Photoacoustic imaging in percutaneous radiofrequency ablation: device guidance and ablation visualization

Thao

et al. 2020

Sensors

Correct guiding of the catheter is a critical issue in almost all balloon catheter applications, including arterial stenosis expansion, coronary arterial diseases, and gastrointestinal tracking. To achieve safe and precise guiding of the balloon catheter, a novel imaging method with high-resolution, sufficient depth of penetration, and real-time display is required. Here, we present a new balloon catheter guiding method using fast photoacoustic microscopy (PAM) technique for precise balloon catheter tracking and visualization as a feasibility study. We implemented ex vivo and in vivo experiments with three different medium conditions of balloon catheter: no air, air, and water. Acquired cross-sectional, maximum amplitude projection (MAP), and volumetric 3D PAM images demonstrated its capability as a new imaging guiding tool for balloon catheter tracking and visualization.

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Precise Balloon Catheter Tracking and Visualization with Fast Photoacoustic Microscopy

Thao

et al. 2020

Sensors

“…In future work, we will investigate the feasibility for automated determination of the low order cutoff threshold by estimating the mean frequency of each temporal singular vectors contained in the matrix V. 30 In this paper, the focus was on in vivo murine cardiac PAI where the myocardial signals are diffuse in nature. PAI has also been used for imaging prostate brachytherapy seeds, 52,53 percutaneous radiofrequency ablation needle detection, 54 and surgical guidance 7 where the signals of interest appear to be more coherent. We anticipate that our proposed spatiotemporal SVD processing can be applied for those applications with appropriate adjustment of singular value thresholds.…”

Section: Resultsmentioning

confidence: 99%

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

et al. 2021

. Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

“…In addition, the SNR enhancement provided with GPU-SLSC is expected to be beneficial in other applications with high noise and high acoustic scattering or attenuation, such as navigating inside bony anatomy during spinal fusion surgeries 51 or endonasal transphenoidal surgeries 16 and navigating within liver tissue during surgeries, biopsies, or radio frequency ablations. 70,78…”

Section: Discussionmentioning

confidence: 99%

GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions

González

Bell

2020

J. Biomed. Opt.

Significance: Photoacoustic-based visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, one outstanding challenge has been the reliability of obtaining segmentations using low-energy light sources that operate within existing laser safety limits. Aim: We developed the first known graphical processing unit (GPU)-based real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applied this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers. Approach: A 1-mm-core-diameter optical fiber was inserted into ex vivo bovine tissue. Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. Performance was additionally evaluated with in vivo cardiac data. Results: The GPU-SLSC implementation achieved frame rates up to 41.2 Hz, representing a factor of 348 speedup when compared with offline CPU-SLSC. In addition, GPU-SLSC successfully recovered low-energy signals (i.e., ≤268 μJ) with mean ± standard deviation of signal-to-noise ratios of 11.2 AE 2.4 (compared with 3.5 AE 0.8 with conventional DAS beamforming). When energies were lower than the safety limit for skin (i.e., 394.6 μJ for 900-nm wavelength laser light), the median and interquartile range (IQR) of visual servoing tracking errors obtained with GPU-SLSC were 0.64 and 0.52 mm, respectively (which were lower than the median and IQR obtained with DAS by 1.39 and 8.45 mm, respectively). GPU-SLSC additionally reduced the percentage of failed segmentations when applied to in vivo cardiac data. Conclusions: Results are promising for the use of low-energy, miniaturized lasers to perform GPU-SLSC photoacoustic-based visual servoing in the operating room with laser pulse repetition frequencies as high as 41.2 Hz.