Dynamics-aware deep predictive adaptive scanning optical coherence tomography.

IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Biomedical optics express Pub Date : 2024-12-17 eCollection Date: 2025-01-01 DOI:10.1364/BOE.545165
Dhyey Manish Rajani, Federico Seghizzi, Yang-Lun Lai, Koerner Gray Buchta, Mark Draelos
{"title":"Dynamics-aware deep predictive adaptive scanning optical coherence tomography.","authors":"Dhyey Manish Rajani, Federico Seghizzi, Yang-Lun Lai, Koerner Gray Buchta, Mark Draelos","doi":"10.1364/BOE.545165","DOIUrl":null,"url":null,"abstract":"<p><p>Conventional scanned optical coherence tomography (OCT) suffers from the frame rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. To overcome this limitation, we propose two variants of machine learning (ML)-based adaptive scanning approaches: one using a ConvLSTM-based sequential prediction model and another leveraging a temporal attention unit (TAU)-based parallel prediction model for scene dynamics prediction. These models are integrated with a kinodynamic path planner based on the clustered traveling salesperson problem to create two versions of ML-based adaptive scanning pipelines. Through experimental validation with novel deterministic phantoms based on a digital light processing board, our techniques achieved mean frame rate speed-ups of up to 40% compared to conventional raster scanning and the probabilistic adaptive scanning method without compromising image quality. Furthermore, these techniques reduced scene-dependent manual tuning of system parameters to demonstrate better generalizability across scenes of varying types, including those of intrasurgical relevance. In a real-time surgical tool tracking experiment, our technique achieved an average speed-up factor of over 3.2× compared to conventional scanning methods, without compromising image quality.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 1","pages":"186-207"},"PeriodicalIF":2.9000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729299/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical optics express","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1364/BOE.545165","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Conventional scanned optical coherence tomography (OCT) suffers from the frame rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. To overcome this limitation, we propose two variants of machine learning (ML)-based adaptive scanning approaches: one using a ConvLSTM-based sequential prediction model and another leveraging a temporal attention unit (TAU)-based parallel prediction model for scene dynamics prediction. These models are integrated with a kinodynamic path planner based on the clustered traveling salesperson problem to create two versions of ML-based adaptive scanning pipelines. Through experimental validation with novel deterministic phantoms based on a digital light processing board, our techniques achieved mean frame rate speed-ups of up to 40% compared to conventional raster scanning and the probabilistic adaptive scanning method without compromising image quality. Furthermore, these techniques reduced scene-dependent manual tuning of system parameters to demonstrate better generalizability across scenes of varying types, including those of intrasurgical relevance. In a real-time surgical tool tracking experiment, our technique achieved an average speed-up factor of over 3.2× compared to conventional scanning methods, without compromising image quality.

动态感知深度预测自适应扫描光学相干层析成像。
传统的扫描光学相干断层扫描(OCT)受到帧速率/分辨率权衡的影响,即图像分辨率的增加导致最大可实现帧速率的降低。为了克服这一限制,我们提出了两种基于机器学习(ML)的自适应扫描方法:一种使用基于convlstm的顺序预测模型,另一种利用基于时间注意单元(TAU)的并行预测模型进行场景动态预测。将这些模型与基于聚类旅行销售人员问题的动态路径规划器相结合,创建了两个版本的基于ml的自适应扫描管道。通过基于数字光处理板的新型确定性幻影的实验验证,与传统的光栅扫描和概率自适应扫描方法相比,我们的技术在不影响图像质量的情况下实现了高达40%的平均帧速率加速。此外,这些技术减少了依赖场景的系统参数手动调优,从而在不同类型的场景(包括手术内相关的场景)中表现出更好的通用性。在实时手术工具跟踪实验中,与传统扫描方法相比,我们的技术实现了超过3.2倍的平均加速因子,而不影响图像质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biomedical optics express
Biomedical optics express BIOCHEMICAL RESEARCH METHODS-OPTICS
CiteScore
6.80
自引率
11.80%
发文量
633
审稿时长
1 months
期刊介绍: The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including: Tissue optics and spectroscopy Novel microscopies Optical coherence tomography Diffuse and fluorescence tomography Photoacoustic and multimodal imaging Molecular imaging and therapies Nanophotonic biosensing Optical biophysics/photobiology Microfluidic optical devices Vision research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信