Access the full text.
Sign up today, get DeepDyve free for 14 days.
Cheng-Bin Jin, Hakil Kim, Mingjie Liu, I. Han, Jae Lee, Jung Lee, Seongsu Joo, Eunsik Park, Young Ahn, X. Cui (2019)
DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised LearningApplied Sciences
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei Efros (2016)
Image-to-Image Translation with Conditional Adversarial Networks2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Prashanth Rawla (2019)
Epidemiology of Prostate CancerWorld Journal of Oncology, 10
C. Debus, R. Floca, M. Ingrisch, I. Kompan, Klaus Maier-Hein, A. Abdollahi, M. Nolden (2018)
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of DCE-MRIBMC Bioinformatics, 20
K. Clark, Bruce Vendt, Kirk Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, Stanley Phillips, David Maffitt, Michael Pringle, Lawrence Tarbox, F. Prior (2013)
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information RepositoryJournal of Digital Imaging, 26
ZENGMING SHEN, S.kevin Zhou, Yifan Chen, B. Georgescu, Xuqi Liu, Thomas Huang (2019)
One-to-one Mapping for Unpaired Image-to-image Translation2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
Anwar Kamil, Talal Shaikh (2019)
Literature Review of Generative models for Image-to-Image translation problems2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, AA Bharath (2018)
10.1109/MSP.2017.2765202IEEE Signal Process. Mag., 35
Pedro Martins, L. Santos, D. Mariano, Felippe Queiroz, L. Bastos, I. Gomes, Pedro Fischer, R. Rocha, S. Silveira, L. Lima, M. Magalhães, Maria Oliveira, R. Minardi (2021)
Propedia: a database for protein–peptide identification based on a hybrid clustering algorithmBMC Bioinformatics, 22
H. Bickel, S. Polanec, G. Wengert, K. Pinker, W. Bogner, T. Helbich, P. Baltzer (2019)
Diffusion‐Weighted MRI of Breast Cancer: Improved Lesion Visibility and Image Quality Using Synthetic b‐ValuesJournal of Magnetic Resonance Imaging, 50
R. Bourne, Eleftheria Panagiotaki (2016)
Limitations and Prospects for Diffusion-Weighted MRI of the ProstateDiagnostics, 6
Ryota Shimofusa, H. Fujimoto, Hajime Akamata, K. Motoori, Seiji Yamamoto, T. Ueda, Hisao Ito (2005)
Diffusion-Weighted Imaging of Prostate CancerJournal of Computer Assisted Tomography, 29
P. Visschere, Chloë Standaert, J. Fütterer, G. Villeirs, V. Panebianco, J. Walz, T. Maurer, B. Hadaschik, F. Lecouvet, G. Giannarini, S. Fanti (2019)
A Systematic Review on the Role of Imaging in Early Recurrent Prostate Cancer.European urology oncology, 2 1
J. Barentsz, J. Richenberg, R. Clements, P. Choyke, S. Verma, G. Villeirs, O. Rouvière, V. Løgager, J. Fütterer (2012)
ESUR prostate MR guidelines 2012European Radiology, 22
(2014)
Prostate Cancer (2014)
Geoffrey Gaunay, Vinay Patel, Paras Shah, D. Moreira, S. Hall, M. Vira, Michael Schwartz, J. Kreshover, Eran Ben‐Levi, R. Villani, A. Rastinehad, L. Richstone (2016)
Role of multi-parametric MRI of the prostate for screening and staging: Experience with over 1500 casesAsian Journal of Urology, 4
S. Rezaeijo, B. Hashemi, B. Mofid, Mohsen Bakhshandeh, Arash Mahdavi, Mohammad Hashemi (2021)
The feasibility of a dose painting procedure to treat prostate cancer based on mpMR images and hierarchical clusteringRadiation Oncology (London, England), 16
T. Barrett, B. Turkbey, P. Choyke (2015)
PI-RADS version 2: what you need to know.Clinical radiology, 70 11
R. Robertis, P. Martini, E. Demozzi, Flavia Corso, C. Bassi, P. Pederzoli, M. D’Onofrio (2015)
Diffusion-weighted imaging of pancreatic cancer.World journal of radiology, 7 10
Y. Itou, K. Nakanishi, Y. Narumi, Y. Nishizawa, H. Tsukuma (2011)
Clinical utility of apparent diffusion coefficient (ADC) values in patients with prostate cancer: Can ADC values contribute to assess the aggressiveness of prostate cancer?Journal of Magnetic Resonance Imaging, 33
G Litjens, O Debats, J Barentsz, N Karssemeijer, H Huisman (2014)
10.1109/TMI.2014.2303821IEEE Trans. Med. Imaging, 33
David Johnson, R. Reiter (2017)
Multi-parametric magnetic resonance imaging as a management decision toolTranslational Andrology and Urology, 6
(2017)
Cancer imaging archive wiki (2017)
C. Dinh, P. Steenbergen, G. Ghobadi, S. Heijmink, F. Pos, K. Haustermans, U. Heide (2016)
Magnetic resonance imaging for prostate cancer radiotherapy.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, 32 3
A. Wetter, F. Nensa, C. Lipponer, N. Guberina, T. Olbricht, M. Schenck, T. Schlosser, M. Gratz, T. Lauenstein (2015)
High and ultra-high b-value diffusion-weighted imaging in prostate cancer: a quantitative analysisActa Radiologica, 56
Feasibility Study of Synthetic DW-MR Images with Different…
C. Cavaro-Ménard, Lu Zhang, P. Callet (2010)
Diagnostic quality assessment of medical images: Challenges and trends2010 2nd European Workshop on Visual Information Processing (EUVIP)
G. Manenti, M. Nezzo, F. Chegai, E. Vasili, E. Bonanno, G. Simonetti (2014)
DWI of Prostate Cancer: Optimal b-Value in Clinical PracticeProstate Cancer, 2014
S Heydarheydari (2016)
343Acta Med. Iran., 54
H. Agarwal, Francesca Mertan, S. Sankineni, M. Bernardo, J. Sénégas, J. Keupp, D. Daar, M. Merino, B. Wood, P. Pinto, P. Choyke, B. Turkbey (2017)
Optimal high b‐value for diffusion weighted MRI in diagnosing high risk prostate cancers in the peripheral zoneJournal of Magnetic Resonance Imaging, 45
Moritz Kasel-Seibert, T. Lehmann, René Aschenbach, F. Guettler, M. Abubrig, M. Grimm, U. Teichgraeber, T. Franiel (2016)
Assessment of PI-RADS v2 for the Detection of Prostate Cancer.European journal of radiology, 85 4
P. Sahoo, R. Rockne, Alexander Jung, P. Gupta, R. Rathore, Rakesh Gupta (2019)
Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in ProstateProstate Cancer, 2020
B. Choi, H. Baek, J. Ha, K. Ryu, J. Moon, S. Park, Kyungsoo Bae, K. Jeon, E. Jung (2020)
Feasibility Study of Synthetic Diffusion-Weighted MRI in Patients with Breast Cancer in Comparison with Conventional Diffusion-Weighted MRI.Korean journal of radiology
MC Maas, JJ Fütterer, TWJ Scheenen (2013)
10.1097/RLI.0b013e31829705bbInvest. Radiol., 48
(2013)
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
This study aimed to assess the clinical feasibility of employing synthetic diffusion-weighted (DW) images with different b values (50, 400, 800 s/mm2) for the prostate cancer patients with the help of three models, namely CycleGAN, Pix2PiX, and DC2Anet. DW images of 170 prostate cancer patients were used to train and test models. Here, 119 patients were assigned to the training set and 51 patients to the testing set according to a ratio of 7:3. To generate synthetic b value DW images based on CycleGAN, Pix2Pix, and DC2Anet networks, three experiments were performed as follows: generating synthetic DW images with b values of 400 and 800 s/mm2 from acquired DW images with b value of 50 s/mm2; generating synthetic DW images with b value of 800 8 s/mm2 from acquired DW images with b value of 400 s/mm2. Five metrics were used to compare real and synthetic b values. These metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Pearson’s Correlation Coefficient (PCC), Peak-Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index Measure (SSIM). As well as, ADC values for different b values were computed using the mono-exponentially mode. The whole prostate volume was manually segmented by drawing regions of interest (ROIs) in each slice of the ADC maps. P values less than 0.05 were considered statistically significant. Based on the quantitative evaluation and for all metrics, especially for generating b values of 400 and 800 s/mm2 from a b value of 50 s/mm2, the DC2Anet model was found accurate and it outperformed CycleGAN and Pix2Pix models (P < 0.05). It is necessary to mention that the agreement between synthetic ADC (sADC) and real ADC (rADC) was satisfactory. No significant difference was observed in the one-way ANOVA between sADC and rADC in the whole prostate volume (P > 0.05). Our results showed the significant potential of the three used models for generating images with different b values in the case of prostate cancer. The results demonstrated that the used three models were accurate and robust for generating DW images and also, they outperformed other methods mentioned in the literature review.
Applied Magnetic Resonance – Springer Journals
Published: Oct 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.