{"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.
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.