Super-Resolution Reconstruction Algorithm-Based MRI Diagnosis of Prostate Cancer and Evaluation of Treatment Effect of Prostate Specific Antigen

MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were se...

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Published in:	Concepts in magnetic resonance. Part A, Bridging education and research Vol. 2022; no. 1; pp. 1 - 7
Main Authors:	Liu, Biao, Tan, Rongping, Tan, Baogao, Huang, Chenhui, Yang, Keqin
Format:	Journal Article
Language:	English
Published:	Hoboken Hindawi 15.10.2022 John Wiley & Sons, Inc
Subjects:	Algorithms Antigens Biopsy Cancer Care and treatment Clinical medicine Diagnosis Disease Health aspects Magnetic resonance imaging Medical diagnosis Medical imaging equipment Prostate cancer Prostate-specific antigen Tumors China
ISSN:	1546-6086, 1552-5023
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Abstract	MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA examination, and MRI combined with PSA based on a super-resolution reconstruction algorithm were then compared. The simulation results showed that the POCS algorithm was superior to the bilinear interpolation results and was superior to the common POCS algorithm. After adding noise artificially, the restoration algorithm was effective and could preserve the details in the image. The performance indexes of PSA in the diagnosis of PCa were 75.4%, 60.1%, 70.08%, 72.2%, and 60.3%, respectively; the performance indexes of MRI in the diagnosis of PCa were 84.6%, 61.4%, 71.11%, 73.08%, and 61.9%, respectively; and the performance indexes of MRI combined with PSA based on the super-resolution reconstruction algorithm in the diagnosis of PCa were 96.05%, 88.3%, 95.1%, 93.6%, and 92.7%, respectively. The indicators of MRI combined with PSA based on the super-resolution reconstruction algorithm were significantly higher than those of the other two methods (P < 0.05). The signal-to-noise ratio of MRI of PCa based on the super-resolution reconstruction algorithm has been greatly improved, with good clarity, which can improve the diagnostic accuracy of PCa patients and has certain advantages in the examination. MRI based on the super-resolution reconstruction algorithm has a high value in the diagnosis and treatment of PCa.
AbstractList	MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super‐resolution reconstruction algorithm to evaluate and analyze the treatment of prostate‐specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA examination, and MRI combined with PSA based on a super‐resolution reconstruction algorithm were then compared. The simulation results showed that the POCS algorithm was superior to the bilinear interpolation results and was superior to the common POCS algorithm. After adding noise artificially, the restoration algorithm was effective and could preserve the details in the image. The performance indexes of PSA in the diagnosis of PCa were 75.4%, 60.1%, 70.08%, 72.2%, and 60.3%, respectively; the performance indexes of MRI in the diagnosis of PCa were 84.6%, 61.4%, 71.11%, 73.08%, and 61.9%, respectively; and the performance indexes of MRI combined with PSA based on the super‐resolution reconstruction algorithm in the diagnosis of PCa were 96.05%, 88.3%, 95.1%, 93.6%, and 92.7%, respectively. The indicators of MRI combined with PSA based on the super‐resolution reconstruction algorithm were significantly higher than those of the other two methods (P < 0.05). The signal‐to‐noise ratio of MRI of PCa based on the super‐resolution reconstruction algorithm has been greatly improved, with good clarity, which can improve the diagnostic accuracy of PCa patients and has certain advantages in the examination. MRI based on the super‐resolution reconstruction algorithm has a high value in the diagnosis and treatment of PCa. MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA examination, and MRI combined with PSA based on a super-resolution reconstruction algorithm were then compared. The simulation results showed that the POCS algorithm was superior to the bilinear interpolation results and was superior to the common POCS algorithm. After adding noise artificially, the restoration algorithm was effective and could preserve the details in the image. The performance indexes of PSA in the diagnosis of PCa were 75.4%, 60.1%, 70.08%, 72.2%, and 60.3%, respectively; the performance indexes of MRI in the diagnosis of PCa were 84.6%, 61.4%, 71.11%, 73.08%, and 61.9%, respectively; and the performance indexes of MRI combined with PSA based on the super-resolution reconstruction algorithm in the diagnosis of PCa were 96.05%, 88.3%, 95.1%, 93.6%, and 92.7%, respectively. The indicators of MRI combined with PSA based on the super-resolution reconstruction algorithm were significantly higher than those of the other two methods ( P < 0.05). The signal-to-noise ratio of MRI of PCa based on the super-resolution reconstruction algorithm has been greatly improved, with good clarity, which can improve the diagnostic accuracy of PCa patients and has certain advantages in the examination. MRI based on the super-resolution reconstruction algorithm has a high value in the diagnosis and treatment of PCa.
Audience	Academic
Author	Yang, Keqin Tan, Rongping Liu, Biao Huang, Chenhui Tan, Baogao
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Cites_doi	10.15585/mmwr.mm6941a1 10.1007/978-3-319-95693-0_1 10.1007/s11934-019-0922-4 10.3389/fnins.2021.705323 10.1038/s41585-020-00400-w 10.23736/S0393-2249.17.02976-9 10.3390/ijms21041447 10.1016/j.euo.2020.03.002 10.1111/iju.13512 10.1111/iju.13593 10.1002/cncr.31860 10.3390/ijms22041551 10.12688/f1000research.15382.1 10.3390/ijms222312777 10.3390/biom10111536 10.46883/ONC.2021.3510.0645 10.6004/jnccn.2019.5011 10.3390/ijms22073753 10.1186/s40880-019-0425-1 10.1097/CCO.0000000000000624 10.1146/annurev-med-051517-011947 10.1111/bju.15153 10.32604/cmes.2021.016728
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Snippet	MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the... MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super‐resolution reconstruction algorithm to evaluate and analyze the...
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SubjectTerms	Algorithms Antigens Biopsy Cancer Care and treatment Clinical medicine Diagnosis Disease Health aspects Magnetic resonance imaging Medical diagnosis Medical imaging equipment Prostate cancer Prostate-specific antigen Tumors
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Title	Super-Resolution Reconstruction Algorithm-Based MRI Diagnosis of Prostate Cancer and Evaluation of Treatment Effect of Prostate Specific Antigen
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