Publications

Journal Paper Accepted to IEEE TBME

Congratulations to Mardava Gubbi! His first-author paper entitled “In Vivo Demonstration of Deep Learning-Based Photoacoustic Visual Servoing System” was accepted for publication in IEEE Transactions on Biomedical Engineering.

This paper is the first to present a real-time deep learning-based photoacoustic visual servoing system utilizing both object detection and instance segmentation to estimate catheter tip positions in three spatial dimensions from a single frame of two-dimensional of raw photoacoustic channel data. This system was designed with two features not present in previous amplitude-based or deep learning-based photoacoustic visual servoing systems. First, we estimated the location of the catheter tip along the elevation dimension of the transducer with the input being a single frame of two-dimensional of raw channel data. Second, the integration of force control into our visual servoing system improved the tracking of targets across uneven imaging surfaces. These features improve the potential of our novel visual servoing system for clinical translation in cardiac catheterizations procedures (and other minimally invasive interventional procedures).

Citation: M. R. Gubbi, A. Kolandaivelu, N. Venkatayogi, J. Zhang, P. Warbal, G. C. Keene, M. Khairalseed, J. Chrispin, and M. A. Lediju Bell, “In Vivo Demonstration of Deep Learning-Based Photoacoustic Visual Servoing System”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted June 23, 2025) [pdf]

Journal Paper Accepted to IEEE T-UFFC

Congratulations to Mardava Gubbi! His first-author paper entitled “Deep learning to localize photoacoustic sources in three dimensions: Theory and implementation” was accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

This paper introduces two novel deep learning-based three-dimensional (3D) photoacoustic point source localization systems. One system uses an object detection-based approach with elevation-encoded classification (System A). The second system uses our novel theoretical framework relating the three-dimensional location of photoacoustic point sources, sound speeds, and waveform shapes in photoacoustic raw channel data (System B). System B leverages our theoretical framework to implement a novel instance segmentation and least squares optimization-based photoacoustic point source location and sound speed estimation approach. This instance segmentation-based system improves localization and sound speed estimation performance in simulated, phantom, and ex vivo data compared to traditional methods. Overall, the results enhance the potential applicability of photoacoustic guidance to surgical and interventional scenarios.

Citation: M. R. Gubbi and M. A. Lediju Bell, Deep Learning to Localize Photoacoustic Sources in Three Dimensions: Theory and Implementation, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted April 15, 2025) [pdf]

Two Journal Papers Recently Published in BOE and UMB

Congratulations to Nethra and Junhao! Their first-author papers were recently published in Biomedical Optics Express and Ultrasound in Medicine and Biology, respectively.

Multispectral photoacoustic imaging of breast cancer tissue with histopathology validation describes the first known work to use multispectral photoacoustic imaging with optical wavelengths up to 2000 nm to provide pathological information about breast tissue without traditional staining staining. We leveraged machine learning to differentiate between healthy and cancerous tissues based on photoacoustic spectra. Using our custom imaging platform, this method generates a virtual stain of a microscope tissue slide in under 30 minutes, compared to the more than 24 hours required for conventional H&E staining. The co-registered ultrasound images provide complementary anatomical context to the pathological information derived from multispectral photoacoustic images. Our approach has the potential to enable real-time tumor margin determination during biopsy or surgery.

Comparative Assessment of Real-Time and Offline Short-Lag Spatial Coherence (SLSC) Imaging of Ultrasound Breast Masses is the first to evaluate the effectiveness of real-time SLSC imaging, relative to historical offline SLSC implementations, when differentiating fluid from solid masses. Real-time SLSC imaging reduced uncertainty about fluid masses that existed when reading B-mode images alone. The application of the generalized contrast-to-noise ratio (gCNR) to real-time SLSC images further enhanced the sensitivity and specificity of cystic versus solid differentiation to overcome residual reader uncertainty. These benefits are promising to reduce 2-year follow-up recommendations (i.e., BI-RADS 3 diagnoses) or recommendations for more invasive procedures (e.g., biopsy, aspiration).

Research Featured on Cover of Biophotonics Discovery

Congratulations to Junior Arroyo! His first-author publication is featured on the cover of Biophotonics Discovery. The figure is from the article “Predictive model for laser-induced tissue necrosis with immunohistochemistry validation” by J. Junior Arroyo, Arunima Sharma, Jiaxin Zhang, and Muyinatu A. Lediju Bell.

This work is the first to demonstrate a unified theoretical, computational, and experimental approach with quantitative immunohistochemistry (IHC) validation to determine laser safety for biological tissues other than skin or eyes. Although photoacoustic imaging has the potential to provide critical guidance in surgical interventions, its widespread use is challenged by the absence of applicable safety guidelines across diverse target tissues. Maximum permissible exposure (MPE) guidelines currently focus solely on skin and eyes. Results are promising to provide tissue-specific MPE guidelines to maintain healthy liver tissue during laser-based optical and photoacoustic surgeries and interventions. The presented approach and associated outcomes are promising for the introduction of tissue-specific safety guidelines for photoacoustic imaging and other optics-based imaging technologies that are designed to maximize signal-to-noise ratios while being designated as safe for patient use. In addition, the presented simulation framework and corresponding experimental protocols may be applied to other internal organs to achieve similar benefits.

Citation: Arroyo J, Sharma A, Zhang J, Bell MAL, Predictive model for laser-induced tissue necrosis with immunohistochemistry validation, Biophotonics Discovery 1(2):025003, 2024 [pdf]

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Journal Paper Accepted to IEEE T-UFFC

Congratulations to Md Ashikuzzaman! His first-author paper entitled MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography was accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

This work is the first to introduce an L1-norm-based second-order regularizer in a mechanically-inspired total strain tensor imaging framework named — L1-norm Mixed derivative for Total UltRasound Elastography — or, L1-MixTURE when abbreviated. Displacement tracking of ultrasound images can be implemented by optimizing a cost function consisting of a data term, a mechanical congruency term, and first- and second-order continuity terms. This approach recently provided a promising solution to two-dimensional axial and lateral displacement tracking in ultrasound strain elastography. However, the associated second-order regularizer only considers the unmixed second derivatives and disregards the mixed derivatives, thereby providing suboptimal noise suppression and limiting possibilities for total strain tensor imaging. We improved axial, lateral, axial shear, and lateral shear strain estimation quality by formulating and optimizing a novel L1-norm-based second-order regularizer that penalizes both mixed and unmixed displacement derivatives. Results are promising to advance the state-of-the-art in elastography-guided diagnostic and interventional decisions.

Citation: Ashikuzzaman M, Sharma A, Venkatayogi N, Oluyemi E, Myers K, Ambinder E, Rivaz H, Bell MAL, MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted August 13, 2024) [pdf]

Journal Paper Accepted to Biophotonics Discovery

Congratulations to Junior Arroyo! His first-author paper entitled Predictive model for laser-induced tissue necrosis with immunohistochemistry validation was accepted for publication in the newest SPIE journal, Biophotonics Discovery.

This work is the first to demonstrate a unified theoretical, computational, and experimental approach with quantitative immunohistochemistry (IHC) validation to determine laser safety for biological tissues other than skin or eyes. Although photoacoustic imaging has the potential to provide critical guidance in surgical interventions, its widespread use is challenged by the absence of applicable safety guidelines across diverse target tissues. Maximum permissible exposure (MPE) guidelines currently focus solely on skin and eyes. Results are promising to provide tissue-specific MPE guidelines to maintain healthy liver tissue during laser-based optical and photoacoustic surgeries and interventions. The presented approach and associated outcomes are promising for the introduction of tissue-specific safety guidelines for photoacoustic imaging and other optics-based imaging technologies that are designed to maximize signal-to-noise ratios while being designated as safe for patient use. In addition, the presented simulation framework and corresponding experimental protocols may be applied to other internal organs to achieve similar benefits.

Citation: Arroyo J, Sharma A, Zhang J, Bell MAL, Predictive model for laser-induced tissue necrosis with immunohistochemistry validation, Biophotonics Discovery 1(2):025003, 2024 [pdf]

Peer-Reviewed Paper Accepted to IEEE ISBI

Congratulations to Md Ashikuzzaman! His paper, Deep learning-based displacement tracking for post-stroke myofascial shear strain quantification, was accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI),  Athens, Greece, May 27-30, 2024. This peer-reviewed paper was also accepted for inclusion in the conference proceedings.

Congrats again on this significant achievement and milestone, Ashik!

Citation: M Ashikuzzaman, J Huang, S Bonwit, A Etemadimanesh, P Raghavan, MAL Bell, Deep learning-based displacement tracking for post-stroke myofascial shear strain quantification, IEEE International Symposium on Biomedical Imaging (ISBI),  Athens, Greece, May 27-30, 2024

Paper Published in Nature Communications Medicine

Congratulations to former PULSE Lab postdoc Lingyi Zhao! Her first-author journal paper entitled Detection of COVID-19 features in lung ultrasound images using deep neural networks was published in Communications Medicine, which is a Nature Publishing group journal). This paper is the first to demonstrate that simulations can be used to train deep neural networks to detect COVID-19 features in lung ultrasound images of patients. Therefore, DNNs trained with simulated and in vivo data are promising alternatives to training with only real or only simulated data when segmenting in vivo COVID-19 lung ultrasound features.

We offer public access (https://gitlab.com/pulselab/covid19) to the datasets and code described in the paper “Detection of COVID-19 features in lung ultrasound images using deep neural networks.” Communications Medicine, 2024. https://www.nature.com/articles/s43856-024-00463-5.

Access includes simulated B-mode images containing A-line, B-line, and consolidation features with paired ground truth segmentations, as well as our segmentation annotations of publicly available point of care ultrasound (POCUS) datasets (originating from https://github.com/jannisborn/covid19_ultrasound).

If you find our datasets and/or code useful, please cite the following references:

  1. L. Zhao, T.C. Fong, M.A.L. Bell, “Detection of COVID-19 features in lung ultrasound images using deep neural networks”, Communications Medicine, 2024. https://www.nature.com/articles/s43856-024-00463-5
  2. L. Zhao, M.A.L. Bell (2023). Code for the paper “Detection of COVID-19 features in lung ultrasound images using deep neural networks”. Zenodo. https://doi.org/10.5281/zenodo.10324042

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PULSE Lab Research Featured on CNN

Research conducted by PULSE Lab visiting PhD student, Guilherme S. P. Fernandes, and co-advisor, Prof. Muyinatu Bell, is featured on CNN.

The topic of the feature is an algorithm that Prof. Bell invented as a graduate student, which is now revealing capabilities to address skin tone bias in photoacoustic imaging. When using this technique to image through skin with light, darker skin tones produce more acoustic clutter than lighter skin tones, which introduces unwanted biases (e.g., Black patients have worse images than white patients, leading to a disparity in the ability to see the important content needed to make an accurate diagnosis). The novel and innovative algorithm, termed short-lag spatial coherence (SLSC) beamforming, makes clearer pictures for all patients, regardless of skin tone. Prof. Bell collaborated with Prof. Theo Pavan and colleagues at the University of São Paulo in Brazil to test this algorithm on multiple volunteers, and the PULSE Lab hosted Guilherme over the past year to finalize this work, leading to the publication Mitigating skin tone bias in linear array in vivo photoacoustic imaging with short-lag spatial coherence beamforming.

SLSC beamforming was previously shown to reduce acoustic clutter in cardiac ultrasound images and in abdominal ultrasound images of patients with higher body mass indexes. The technique and its derivatives can also be used to clarify the fluid vs. solid content of indeterminate breast masses surrounded by dense breast tissue, thereby reducing unnecessary procedures and follow-ups when trying to detect breast cancer. Additional use cases include clarifying anatomical details in photoacoustic-guided surgery. These multiple examples demonstrate the expansive power, potential, and capabilities of designing more equitable imaging and healthcare technologies that serve a wider range of our global patient population.

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Paper Accepted to Journal of Biomedical Optics

Congratulations to Eduardo González! His first author paper entitled, Dual-wavelength photoacoustic atlas method to estimate fractional methylene blue and hemoglobin contents, was accepted to the Journal of Biomedical Optics.

This work discusses a novel approach to estimate concentration levels from a mixture of two photoacoustic-sensitive materials after only two laser wavelength emissions. The work builds on our previously proposed acoustic atlas alternative to spectral unmixing, and it is the first to present an acoustic-based photoacoustic estimator that relies on training sets to estimate concentration levels from mixtures of photoacoustic-sensitive materials. Results are promising for real-time monitoring of the concentration of contrast agents in the operating room.

Citations:

González EA, Bell MAL, Dual-wavelength photoacoustic atlas method to estimate fractional methylene blue and hemoglobin contents, Journal of Biomedical Optics, 27(9):096002, 2022 [pdf]

González EA, Graham CA, Bell MAL, Acoustic frequency-based approach for identification of photoacoustic surgical biomarkers, Frontiers in Photonics, 2, 2021 [pdf]

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Journal Paper Accepted to Frontiers in Photonics

Journal Paper Accepted to IEEE TUFFC

Congratulations to Mardava Gubbi! His first-author paper entitled Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision was accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

This work is the first to the first to present a novel framework to establish relationships among photoacoustic imaging system parameters, image quality, and computer vision-based task performance. Our framework leverages gCNR to quantify the relationships between system parameters (e.g., channel SNR, laser energy) and photoacoustic image quality. Within this framework, we present a theoretical derivation of gCNR predictions based on using the statistics of the target and background signal powers, then validate these predictions on simulated, experimental, and in vivo data. This framework was then leveraged to quantify the accuracy of a photoacoustic target segmentation algorithm as a function of gCNR and demonstrate the robustness of gCNR to thresholding, with possible extensions to other computer vision-based tasks (e.g., target tracking and image classification) and to improve the overall photoacoustic imaging system design process.

Citation: Gubbi MR, Gonzalez EA, Bell MAL,Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(6):2098-2114, 2022 [pdf]

Mardava Completed the ECE Department Preliminary Research Proposal Seminar

Congratulations to Mardava Gubbi on his successful completion of the ECE Department preliminary thesis research proposal and seminar requirement!

The topics discussed in Mardava’s seminar are summarized in the following peer-reviewed publications:

  1. Gubbi MR, Bell MAL, Deep Learning-Based Photoacoustic Visual Servoing: Using Outputs from Raw Sensor Data as Inputs to a Robot Controller, IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 30 – June 5, 2021 [pdf]
  2. Gubbi MR, Gonzalez EA, Bell MAL,Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(6):2098-2114, 2022 [pdf]
  3. Kempski KM, Graham MT, Gubbi MR, Palmer T, Bell MAL, Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality, Biomedical Optics Express, 11(7), 3684-3698, 2020 [pdf]
  4. Graham M, Assis F, Allman D, Wiacek A, González E, Gubbi M, Dong J, Hou H, Beck S, Chrispin J, Bell MAL, In vivo demonstration of photoacoustic image guidance and robotic visual servoing for cardiac catheter-based interventions, IEEE Transactions on Medical Imaging, 39(4):1015-1029, 2020 [pdf]

Alycen Wiacek Wins AIUM New Investigator Award

Congratulations to Alycen Wiacek for winning the New Investigator Award from the American Institute of Ultrasound in Medicine (AIUM)! Alycen was selected to present her research in the New Investigator Scientific Session Plenary at the AIUM 2022 Annual Meeting. She discussed her three first-author papers on the clinical implications of spatial coherence features for breast ultrasound:

  1. Wiacek A, Rindal OMH, Falomo E, Myers K, Fabrega-Foster K, Harvey S, Bell MAL, Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66(3):527-540, 2019 [pdf]
  2. Wiacek A, Oluyemi E, Myers K, Mullen L, Bell MAL, Coherence-based beamforming increases the diagnostic certainty of distinguishing fluid from solid masses in breast ultrasound exams, Ultrasound in Medicine and Biology, 46(6):1380-1394, 2020 [pdf]
  3. Wiacek A, Oluyemi E, Myers K, Mullen L, Bell MAL, Coherence-based beamforming improves the diagnostic certainty of breast ultrasound exams, Proceedings of the 2020 IEEE International Ultrasonics Symposium, Virtual, September 6-11, 2020 [pdf]

Alycen was selected as the winner of this symposium, alongside Prof. Brooks Lindsey from Georgia Institute of Technology.

Invited Review Published in BMEF

Prof. Bell and Lingyi Zhao co-authored an invited review entitled, A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients, which was recently published in BME Frontiers.

This review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance..

Citation: Lingyi Zhao, Muyinatu A. Lediju Bell, “A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients“, BME Frontiers, vol. 2022, Article ID 9780173, 17 pages, 2022. https://doi.org/10.34133/2022/9780173

Application of gCNR to Photoacoustic Imaging is a Top-Cited Paper

Published less than two years ago in June 2020, Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality received the honor of being a top-cited paper in the journal Biomedical Optics Express in January 2022. This paper investigates a newly developed, probability-based, generalized contrast-to-noise (gCNR) when applied to photoacoustic images. We recommend gCNR as a new standard for assessment of novel photoacoustic beamforming and image formation techniques.

Congratulations to Kelley, Michelle, Mardava, Theron, and Prof. Bell for contributing this foundational development for the photoacoustics and biomedical optics community!

Journal Paper Accepted to Frontiers in Photonics

Congratulations to Eduardo González! His first-author paper entitled Acoustic Frequency-Based Approach for Identification of Photoacoustic Surgical Biomarkers was accepted for publication in the journal Frontiers in Photonics.

This paper demonstrates a novel approach to accurately identify biological markers by analyzing the acoustic frequency response from either a single-wavelength emission (i.e., single-wavelength atlas method) or two consecutive wavelength emissions (i.e., dual-wavelength atlas method). The proposed approach relies on training sets to identify photoacoustic-sensitive materials, is robust against changes in fluence levels, and has comparable sensitivity, specificity, and accuracy to those obtained with conventional and enhanced spectral unmixing methods. This paper is part of the journal’s research topic Biophotonics Technologies for Clinical Translation.

Citation: González EA, Graham CA, Bell MAL, Acoustic frequency-based approach for identification of photoacoustic surgical biomarkers, Frontiers in Photonics, 2, 2021 [pdf]

Photoacoustic-Guided Surgery Review is a Top 10 Download

Within its first month of publication, Photoacoustic-Guided Surgery from Head to Toe, an invited review co-authored by Alycen Wiacek and Prof. Bell, received the honor of being a top 10 download from the journal Biomedical Optics Express. This invited review covers multiple aspects of the use of photoacoustic imaging to guide both surgical and related non-surgical interventions and includes a discussion of complete systems and tools needed to maximize success.

Congratulations to Alycen and Prof. Bell for capturing the attention of the biomedical optics community!

Journal Paper Accepted to IEEE TMI

Congratulations to Alycen Wiacek! Her first-author paper entitled Photoacoustic-guided laparoscopic and open hysterectomy procedures demonstrated with human cadavers was accepted for publication in IEEE Transactions on Medical Imaging.

This work is the first to demonstrate a novel method for photoacoustic image-guided hysterectomies within the realistic imaging environment of a human cadaver during both open and laparoscopic procedures. With a contrast agent injected into the ureter, two laser wavelengths can be used to create a simultaneous display of the ureter and the uterine artery. This dual-wavelength approach was then integrated to create a novel surgical guidance system by estimating the tool-to-ureter distance and mapping that distance to an audible signal, similar to the parking sensor on a modern automobile. This auditory signal is intended to alert surgeons who are operating too closely to the ureter, which can lead to multiple life-threatening complications caused by accidental injury to the ureter during surgery.

Citation: Wiacek A, Wang KC, Wu H, Bell MAL, Photoacoustic-guided laparoscopic and open hysterectomy procedures demonstrated with human cadavers, IEEE Transactions on Medical Imaging (accepted May 13, 2021) [pdf]

Michelle Completed the ECE Department Preliminary Research Proposal Seminar

Congratulations to Michelle Graham on her successful completion of the ECE Department preliminary thesis research proposal and seminar requirement!

Details about Michelle’s proposal seminar are available here: https://engineering.jhu.edu/ece/events/thesis-proposal-michelle-graham/?instance_id=1075#.YLSFAS33ZOk.

The topics discussed in Michelle’s seminar are summarized in the following publications:

Journal Articles

  1. Graham MT, Bell MAL, Photoacoustic Spatial Coherence Theory and Applications to Coherence-Based Image Contrast and Resolution, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(10):2069-2084, 2020 [pdf]
  2. Graham MT, Huang J, Creighton F, Bell MAL, Simulations and human cadaver head studies to identify optimal acoustic receiver locations for minimally invasive photoacoustic-guided neurosurgery, Photoacoustics, 19:100183, 2020 [pdf]

Conference Proceedings

  1. Graham M, Creighton F, Bell MAL, Validation of eyelids as acoustic receiver locations for photoacoustic-guided neurosurgery, Proceedings of SPIE Photonics West, San Francisco, CA, March 6-11, 2021 [pdf]
  2. Graham M, Creighton F, Bell MAL, Investigation of acoustic windows for photoacoustic imaging of intracranial blood vessels, Proceedings of the 2020 IEEE International Ultrasonics Symposium, Virtual, September 6-11, 2020 [pdf]
  3. Graham M, Guo J, Bell MAL, Simultaneous visualization of nerves and blood vessels with multispectral photoacoustic imaging for intraoperative guidance of neurosurgeries, Proceedings of SPIE Photonics West, San Francisco, CA, February 2-7, 2019 [pdf]
  4. Graham M, Bell MAL, Development and validation of a short-lag spatial coherence theory for photoacoustic imaging, Proceedings of SPIE Photonics West, San Francisco, CA, January 28-31, 2018 [pdf]
  5. M Graham, MAL Bell, Theoretical Application of Short-Lag Spatial Coherence to Photoacoustic Imaging, Proceedings of the 2017 IEEE International Ultrasonics Symposium, Washington, DC, September 6-9, 2017 [pdf]

Invited Review Published in Biomedical Optics Express

Prof. Bell and Alycen Wiacek co-authored an invited review entitled, Photoacoustic-Guided Surgery from Head to Toe, which was recently published in Biomedical Optics Express.

This review covers multiple aspects of photoacoustic imaging to guide surgical & related non-surgical interventions, including a discussion of complete systems and tools needed to maximize success.

Citation: Alycen Wiacek and Muyinatu A. Lediju Bell, “Photoacoustic-guided surgery from head to toe [Invited],” Biomed. Opt. Express 12, 2079-2117 (2021) [bibtex]

Alycen Completed the ECE Department Preliminary Research Proposal Seminar

Congratulations to Alycen Wiacek on her successful completion of the ECE Department preliminary thesis research proposal and seminar requirement!

Details about Alycen’s proposal seminar are available here: https://engineering.jhu.edu/ece/events/thesis-proposal-alycen-wiacek/?instance_id=1066#.YGna5khKi8o.

The topics discussed in Alycen’s seminar are summarized in the following publications:

Journal Articles

  1. Wiacek A, Oluyemi E, Myers K, Mullen L, Bell MAL, Coherence-based beamforming increases the diagnostic certainty of distinguishing fluid from solid masses in breast ultrasound exams, Ultrasound in Medicine and Biology, 46(6):1380-1394, 2020 [pdf]
  2. Wiacek A, González E, Bell MAL, CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12):2574-2583, 2020 [featured on journal cover] [pdf]
  3. Wiacek A, Rindal OMH, Falomo E, Myers K, Fabrega-Foster K, Harvey S, Bell MAL, Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66(3):527-540, 2019 [pdf]

Conference Proceedings

  1. Wiacek A, Oluyemi E, Myers K, Mullen L, Bell MAL, Coherence-based beamforming improves the diagnostic certainty of breast ultrasound exams, Proceedings of the 2020 IEEE International Ultrasonics Symposium, Virtual, September 6-11, 2020 [pdf]
  2. Wiacek A, González E, Dehak N, Bell MAL, CohereNet: A deep learning approach to coherence-based beamforming, Proceedings of the 2019 IEEE International Ultrasonics Symposium, Glasgow, Scotland, October 6-9, 2019 [pdf]
  3. Wiacek A, Myers K, Falomo E, Rindal OMH, Fabrega-Foster K, Harvey S, Bell MAL, Clinical feasibility of coherence-based beamforming to distinguish solid from fluid hypoechoic breast masses, Proceedings of the 2018 IEEE International Ultrasonics Symposium, Kobe, Japan, October 22-25, 2018 [pdf]

Peer-Reviewed Paper Accepted to ICRA

Congratulations to Mardava Gubbi! His paper, Deep Learning-Based Photoacoustic Visual Servoing: Using Outputs from Raw Sensor Data as Inputs to a Robot Controller, was accepted for presentation at the IEEE International Conference on Robotics and Automation (ICRA),  Xi’an, China, May 30 – June 5, 2021. This peer-reviewed paper was also accepted for inclusion in the conference proceedings.

ICRA is the IEEE Robotics and Automation Society’s flagship conference and the premier international forum for robotics researchers to present and discuss their work.

Congrats again on this significant achievement, recognition, and milestone, Mardava!

Prof. Bell Co-Authors Cell Commentary, Fund Black Scientists

Prof. Bell co-authored a publication in the scientific journal Cell with fellow colleagues at 15 institutions across the nation to shed light on NIH funding disparities. The publication is entitled Fund Black Scientists. All academics are encouraged to read it, digest the contents, and reflect on what we each want our role to be at this historic moment in time.

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Journal Paper Accepted to IEEE T-BME

Congratulations to Eduardo González! His first-author journal paper entitled Combined ultrasound and photoacoustic image guidance of spinal pedicle cannulation demonstrated with intact ex vivo specimens was accepted to the journal IEEE Transactions on Biomedical Engineering.

This paper presents the first known combined ultrasound and photoacoustic image guidance system with software capabilities that are optimized for pedicle cannulation in posterior spinal fusion surgery. We demonstrate that both amplitude- and coherence-based beamforming methods are mutually beneficial for this task. Specifically, coherence-based beamforming of ultrasound images improved the visualization of bone for ultrasound-to-CT registration, while coherence-based beamforming of photoacoustic images improved target localization, which is important for tracking tool tips during pedicle hole creation. As shown in the figure, amplitude-based photoacoustic beamforming differentiated signals associated with an optical fiber place inside a pedicle hole (which is ideal for screw placement) from signals associated with an optical fiber touching cortical bone (which is characteristic of an impending bone breach that needs to be avoided). This proposed combination of imaging modalities and beamforming methods is promising to assist surgeons with identifying and avoiding accidental bone breaches during spinal fusion surgeries.

These new findings nicely complement our previous findings demonstrating that photoacoustic-based differentiation is possible prior to the creation of any holes, which is advantageous for correctly determining an appropriate starting point for hole creation. Together, these findings represent a complete system that can be used prior to and during pedicle hole creation for spinal fusion surgeries.

Citation: González E, Jain A, Bell MAL, Combined ultrasound and photoacoustic image guidance of spinal pedicle cannulation demonstrated with intact ex vivo specimens, IEEE Transactions on Biomedical Engineering (accepted December 17, 2020) [pdf]

Invited Perspective Published in Journal of Applied Physics

Prof. Bell shares her thoughts on Photoacoustic imaging for surgical guidance: Principles, applications, and outlook in an invited Perspective that was recently published in the Journal of Applied Physics. According to the journal’s website: Perspective articles are written to present an expert viewpoint on topics currently generating a lot of interest in the research community. While perspectives generally provide a brief overview of the topic, their main purpose is to provide a forward looking view on where progress in a particular research area is heading. 

Citation: Bell MAL, Photoacoustic imaging for surgical guidance: Principles, applications, and outlook, Journal of Applied Physics, 128(6):060904, 2020 [pdf]

Journal Paper Published in Biomedical Optics Express

Congratulations to Kelley Kempski! Her first-author journal paper entitled Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality was published in Biomedical Optics Express.

This paper investigates a newly developed, probability-based, generalized contrast-to-noise (gCNR) when applied to photoacoustic images. More traditional metrics experience large variations when a target is fully detectable with additional increases bearing no impact on photoacoustic target detectability. In addition, gCNR is robust to changes in traditional metrics introduced by applying a minimum threshold to image amplitudes. Therefore, gCNR has promising potential to provide additional insight, particularly when designing new beamformers and image formation techniques and when reporting quantitative performance without an opportunity to qualitatively assess corresponding images (e.g., in text-only abstracts). We recommend gCNR as a new standard for assessment of novel photoacoustic beamforming and image formation techniques.

Citation: Kempski KM, Graham MT, Gubbi MR, Palmer T, Bell MAL, Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality, Biomedical Optics Express, 11(7), 3684-3698, 2020 [pdf]

Paper Published in Journal of Biomedical Optics

Congratulations to Eduardo González! His first-author journal paper entitled GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions was accepted to Journal of Biomedical Optics.

This paper introduces the first known real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applies this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers. 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.

Citation: González E, Bell MAL, GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions, Journal of BiomedicalOptics, 25(7):077002, 2020 [pdf]

Journal Paper Accepted to Photoacoustics

Congratulations to Michelle Graham! Her first-author journal paper entitled Simulations and human cadaver head studies to identify optimal acoustic receiver locations for minimally invasive photoacoustic-guided neurosurgery was accepted to the journal Photoacoustics.

This paper presents simulation and experimental studies performed with both an intact human skull (which was cleaned from tissue attachments) and a complete human cadaver head (with contents and surrounding tissue intact) to investigate optimal locations for ultrasound probe placement during photoacoustic-guided surgeries of the skull base. The combined simulation and experimental results newly introduce the eye as a suitable acoustic window for surgical guidance. We also demonstrate a light delivery design that is suitable for patient use. Results are generally promising toward identifying, quantifying, and overcoming major system design barriers for progression to future patient testing.

Citation: Graham MT, Huang J, Creighton FX, Bell MAL, Simulations and human cadaver head studies to identify optimal acoustic receiver locations for minimally invasive photoacoustic-guided neurosurgery, Photoacoustics, 19:100183, 2020 [pdf]

Journal Paper Accepted to IEEE T-UFFC

Congratulations to Michelle Graham! Her first-author journal paper entitled Photoacoustic Spatial Coherence Theory and Applications to Coherence-Based Image Contrast and Resolution was accepted to the journal IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

This paper presents the first known theoretical derivation for photoacoustic spatial coherence functions, demonstrating excellent agreement with experimental results, particularly in the short spatial lag region, which is represented as a percentage of the receive aperture (click on the .gif to see for yourself). The coherence functions theory can be described by relating the van Cittert Zernike theorem to photoacoustics. We achieved the associated incoherent source requirement by modeling a photoacoustic target as a collection of spatially incoherent absorbers. This theory was then used to hypothesize and test previously unexplored principles for optimizing photoacoustic short-lag spatial coherence (SLSC) images, including the influence of the incident light profile on photoacoustic spatial coherence functions and associated SLSC image contrast and resolution.

Citation: Graham MT, Bell MAL, Photoacoustic Spatial Coherence Theory and Applications to Coherence-Based Image Contrast and Resolution, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted May 26, 2020) [pdf]

Journal Paper Accepted to IEEE T-UFFC

Congratulations to Arun Nair! His first-author journal paper entitled Deep learning to obtain simultaneous ultrasound image and segmentation outputs from a single input of raw channel data was accepted to IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. The paper will appear in the journal’s special issue on Deep Learning in Medical Ultrasound – from image formation to image analysis.

This paper explores the use of deep neural network (DNNs) as alternatives to delay-and-sum beamforming. The DNNs learned information directly from raw channel data to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. Although the focus was visualization and segmentation of anechoic targets surrounded by tissue, the concept can be adapted to any specific task of interest. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.

Citation: Nair AA, Washington K, Tran T, Reiter A, Bell MAL, Deep learning to obtain simultaneous ultrasound image and segmentation outputs from a single input of raw channel data, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted May 6, 2020) [pdf]

Journal Paper Accepted to IEEE T-UFFC

Congratulations to Alycen Wiacek! Her first-author journal paper entitled CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming was accepted to IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. The paper will appear in the journal’s special issue on Deep Learning in Medical Ultrasound – from image formation to image analysis.

This paper presents details of a novel deep neural network (DNN) architecture, named CohereNet, that was trained to estimate spatial correlation functions. The DNN-estimated correlation functions were then used to create short-lag spatial coherence ultrasound images at a faster rate than a CPU approach and with more accuracy than a GPU approach. Results were generalizable across multiple phantoms, in vivo datasets, ultrasound transducers, and ultrasound system manufacturers not included during training. CohereNet has additional potential benefits in low-power DNN-based FPGA implementations of coherence-based beamforming for miniaturized ultrasound imaging systems. In addition, CohereNet has potential utility in other areas of ultrasound imaging that require fundamental cross-correlation calculations, including elastography, speckle tracking, sound speed correction, and other advanced beamforming algorithms, such as minimum variance beamforming.

Citation: A. Wiacek, E. González and M. A. L. Bell, “CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, accepted March 20, 2020 [pdf]

Journal Paper Accepted to Ultrasound in Medicine & Biology

Congratulations to Alycen Wiacek! Her first-author journal paper entitled Coherence-based beamforming increases the diagnostic certainty of distinguishing fluid from solid masses in breast ultrasound exams was accepted to Ultrasound in Medicine & Biology. A major highlight of this paper is the inclusion of a task-based user study to determine the ability of coherence-based beamforming, specifically robust short-lag spatial coherence (R-SLSC) imaging, to assess breast mass content and ultimately impact clinical care.

The information from R-SLSC images reduced the uncertainty of fluid mass content from 47.5% to 15.8%, and the number of fluid-filled masses recommended for biopsy was reduced from 43.3% to 13.3%. This work is the first to investigate coherence-based beamforming in breast ultrasound to inform clinical decision making, highlighting the potential of this novel technique to improve diagnostic certainty and to reduce the number of unnecessary biopsies of of fluid-filled breast masses.

Citation: A Wiacek, E Oluyemi, K Myers, L Mulen, MAL Bell, Coherence-based beamforming increases the diagnostic certainty of distinguishing fluid from solid masses in breast ultrasound exams, Ultrasound in Medicine and Biology, accepted January 20, 2020 [pdf]

Journal Paper Accepted to IEEE TMI

A large collaborative effort between PULSE Lab members and collaborators at the School of Medicine culminated with a journal paper that was recently accepted to IEEE Transactions on Medical Imaging.  The paper is entitled, In vivo demonstration of photoacoustic image guidance and robotic visual servoing for cardiac catheter-based interventions.

This work is the first known in vivo demonstration of any type of cardiac photoacoustic application, including an in vivo example that pairs robotic assistance with photoacoustic image guidance to find and constantly visualize cardiac catheter tips. We also show the first known photoacoustic images of cardiac catheter tips within an in vivo heart. These catheter tips were visualized at depths as large as 9 cm from the chest wall with photoacoustic imaging in cases where ultrasound imaging failed (due to the similar echogenicity of catheter tips and nearby cardiac tissue). Results show promise toward reducing the use of fluoroscopy during cardiac catheter-based interventions, which is desirable because fluoroscopy exposes both patients and operators to harmful ionizing radiation.

Citation: Graham M, Assis F, Allman D, Wiacek A, González E, Gubbi M, Dong J, Hou H, Beck S, Chrispin J, Bell MAL, In vivo demonstration of photoacoustic image guidance and robotic visual servoing for cardiac catheter-based interventions, IEEE Transactions on Medical Imaging (accepted) [pdf]

Paper Accepted to Journal of Biomedical Optics

Congratulations to Kelley Kempski! Her first author paper entitled, In vivo photoacoustic imaging of major blood vessels in the pancreas and liver during surgery, was accepted to the Journal of Biomedical Optics. This work is the first to demonstrate in vivo blood vessel visualization with possible applications to a range of photoacoustic-guided pancreatic and liver surgeries.

Special thanks to Alycen Wiacek, who mentored Kelley on this project in her role as Kelley’s graduate student mentor through the Research Experience for Undergraduates (REU) Computational Sensing and Medical Robotics (CSMR) summer program. This program was recently renewed with 3 more years of funding support from the National Science Foundation.

Citation: Kempski K, Wiacek A, Graham M, González E, Goodson B, Allman D, Palmer J, Hou H, Beck S, He J, Bell MAL, In vivo photoacoustic imaging of major blood vessels in the pancreas and liver during surgery, Journal of Biomedical Optics, 24(12):121905, 2019 [pdf]

Related News:

Kelley Kempski Wins Best Presentation Award

SPIE Photonics West 2019 Recap

Kelley Kempski Wins NSF GRFP Fellowship

The Research Experience for Undergraduates in Computational Sensing and Medical Robotics program receives a three-year grant from NSF

Journal Paper Accepted to IEEE T-UFFC

Congratulations to Alycen Wiacek! Her first-author journal paper entitled “Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results” was accepted to the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. This paper will appear in the Special Issue on Pilot Clinical Translation of New Medical Ultrasound Methodologies.

This work is the first to investigate the application of short-lag spatial coherence (SLSC) imaging — and two new variations of this method developed in our lab (i.e., M-Weighted SLSC imaging and Robust SLSC imaging) — to breast ultrasound data. The two newer approaches revisit the lag summation step of SLSC imaging to achieve additional robustness to coherence outliers through weighted summation of individual coherence images (i.e., M-weighting) and through the application of robust principal component analysis (i.e., Robust SLSC, or R-SLSC).

An interesting finding from this initial investigation is that solid breast masses, which appear hypoechoic in traditional B-mode images, have similarly high coherence to that of surrounding tissue in these coherence-based images. This finding represents the first known instance of this phenomenon in hypoechoic ultrasound data from any type of tissue (including simulated, phantom, and in vivo liver data). This work shows great promise for implementing SLSC, M-Weighted SLSC, and/or R-SLSC imaging to distinguish between fluid-filled and solid hypoechoic breast masses, which has implications for improved breast cancer detection and screening, more streamlined diagnostic work ups, and reduced patient anxiety over suspicious breast mass findings.

Citation: A Wiacek, OMH Rindal, E Falomo, K Myers, K Fabrega-Foster, S Harvey, MAL Bell, Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, accepted November 20, 2018 [pdf]

Also Available on Journal Website: https://ieeexplore.ieee.org/document/8548567

Journal Paper Published in Scientific Reports

Our journal paper entitled Photoacoustic-based visual servoing of a needle tip was accepted for publication in Scientific Reports. Check out this video for a demonstration of our robotic photoacoustic assistant!

Citation: Bell MAL, Shubert J, Photoacoustic-based visual servoing of a needle tip, Scientific Reports, 8:15519, 2018 [pdf]

Journal Paper Accepted to Biomedical Optics Express

Our journal paper entitled Additive noise models for photoacoustic spatial coherence theory was accepted for publication in Biomedical Optics Express. This paper introduces two noise models for photoacoustic spatial coherence theory that compare favorably with experimental measurements. It will appear in the journal’s feature issue Topics in Biomedical Optics from the OSA Biophotonics Congress 2018.

Citation: B Stephanian, MT Graham, H Hou, MAL Bell, Additive noise models for photoacoustic spatial coherence theory, Biomedical Optics Express, 9(11):5566-5582, 2018 [pdf]

Journal Paper Accepted to Physics in Medicine and Biology

Our journal paper entitled Photoacoustic imaging of a human vertebra: implications for guiding spinal fusion surgeries was accepted for publication in Physics in Medicine and Biology. This paper will appear in the journal’s special issue Focus on Interventional Photoacoustic Imaging.  The accepted version of the manuscript is currently accessible online (ahead of print).

Citation: J Shubert and MAL Bell, Photoacoustic imaging of a human vertebra: Implications for guiding spinal fusion surgeries, Physics in Medicine and Biology (accepted) [pdf]

Arun Presents A Deep Learning Based Alternative to Beamforming Ultrasound Images at IEEE ICASSP 2018

Congrats to Arun on the successful presentation of his research paper entitled “A Deep Learning Based Alternative to Beamforming Ultrasound Images” at IEEE ICASSP 2018 in Calgary, Alberta, Canada. This work is the first to propose deep learning as an alternative to the traditional ultrasound beamforming process and it was implemented for a single plane wave transmission. Check out  our associated conference paper for more details!

Citation: Nair AA, Tran T, Reiter A, Bell MAL, A deep learning based alternative to beamforming ultrasound images, IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, April 15-20, 2018 [pdf]

Journal Paper Accepted to IEEE TMI

Congratulations to Derek Allman! His paper entitled “Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning” was accepted to the IEEE Transactions on Medical Imaging. This paper is expected to appear in the Special Issue on Machine Learning for Image Reconstruction.

This work is the first to use deep convolutional neural networks (CNNs) as an alternative to the photoacoustic beamforming and image reconstruction process. We used simulations to train CNNs to identify sources and reflection artifacts in raw photoacoustic channel data, reformatted the network outputs to usable images that we call CNN-Based images, and transferred these trained networks to operate on experimental data. Multiple parameters were varied during training (e.g., channel noise, number of sources, number of artifacts, sound speed, signal amplitude, transducer model, lateral and axial locations of sources and artifacts, and spacing between sources and artifacts). The classification accuracy of simulation and experimental data  ranged from 96-100% when the channel signal-to-noise ratio was -9 dB or greater and when sources were located in trained locations. Over 99% of the results had submillimeter location accuracy. Our CNN-Based images have high contrast,  no artifacts, and resolution that rivals the traditional photoacoustic image resolution of low-frequency ultrasound probes.

Citation: D Allman, A Reiter, MAL Bell, Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning, IEEE Transactions on Medical Imaging, 37(6):1464-1477, 2018 [pdf | datasets | code]

JMI Paper Accepted

Congrats to undergraduate student Margaret Allard on the acceptance of her first-author journal paper entitled Feasibility of photoacoustic-guided teleoperated hysterectomies. This paper will appear in the Journal of Medical Imaging (JMI) Special Section on Image-Guided Procedures, Robotic Interventions, and Modeling.

This paper is the first to describe the feasibility of photoacoustic integration with the da Vinci surgical robot to potentially guide minimally invasive hysterectomies and other gynecological surgeries.  To implement photoacoustic imaging, a novel light delivery system was designed and implemented  to surround da Vinci tools. This new light delivery system uniquely enabled the investigations described in the paper, including  the first known analysis of the optimal tool orientations for photoacoustic-guided hysterectomies using a da Vinci scissor tool (which partially blocks the transmitted light in some cases). This work can be extended to other da Vinci tools and laparoscopic instruments with similar tip geometry.

Margaret completed this work through her participation in our NSF Research Experience for Undergraduates in Computational Sensing and Medical Robotics.

Journal Paper Accepted to IEEE T-UFFC

Congrats to Arun Nair on the acceptance of his paper entitled “Robust Short-Lag Spatial Coherence Imaging” to the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. This paper will appear in the special issue on sparsity driven methods in medical ultrasound.

This work is the first to re-examine the lag summation step of the Short-Lag Spatial Coherence (SLSC) algorithm and achieve additional robustness to coherence outliers through both weighted summation of individual coherence images (i.e., M-weighting) and the application of robust principal component analysis (i.e., Robust SLSC, or R-SLSC). Results show great promise for smoothing out the tissue texture of SLSC images, improving boundary delineation, and enhancing anechoic or hypoechoic target visibility at higher lag values. These improvements could be useful in clinical tasks such as breast cyst visualization, liver vessel tracking, and obese patient imaging.

Citation: AA Nair, T Tran, MAL Bell, Robust Short-Lag Spatial Coherence Imaging, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted) [pdf]

Also Available on Journal Website: http://ieeexplore.ieee.org/document/8166807/

JBO Paper Accepted

Our paper entitled Photoacoustic-based approach to surgical guidance performed with and without a da Vinci robot was accepted for publication in the Journal of Biomedical Optics (JBO) Special Section on Translational Biophotonics.

Congrats to undergraduates Neeraj Gandhi and Margaret Allard!

This work was completed in partnership with the NSF REU in Computational Sensing and Medical Robotics along with collaborators Sungmin Kim and Peter Kazanzides, and it is the first to integrate photoacoustic imaging with the da Vinci surgical robot. It was also featured on the journal homepage.

ECE Department Announcement

BioOptics World Article

IEEE TBME Paper Accepted

Prof. Bell’s co-authored paper, “System integration and in-vivo testing of a robot for ultrasound guidance and monitoring during radiotherapy”, has been accepted for publication in IEEE Transactions on Biomedical Engineering.

IEEE TMI Paper Accepted

Dr. Bell’s paper entitled “Spatial Angular Compounding of Photoacoustic Images” was accepted to IEEE Transactions on Medical Imaging. You can view a pre-print of the paper here.