 different brain regions. 
      RESULTS: The DL-EM approach resulted in 6.1 +/- 9.7% relative mean absolute error 
      (RMAE) in bony structures compared to SEG AC method with RMAE of 16.1 +/- 8.2% 
      (p-value <0.001). Considering the entire head region, DL-EM led to a root mean 
      square error (RMSE) of 0.3 +/- 0.01 outperforming the SEG method with RMSE of 
      0.8 +/- 0.02 SUV (p-value <0.001). The region-wise analysis of brain PET studies 
      revealed less than 7% absolute SUV bias for the DL-EM approach, whereas the SEG 
      method resulted in more than 14% absolute SUV bias (p-value <0.05). CONCLUSIONS: 
      Qualitative assessment and quantitative PET analysis demonstrated the superior 
      performance of the DL-EM approach over the segmentation-based technique with 
      clinically acceptable SUV bias. The results obtained using the DL-EM approach are 
      comparable to state-of-the-art MRI-guided AC methods. Yet, this approach enables 
      the extraction of interesting features about patient-specific attenuation which 
      could be employed not only as a stand-alone AC approach but also as 
      complementary/prior information in other AC algorithms.
CI  - Copyright (c) 2020 Elsevier B.V. All rights reserved.
FAU - Arabi, Hossein
AU  - Arabi H
AD  - Division of Nuclear Medicine and Molecular Imaging, Department of Medical 
      Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland.
FAU - Zaidi, Habib
AU  - Zaidi H
AD  - Division of Nuclear Medicine and Molecular Imaging, Department of Medical 
      Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva 
      Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland; Department 
      of Nuclear Medicine and Molecular Imaging, University of Groningen, University 
      Medical Center Groningen, 9700 RB Groningen, Netherlands; Department of Nuclear 
      Medicine, University of Southern Denmark, DK-500 Odense, Denmark. Electronic 
      address: habib.zaidi@hcuge.ch.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200519
PL  - Netherlands
TA  - Med Image Anal
JT  - Medical image analysis
JID - 9713490
SB  - IM
MH  - *Deep Learning
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - Magnetic Resonance Imaging
MH  - *Positron Emission Tomography Computed Tomography
MH  - Positron-Emission Tomography
OTO - NOTNLM
OT  - Attenuation correction
OT  - Deep learning
OT  - Machine learning
OT  - PET/CT
OT  - Quantification
EDAT- 2020/06/04 06:00
MHDA- 2021/06/24 06:00
CRDT- 2020/06/04 06:00
PHST- 2019/10/09 00:00 [received]
PHST- 2020/03/30 00:00 [revised]
PHST- 2020/04/30 00:00 [accepted]
PHST- 2020/06/04 06:00 [pubmed]
PHST- 2021/06/24 06:00 [medline]
PHST- 2020/06/04 06:00 [entrez]
AID - S1361-8415(20)30082-7 [pii]
AID - 10.1016/j.media.2020.101718 [doi]
PST - ppublish
SO  - Med Image Anal. 2020 Aug;64:101718. doi: 10.1016/j.media.2020.101718. Epub 2020 
      May 19.