In Vivo Demonstration of Photoacoustic Image Guidance and Robotic Visual Servoing for Cardiac Catheter-Based Interventions.

Cardiac interventional procedures are often performed under fluoroscopic guidance, exposing both the patient and operators to ionizing radiation. To reduce this risk of radiation exposure, we are exploring the use of photoacoustic imaging paired with robotic visual servoing for cardiac catheter visualization and surgical guidance. A cardiac catheterization procedure was performed on two in vivo swine after inserting an optical fiber into the cardiac catheter to produce photoacoustic signals from the tip of the fiber-catheter pair. A combination of photoacoustic imaging and robotic visual servoing was employed to visualize and maintain constant sight of the catheter tip in order to guide the catheter through the femoral or jugular vein, toward the heart. Fluoroscopy provided initial ground truth estimates for 1D validation of the catheter tip positions, and these estimates were refined using a 3D electromagnetic-based cardiac mapping system as the ground truth. The 1D and 3D root mean square errors ranged 0.25-2.28 mm and 1.24-1.54 mm, respectively. The catheter tip was additionally visualized at three locations within the heart: (1) inside the right atrium, (2) in contact with the right ventricular outflow tract, and (3) inside the right ventricle. Lasered regions of cardiac tissue were resected for histopathological analysis, which revealed no laser-related tissue damage, despite the use of 2.98 mJ per pulse at the fiber tip (379.2 mJ/cm2 fluence). In addition, there was a 19 dB difference in photoacoustic signal contrast when visualizing the catheter tip pre- and post-endocardial tissue contact, which is promising for contact confirmation during cardiac interventional procedures (e.g., cardiac radiofrequency ablation). These results are additionally promising for the use of photoacoustic imaging to guide cardiac interventions by providing depth information and enhanced visualization of catheter tip locations within blood vessels and within the beating heart.

In vivo photoacoustic imaging of major blood vessels in the pancreas and liver during surgery.

Abdominal surgeries carry considerable risk of gastrointestinal and intra-abdominal hemorrhage, which could possibly cause patient death. Photoacoustic imaging is one solution to overcome this challenge by providing visualization of major blood vessels during surgery. We investigate the feasibility of in vivo blood vessel visualization for photoacoustic-guided liver and pancreas surgeries. In vivo photoacoustic imaging of major blood vessels in these two abdominal organs was successfully achieved after a laparotomy was performed on two swine. Three-dimensional photoacoustic imaging with a robot-controlled ultrasound (US) probe and color Doppler imaging were used to confirm vessel locations. Blood vessels in the in vivo liver were visualized with energies of 20 to 40 mJ, resulting in 10 to 15 dB vessel contrast. Similarly, an energy of 36 mJ was sufficient to visualize vessels in the pancreas with up to 17.3 dB contrast. We observed that photoacoustic signals were more focused when the light source encountered a major vessel in the liver. This observation can be used to distinguish major blood vessels in the image plane from the more diffuse signals associated with smaller blood vessels in the surrounding tissue. A postsurgery histopathological analysis was performed on resected pancreatic and liver tissues to explore possible laser-related damage. Results are generally promising for photoacoustic-guided abdominal surgery when the US probe is fixed and the light source is used to interrogate the surgical workspace. These findings are additionally applicable to other procedures that may benefit from photoacoustic-guided interventional imaging of the liver and pancreas (e.g., biopsy and guidance of radiofrequency ablation lesions in the liver).

Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning.

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these types of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with -Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2-D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of -9dB or greater, with a severe decrease in performance below -21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNN-based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.