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Applied Sciences
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Methods, Applications and Developments in Positron Emission Tomography
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Methods, Applications and Developments in Positron Emission Tomography

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 8605

Special Issue Editors

Dr. Jarmo Teuho

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Guest Editor
Turku PET Centre, University of Turku and Turku University Hospital, 20521 Turku, Finland
Interests: PET/CT, PET/MR; total-body PET; image quantification; deep learning
Dr. Riku Klén

E-Mail Website
Guest Editor
Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
Interests: PET imaging; imaging instrumentation; artificial intelligence; machine learning; image segmentation; modeling
Prof. Dr. Mika Teräs

E-Mail Website
Guest Editor
1. Department of Medical Physics, Turku University Hospital, 20521 Turku, Finland;
2. Department of Biomedicine, University of Turku, 20014 Turku, Finland
Interests: radiation dose optimization and dosimetry; nuclear medicine instrumentation and modeling

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a Special Issue in Applied Sciences in the topic of “Methods, Applications, and Developments in Positron Emission Tomography”. In this Special Issue, we encourage the submission of original research articles, review articles, and short technical communications from the above topic. More specifically, we would like to collect methodological articles with applications and recent developments from the following topics of positron emission tomography (PET): image quantification, phantom measurements and protocols, image reconstruction, machine learning, attenuation correction, motion correction, and time-of-flight imaging in PET, PET/MR and PET/CT.

Please note that submitted papers should be in the scope of Applied Sciences. Furthermore, we encourage all authors to submit a short abstract of 300 words describing their main content of the manuscript, for the editors to better determine whether the paper is suitable for the Special Issue. 

Dr. Jarmo Teuho
Dr. Riku Klén
Prof. Dr. Mika Teräs
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • PET
  • PET/CT
  • PET/MR
  • Quantification
  • Phantom measurements and protocols
  • Image reconstruction
  • Machine learning
  • Attenuation correction
  • Motion correction
  • Time-of-flight imaging

Published Papers (5 papers)

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Research

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24 pages, 21763 KiB  
Article
How to Pseudo-CT: A Comparative Review of Deep Convolutional Neural Network Architectures for CT Synthesis
by Javier Vera-Olmos, Angel Torrado-Carvajal, Carmen Prieto-de-la-Lastra, Onofrio A. Catalano, Yves Rozenholc, Filomena Mazzeo, Andrea Soricelli, Marco Salvatore, David Izquierdo-Garcia and Norberto Malpica
Appl. Sci. 2022, 12(22), 11600; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211600 - 15 Nov 2022
Cited by 1 | Viewed by 1396
Abstract
This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net [...] Read more.
This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Positron Emission Tomography)
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12 pages, 1856 KiB  
Article
Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
by Yucheng Zhang, Shuo Xu, Hongjia Li, Ziren Kong, Xincheng Xiang, Xin Cheng and Shaoyan Liu
Appl. Sci. 2022, 12(10), 5187; https://0-doi-org.brum.beds.ac.uk/10.3390/app12105187 - 20 May 2022
Cited by 1 | Viewed by 1462
Abstract
18F-choline (CHO) PET image remains noisy despite minimum physiological activity in the normal brain, and this study developed a deep learning-based denoising algorithm for brain tumor CHO PET. Thirty-nine presurgical CHO PET/CT data were retrospectively collected for patients with pathological confirmed [...] Read more.
18F-choline (CHO) PET image remains noisy despite minimum physiological activity in the normal brain, and this study developed a deep learning-based denoising algorithm for brain tumor CHO PET. Thirty-nine presurgical CHO PET/CT data were retrospectively collected for patients with pathological confirmed primary diffuse glioma. Two conventional denoising methods, namely, block-matching and 3D filtering (BM3D) and non-local means (NLM), and two deep learning-based approaches, namely, Noise2Noise (N2N) and Noise2Void (N2V), were established for imaging denoising, and the methods were developed without paired data. All algorithms improved the image quality to a certain extent, with the N2N demonstrating the best contrast-to-noise ratio (CNR) (4.05 ± 3.45), CNR improvement ratio (13.60% ± 2.05%) and the lowest entropy (1.68 ± 0.17), compared with other approaches. Little changes were identified in traditional tumor PET features including maximum standard uptake value (SUVmax), SUVmean and total lesion activity (TLA), while the tumor-to-normal (T/N ratio) increased thanks to smaller noise. These results suggested that the N2N algorithm can acquire sufficient denoising performance while preserving the original features of tumors, and may be generalized for abundant brain tumor PET images. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Positron Emission Tomography)
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18 pages, 3755 KiB  
Article
Are Quantitative Errors Reduced with Time-of-Flight Reconstruction When Using Imperfect MR-Based Attenuation Maps for 18F-FDG PET/MR Neuroimaging?
by Jani Lindén, Jarmo Teuho, Riku Klén and Mika Teräs
Appl. Sci. 2022, 12(9), 4605; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094605 - 03 May 2022
Viewed by 1243
Abstract
We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone 18F-FDG PET neuroimaging were included. [...] Read more.
We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone 18F-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm−1) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm−1), air and bone (0.151 cm−1) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23–26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland–Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Positron Emission Tomography)
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15 pages, 1034 KiB  
Article
A Simple Contrast Matching Rule for OSEM Reconstructed PET Images with Different Time of Flight Resolution
by Luca Presotto, Valentino Bettinardi and Elisabetta De Bernardi
Appl. Sci. 2021, 11(16), 7548; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167548 - 17 Aug 2021
Viewed by 1336
Abstract
Background: Time-of-Flight (TOF) is a leading technological development of Positron Emission Tomography (PET) scanners. It reduces noise at the Maximum-Likelihood solution, depending on the coincidence–timing–resolution (CTR). However, in clinical applications, it is still not clear how to best exploit TOF information, as early [...] Read more.
Background: Time-of-Flight (TOF) is a leading technological development of Positron Emission Tomography (PET) scanners. It reduces noise at the Maximum-Likelihood solution, depending on the coincidence–timing–resolution (CTR). However, in clinical applications, it is still not clear how to best exploit TOF information, as early stopped reconstructions are generally used. Methods: A contrast-recovery (CR) matching rule for systems with different CTRs and non-TOF systems is theoretically derived and validated using (1) digital simulations of objects with different contrasts and background diameters, (2) realistic phantoms of different sizes acquired on two scanners with different CTRs. Results: With TOF, the CR matching rule prescribes modifying the iterations number by the CTRs ratio. Without TOF, the number of iterations depends on the background dimension. CR matching was confirmed by simulated and experimental data. With TOF, image noise followed the square root of the CTR when the rule was applied on simulated data, while a significant reduction was obtained on phantom data. Without TOF, preserving the CR on larger objects significantly increased the noise. Conclusions: TOF makes PET reconstructions less dependent on background dimensions, thus, improving the quantification robustness. Better CTRs allows performing fewer updates, thus, maintaining accuracy while minimizing noise. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Positron Emission Tomography)
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Review

Jump to: Research

19 pages, 507 KiB  
Review
FDG PET/CT versus Bone Marrow Biopsy for Diagnosis of Bone Marrow Involvement in Non-Hodgkin Lymphoma: A Systematic Review
by Jawaher Almaimani, Charalampos Tsoumpas, Richard Feltbower and Irene Polycarpou
Appl. Sci. 2022, 12(2), 540; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020540 - 06 Jan 2022
Cited by 3 | Viewed by 2121
Abstract
The management of non-Hodgkin lymphoma (NHL) patients requires the identification of bone marrow involvement (BMI) using a bone marrow biopsy (BMB), as recommended by international guidelines. Multiple studies have shown that [18F]FDG positron emission tomography, combined with computed tomography (PET/CT), may [...] Read more.
The management of non-Hodgkin lymphoma (NHL) patients requires the identification of bone marrow involvement (BMI) using a bone marrow biopsy (BMB), as recommended by international guidelines. Multiple studies have shown that [18F]FDG positron emission tomography, combined with computed tomography (PET/CT), may provide important information and may detect BMI, but there is still an ongoing debate as to whether it is sensitive enough for NHL patients in order to replace or be used as a complimentary method to BMB. The objective of this article is to systematically review published studies on the performance of [18F]FDG PET/CT in detecting BMI compared to the BMB for NHL patients. A population, intervention, comparison, and outcome (PICO) search in PubMed and Scopus databases (until 1 November 2021) was performed. A total of 41 studies, comprising 6147 NHL patients, were found to be eligible and were included in the analysis conducted in this systematic review. The sensitivity and specificity for identifying BMI in NHL patients were 73% and 90% for [18F]FDG PET/CT and 56% and 100% for BMB. For aggressive NHL, the sensitivity and specificity to assess the BMI for the [18F]FDG PET/CT was 77% and 94%, while for the BMB it was 58% and 100%. However, sensitivity and specificity to assess the BMI for indolent NHL for the [18F]FDG PET/CT was 59% and 85%, while for the BMB it was superior, and equal to 94% and 100%. With regard to NHL, a [18F]FDG PET/CT scan can only replace BMB if it is found to be positive and if patients can be categorized as having advanced staged NHL with high certainty. [18F]FDG PET/CT might recover tumors missed by BMB, and is recommended for use as a complimentary method, even in indolent histologic subtypes of NHL. Full article
(This article belongs to the Special Issue Methods, Applications and Developments in Positron Emission Tomography)
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