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Real-Time Therapy Guidance Using Deep Learning- Assisted Focused Ultrasound Transducers

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dc.contributor.advisor 장진호 -
dc.contributor.author Yujeong shin -
dc.date.accessioned 2026-01-23T10:57:20Z -
dc.date.available 2026-01-23T10:57:20Z -
dc.date.issued 2026 -
dc.identifier.uri https://scholar.dgist.ac.kr/handle/20.500.11750/59731 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000947874 -
dc.description Focused Ultrasound (FUS), Deep-Learning, Barker Code, Deconvolution, Real-Time -
dc.description.tableofcontents Ⅰ. INTRODUCTION 1
1.1 Focused Ultrasound 1
1.2 Image Guidance and Monitoring 2
1.3 Integrated Therapy and Imaging for USgFUS 3
1.4 Limitations of Rule-Based Imaging for Linear Array FUS Transducers 5
1.5 CUDA Architecture for Real-Time System 6
1.6 Objectives of Research 7
ⅠI. METHODS 9
2.1 Overview of Proposed Framework 9
2.2 Rule-Based Imaging Pipeline and Ground Truth Generation 12
2.2.1 Channel-Level Ultrasound Signal Model 12
2.2.2 Wiener Deconvolution for Axial Resolution Enhancement 13
2.2.3 Barker Code Compression Using PSL Mismatched Filter 14
2.2.4 Band-Pass Filtering and Normalization 15
2.2.5 Ground Truth Generation Procedure 15
2.3 Deep Learning Models for Scanline-Level RF Enhancement 16
2.3.1 Problem Formulation 16
2.3.2 Network Architecture 18
2.3.3 Training Strategy and Loss Function 18
2.4 CUDA-Based Real-Time System Architecture 19
2.4.1 GPU-Based Processing Pipeline 20
2.4.2 Deep-Learning Inference with TensorRT 21
2.4.3 Multi-Stream Execution for Real-Time Processing 22
ⅠII. RESULTS 24
3.1 Comparison of Deep Learning Models 24
3.2 Experimental Setup and Data Acquisition for Imaging Experiments 26
3.3 Quantitative Image Quality Evaluation 30
3.3.1 Evaluation on Axial Resolution 30
3.3.2 Evaluation on CNR and SNR 32
3.3.3 Evaluation on Ex-Vivo 33
3.4 Evaluation on Computational Efficiency and Frame Rate 36
3.4.1 Comparison between Rule-Based and Deep Learning Pipelines 36
3.4.2 Effect of Multi-Stream Processing on Frame Rate 37
IV. DISCUSSION AND CONCLUSION 38
REFERENCES 41		 -
dc.format.extent 44 -
dc.language eng -
dc.publisher DGIST -
dc.title Real-Time Therapy Guidance Using Deep Learning- Assisted Focused Ultrasound Transducers -
dc.type Thesis -
dc.identifier.doi 10.22677/THESIS.200000947874 -
dc.description.degree Master -
dc.contributor.department Artificial Intelligence Major -
dc.date.awarded 2026-02-01 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.AM 신66 202602 -
dc.date.accepted 2026-01-19 -
dc.contributor.alternativeDepartment 학제학과인공지능전공 -
dc.subject.keyword Focused Ultrasound (FUS), Deep-Learning, Barker Code, Deconvolution, Real-Time -
dc.contributor.affiliatedAuthor Yujeong shin -
dc.contributor.affiliatedAuthor Jin Ho Chang -
dc.contributor.alternativeName 신유정 -
dc.contributor.alternativeName Jin Ho Chang -
dc.rights.embargoReleaseDate 2031-02-28 -
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