Evidence map›Paper›PMID 35122265›Full record

ArticleMedical physics2022

A back-projection-and-filtering-like (BPF-like) reconstruction method with the deep learning filtration from listmode data in TOF-PET.

Li Lv, Gengsheng L Zeng, Yunlong Zan, Xiang Hong, Minghao Guo, Gaoyu Chen, Weijie Tao, Wenxiang Ding, Qiu Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

9 authors.

Li LvSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Gengsheng L ZengDepartment of Computer Science, Utah Valley University, Orem, Utah, USA.
Yunlong ZanDepartment of Nuclear Medicine, School of Medicine, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China.
Xiang HongSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Minghao GuoSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Gaoyu ChenSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Weijie TaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Wenxiang DingSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Qiu HuangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Funding

Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction TechniquesR15EB024283 · NIBIB · WEBER STATE UNIVERSITY · PI ZENG, GENGSHENG · 2018 to 2022
$644k
Interdisciplinary Program of Shanghai Jiao Tong University YG2021QN15National Natural Science Foundation of China 51627807NIBIB NIH HHS R15 EB024283
6 · The paper itself

Abstract

purposeThe time-of-flight (TOF) information improves signal-to-noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back-projection process on reconstructing images from the sinogram data. This work aims to develop a back-projection-and-filtering-like (BPF-like) algorithm that reconstructs the TOF PET image directly from listmode data rapidly.

methodsWe extended the 2D conventional non-TOF PET projection model to a TOF case, where projection data are represented as line integrals weighted by the one-dimensional TOF kernel along the projection direction. After deriving the central slice theorem and the TOF back-projection of listmode data, we designed a deep learning network with a modified U-net architecture to perform the spatial filtration (reconstruction filter). The proposed BP-Net method was validated via Monte Carlo simulations of TOF PET listmode data with three different time resolutions for two types of activity phantoms. The network was only trained on the simulated full-dose XCAT dataset and then evaluated on XCAT and Jaszczak data with different time resolutions and dose levels.

resultsReconstructed images show that when compared with the conventional BPF algorithm and the MLEM algorithm proposed for TOF PET, the proposed BP-Net method obtains better image quality in terms of peak signal-to-noise ratio, relative mean square error, and structure similarity index; besides, the reconstruction speed of the BP-Net is 1.75 times faster than BPF and 29.05 times faster than MLEM using 15 iterations. The results also indicate that the performance of the BP-Net degrades with worse time resolutions and lower tracer doses, but degrades less than BPF or MLEM reconstructions.

conclusionIn this work, we developed an analytical-like reconstruction in the form of BPF with the reconstruction filtering operation performed via a deep network. The method runs even faster than the conventional BPF algorithm and provides accurate reconstructions from listmode data in TOF-PET, free of rebinning data to a sinogram.

Indexed as

Deep LearningImage Processing, Computer-AssistedAlgorithmsPhantoms, ImagingPositron-Emission TomographySignal-To-Noise Ratioanalytical reconstructionback-projection and filteringdeep learninglistmodeTOF PET

Identifiers

PMID35122265
PMCPMC10080664

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.