{"title":"基于量子去噪的 250 兆赫和 500 兆赫定量声学显微镜分辨率增强框架","authors":"Sayantan Dutta;Jonathan Mamou","doi":"10.1109/TCI.2024.3473312","DOIUrl":null,"url":null,"abstract":"Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of acoustic properties of thin tissue sections at a microscopic scale (\n<inline-formula><tex-math>$< 8\\; \\mu$</tex-math></inline-formula>\nm) using very-high-frequency (i.e., \n<inline-formula><tex-math>$>$</tex-math></inline-formula>\n 200 MHz) ultrasonic excitation. Our custom-made QAM systems employ a 250-MHz or a 500-MHz single-element transducer to produce 2D maps with theoretical spatial resolutions smaller than 8 \n<inline-formula><tex-math>$\\mu$</tex-math></inline-formula>\nm and 4 \n<inline-formula><tex-math>$\\mu$</tex-math></inline-formula>\nm, respectively. Even with the utilization of these state-of-the-art QAM instruments, spatial resolution still proves insufficient for certain clinical studies. However, designing a QAM system yielding finer resolution (i.e., using a higher-frequency transducer) is expensive and requires expert users. This work proposes a scheme to enhance the spatial resolution of the 2D QAM maps by exploiting an off-the-shelf quantum-based adaptive denoiser (DeQuIP), leveraging the principles of quantum many-body theory. Drawing upon the recent advancement in regularization-by-denoising (RED) for image restoration, we impose this external DeQuIP denoiser as a RED-prior coupled with an analytical solution to address the degradation operators in solving the QAM super-resolution problem. The efficiency of our proposed scheme is demonstrated by improving the resolution of experimental 2D acoustic-impedance maps (2DZMs) generated from data acquired using the 250-MHz and 500-MHz QAM systems. Our scheme demonstrates superior performance in recovering finer and subtle details with enhanced spatial resolution when applied to 2DZMs. For example, a spatial resolution improvement of 40% was achieved when applied to 2DZMs at 250-MHz, outperforming two other state-of-the-art methods, which only yielded 23–32% improvement. These observations highlight the efficacy of the proposed RED scheme.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1489-1504"},"PeriodicalIF":4.2000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Quantum Denoising-Based Resolution Enhancement Framework for 250-MHz and 500-MHz Quantitative Acoustic Microscopy\",\"authors\":\"Sayantan Dutta;Jonathan Mamou\",\"doi\":\"10.1109/TCI.2024.3473312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of acoustic properties of thin tissue sections at a microscopic scale (\\n<inline-formula><tex-math>$< 8\\\\; \\\\mu$</tex-math></inline-formula>\\nm) using very-high-frequency (i.e., \\n<inline-formula><tex-math>$>$</tex-math></inline-formula>\\n 200 MHz) ultrasonic excitation. Our custom-made QAM systems employ a 250-MHz or a 500-MHz single-element transducer to produce 2D maps with theoretical spatial resolutions smaller than 8 \\n<inline-formula><tex-math>$\\\\mu$</tex-math></inline-formula>\\nm and 4 \\n<inline-formula><tex-math>$\\\\mu$</tex-math></inline-formula>\\nm, respectively. Even with the utilization of these state-of-the-art QAM instruments, spatial resolution still proves insufficient for certain clinical studies. However, designing a QAM system yielding finer resolution (i.e., using a higher-frequency transducer) is expensive and requires expert users. This work proposes a scheme to enhance the spatial resolution of the 2D QAM maps by exploiting an off-the-shelf quantum-based adaptive denoiser (DeQuIP), leveraging the principles of quantum many-body theory. Drawing upon the recent advancement in regularization-by-denoising (RED) for image restoration, we impose this external DeQuIP denoiser as a RED-prior coupled with an analytical solution to address the degradation operators in solving the QAM super-resolution problem. The efficiency of our proposed scheme is demonstrated by improving the resolution of experimental 2D acoustic-impedance maps (2DZMs) generated from data acquired using the 250-MHz and 500-MHz QAM systems. Our scheme demonstrates superior performance in recovering finer and subtle details with enhanced spatial resolution when applied to 2DZMs. For example, a spatial resolution improvement of 40% was achieved when applied to 2DZMs at 250-MHz, outperforming two other state-of-the-art methods, which only yielded 23–32% improvement. These observations highlight the efficacy of the proposed RED scheme.\",\"PeriodicalId\":56022,\"journal\":{\"name\":\"IEEE Transactions on Computational Imaging\",\"volume\":\"10 \",\"pages\":\"1489-1504\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Imaging\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10704947/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Imaging","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10704947/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Quantum Denoising-Based Resolution Enhancement Framework for 250-MHz and 500-MHz Quantitative Acoustic Microscopy
Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of acoustic properties of thin tissue sections at a microscopic scale (
$< 8\; \mu$
m) using very-high-frequency (i.e.,
$>$
200 MHz) ultrasonic excitation. Our custom-made QAM systems employ a 250-MHz or a 500-MHz single-element transducer to produce 2D maps with theoretical spatial resolutions smaller than 8
$\mu$
m and 4
$\mu$
m, respectively. Even with the utilization of these state-of-the-art QAM instruments, spatial resolution still proves insufficient for certain clinical studies. However, designing a QAM system yielding finer resolution (i.e., using a higher-frequency transducer) is expensive and requires expert users. This work proposes a scheme to enhance the spatial resolution of the 2D QAM maps by exploiting an off-the-shelf quantum-based adaptive denoiser (DeQuIP), leveraging the principles of quantum many-body theory. Drawing upon the recent advancement in regularization-by-denoising (RED) for image restoration, we impose this external DeQuIP denoiser as a RED-prior coupled with an analytical solution to address the degradation operators in solving the QAM super-resolution problem. The efficiency of our proposed scheme is demonstrated by improving the resolution of experimental 2D acoustic-impedance maps (2DZMs) generated from data acquired using the 250-MHz and 500-MHz QAM systems. Our scheme demonstrates superior performance in recovering finer and subtle details with enhanced spatial resolution when applied to 2DZMs. For example, a spatial resolution improvement of 40% was achieved when applied to 2DZMs at 250-MHz, outperforming two other state-of-the-art methods, which only yielded 23–32% improvement. These observations highlight the efficacy of the proposed RED scheme.
期刊介绍:
The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.