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Gao S, Ashikaga H, Suzuki M, Mansi T, Kim YH, Ghesu FC, Kang J, Boctor EM, Halperin HR, Zhang HK. Cardiac-gated spectroscopic photoacoustic imaging for ablation-induced necrotic lesion visualization. JOURNAL OF BIOPHOTONICS 2024:e202400126. [PMID: 39075610 DOI: 10.1002/jbio.202400126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/02/2024] [Accepted: 07/04/2024] [Indexed: 07/31/2024]
Abstract
Radiofrequency (RF) ablation is a minimally invasive therapy for atrial fibrillation. Conventional RF procedures lack intraoperative monitoring of ablation-induced necrosis, complicating assessment of completeness. While spectroscopic photoacoustic (sPA) imaging shows promise in distinguishing ablated tissue, multi-spectral imaging is challenging in vivo due to low imaging quality caused by motion. Here, we introduce a cardiac-gated sPA imaging (CG-sPA) framework to enhance image quality using a motion-gated averaging filter, relying on image similarity. Necrotic extent was calculated based on the ratio between spectral unmixed ablated tissue contrast and total tissue contrast, visualizing as a continuous color map to highlight necrotic area. The validation of the concept was conducted in both ex vivo and in vivo swine models. The ablation-induced necrotic lesion was successfully detected throughout the cardiac cycle through CG-sPA imaging. The results suggest the CG-sPA imaging framework has great potential to be incorporated into clinical workflow to guide ablation procedures intraoperatively.
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Affiliation(s)
- Shang Gao
- Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| | - Hiroshi Ashikaga
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Masahito Suzuki
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tommaso Mansi
- Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, USA
| | - Young-Ho Kim
- Siemens Healthineers, Digital Technology and Innovation, Princeton, New Jersey, USA
| | | | - Jeeun Kang
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Whiting School of Engineering, Baltimore, Maryland, USA
| | - Emad M Boctor
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Whiting School of Engineering, Baltimore, Maryland, USA
| | - Henry R Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Haichong K Zhang
- Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
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Hui X, Rajendran P, Ling T, Dai X, Xing L, Pramanik M. Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth. PHOTOACOUSTICS 2023; 34:100575. [PMID: 38174105 PMCID: PMC10761306 DOI: 10.1016/j.pacs.2023.100575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/15/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.
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Affiliation(s)
- Xie Hui
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Praveenbalaji Rajendran
- Stanford University, Department of Radiation Oncology, Stanford, California 94305, United States
| | - Tong Ling
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 637459, Singapore
| | - Xianjin Dai
- Stanford University, Department of Radiation Oncology, Stanford, California 94305, United States
| | - Lei Xing
- Stanford University, Department of Radiation Oncology, Stanford, California 94305, United States
| | - Manojit Pramanik
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States
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