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Paccini M, Paschina G, De Beni S, Stefanov A, Kolev V, Patanè G. US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01176-w. [PMID: 39020154 DOI: 10.1007/s10278-024-01176-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/18/2024] [Accepted: 05/31/2024] [Indexed: 07/19/2024]
Abstract
This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound acquisition. The system eliminates the need for external physical markers and complex training, making image fusion feasible for physicians with different experience levels. The integrated system involves a portable 3D camera for patient-specific surface acquisition, an electromagnetic tracking system, and US components. The fusion algorithm comprises two main parts: skin segmentation and rigid co-registration, both integrated into the US machine. The co-registration aligns the surface extracted from CT/MR images with the 3D surface acquired by the camera, facilitating rapid and effective fusion. Experimental tests in different settings, validate the system's accuracy, computational efficiency, noise robustness, and operator independence.
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Affiliation(s)
| | | | | | | | - Velizar Kolev
- MedCom GmbH, Dolivostr., 11, Darmstadt, 64293, Germany
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2
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Ghauri MD, Šušnjar S, Guadagno CN, Bhattacharya S, Thomasson B, Swartling J, Gautam R, Andersson-Engels S, Konugolu Venkata Sekar S. Hybrid heterogeneous phantoms for biomedical applications: a demonstration to dosimetry validation. BIOMEDICAL OPTICS EXPRESS 2024; 15:863-874. [PMID: 38404353 PMCID: PMC10890852 DOI: 10.1364/boe.514994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/27/2024]
Abstract
Phantoms simultaneously mimicking anatomical and optical properties of real tissues can play a pivotal role for improving dosimetry algorithms. The aim of the paper is to design and develop a hybrid phantom model that builds up on the strengths of solid and liquid phantoms for mimicking various anatomical structures for prostate cancer photodynamic therapy (PDT) dosimetry validation. The model comprises of a photosensitizer-embedded gelatin lesion within a liquid Intralipid prostate shape that is surrounded by a solid silicone outer shell. The hybrid phantom was well characterized for optical properties. The final assembled phantom was also evaluated for fluorescence tomographic reconstruction in conjunction with SpectraCure's IDOSE software. The developed model can lead to advancements in dosimetric evaluations. This would improve PDT outlook as a clinical treatment modality and boost phantom based standardization of biophotonic devices globally.
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Affiliation(s)
- M. Daniyal Ghauri
- Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
- Department of Engineering and Food Sciences, University College Cork, College Road, Cork, T12 K8AF, Ireland
| | - Stefan Šušnjar
- SpectraCure AB, Gasverksgatan 1, SE-222 29 Lund, Sweden
- Department of Physics, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden
| | - Claudia Nunzia Guadagno
- BioPixS Ltd – Biophotonics Standards, IPIC, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
| | - Somdatta Bhattacharya
- Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
| | | | | | - Rekha Gautam
- Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
| | - Stefan Andersson-Engels
- Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
- BioPixS Ltd – Biophotonics Standards, IPIC, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
- Department of Physics, University College Cork, College Road, Cork, T12 K8AF, Ireland
| | - Sanathana Konugolu Venkata Sekar
- Tyndall National Institute, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
- BioPixS Ltd – Biophotonics Standards, IPIC, Lee Maltings Complex, Dyke Parade, T12R5CP, Cork, Ireland
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3
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Shrivastava A, Nag MK. Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach. Surg Innov 2024; 31:58-70. [PMID: 38059371 DOI: 10.1177/15533506231220968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.
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Cortese L, Fernández Esteberena P, Zanoletti M, Lo Presti G, Aranda Velazquez G, Ruiz Janer S, Buttafava M, Renna M, Di Sieno L, Tosi A, Dalla Mora A, Wojtkiewicz S, Dehghani H, de Fraguier S, Nguyen-Dinh A, Rosinski B, Weigel UM, Mesquida J, Squarcia M, Hanzu FA, Contini D, Mora Porta M, Durduran T. In vivocharacterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies. Physiol Meas 2023; 44:125010. [PMID: 38061053 DOI: 10.1088/1361-6579/ad133a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023]
Abstract
Objective.In this paper, we present a detailedin vivocharacterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle (SCM), obtained through ultrasound-guided near-infrared time-domain and diffuse correlation spectroscopies.Approach.A total of sixty-five subjects (forty-nine females, sixteen males) among healthy volunteers and thyroid nodule patients have been recruited for the study. Their SCM hemodynamic (oxy-, deoxy- and total hemoglobin concentrations, blood flow, blood oxygen saturation and metabolic rate of oxygen extraction) and optical properties (wavelength dependent absorption and reduced scattering coefficients) have been measured by the use of a novel hybrid device combining in a single unit time-domain near-infrared spectroscopy, diffuse correlation spectroscopy and simultaneous ultrasound imaging.Main results.We provide detailed tables of the results related to SCM baseline (i.e. muscle at rest) properties, and reveal significant differences on the measured parameters due to variables such as side of the neck, sex, age, body mass index, depth and thickness of the muscle, allowing future clinical studies to take into account such dependencies.Significance.The non-invasive monitoring of the hemodynamics and metabolism of the sternocleidomastoid muscle during respiration became a topic of increased interest partially due to the increased use of mechanical ventilation during the COVID-19 pandemic. Near-infrared diffuse optical spectroscopies were proposed as potential practical monitors of increased recruitment of SCM during respiratory distress. They can provide clinically relevant information on the degree of the patient's respiratory effort that is needed to maintain an optimal minute ventilation, with potential clinical application ranging from evaluating chronic pulmonary diseases to more acute settings, such as acute respiratory failure, or to determine the readiness to wean from invasive mechanical ventilation.
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Affiliation(s)
- Lorenzo Cortese
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, E-08860 Castelldefels (Barcelona), Spain
| | - Pablo Fernández Esteberena
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, E-08860 Castelldefels (Barcelona), Spain
| | - Marta Zanoletti
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, E-08860 Castelldefels (Barcelona), Spain
- Politecnico di Milano, Dipartimento di Fisica, I-20133 Milano, Italy
| | - Giuseppe Lo Presti
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, E-08860 Castelldefels (Barcelona), Spain
| | | | - Sabina Ruiz Janer
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, E-08036 Barcelona, Spain
| | - Mauro Buttafava
- Politecnico di Milano, Dipartimento di Elettronica Informazione e Bioingegneria, I-20133 Milano, Italy
- Now at PIONIRS s.r.l., I-20124 Milano, Italy
| | - Marco Renna
- Politecnico di Milano, Dipartimento di Elettronica Informazione e Bioingegneria, I-20133 Milano, Italy
- Now at Athinoula A. Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, Charlestown, MA 02129, United States of America
| | - Laura Di Sieno
- Politecnico di Milano, Dipartimento di Fisica, I-20133 Milano, Italy
| | - Alberto Tosi
- Politecnico di Milano, Dipartimento di Elettronica Informazione e Bioingegneria, I-20133 Milano, Italy
| | | | - Stanislaw Wojtkiewicz
- University of Birmingham, School of Computer Science, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Now at Nalecz Institute of Biocybernetics and Biomedical Engineering, 02-109 Warsaw, Poland
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | | | | | - Udo M Weigel
- HemoPhotonics S.L., E-08860 Castelldefels (Barcelona), Spain
| | - Jaume Mesquida
- Área de Crítics, Parc Taulí Hospital Universitari, E-08208 Sabadell, Spain
| | - Mattia Squarcia
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, E-08036 Barcelona, Spain
- Neuroradiology Department, Hospital Clínic of Barcelona, E-08036 Barcelona, Spain
| | - Felicia A Hanzu
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, E-08036 Barcelona, Spain
- Endocrinology and Nutrition Department, Hospital Clínic of Barcelona, E-08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), E-28029 Madrid, Spain
| | - Davide Contini
- Politecnico di Milano, Dipartimento di Fisica, I-20133 Milano, Italy
| | - Mireia Mora Porta
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, E-08036 Barcelona, Spain
- Endocrinology and Nutrition Department, Hospital Clínic of Barcelona, E-08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), E-28029 Madrid, Spain
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, E-08860 Castelldefels (Barcelona), Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), E-08010 Barcelona, Spain
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Markow ZE, Trobaugh JW, Richter EJ, Tripathy K, Rafferty SM, Svoboda AM, Schroeder ML, Burns-Yocum TM, Bergonzi KM, Chevillet MA, Mugler EM, Eggebrecht AT, Culver JP. Ultra-high density imaging arrays for diffuse optical tomography of human brain improve resolution, signal-to-noise, and information decoding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.549920. [PMID: 37547013 PMCID: PMC10401969 DOI: 10.1101/2023.07.21.549920] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (∼13 mm) than sparse fNIRS (∼30 mm) and therefore provide higher image quality, with spatial resolution ∼1/2 that of fMRI. Herein, simulations indicated reducing inter-optode spacing to 6.5 mm would further improve image quality and noise-resolution tradeoff, with diminishing returns below 6.5 mm. We then constructed an ultra-high-density DOT system (6.5-mm spacing) with 140 dB dynamic range that imaged stimulus-evoked activations with 30-50% higher spatial resolution and repeatable multi-focal activity with excellent agreement with participant-matched fMRI. Further, this system decoded visual stimulus position with 19-35% lower error than previous HD-DOT, throughout occipital cortex.
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6
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Zhai X, Santosa H, Krafty RT, Huppert TJ. Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model. NEUROPHOTONICS 2023; 10:023516. [PMID: 36788804 PMCID: PMC9912979 DOI: 10.1117/1.nph.10.2.023516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/19/2022] [Indexed: 06/18/2023]
Abstract
Significance Functional near-infrared spectroscopy (fNIRS) is a noninvasive technology that uses low levels of nonionizing light in the range of red and near-infrared to record changes in the optical absorption and scattering of the underlying tissue that can be used to infer blood flow and oxygen changes during brain activity. The challenges and difficulties of reconstructing spatial images of hemoglobin changes from fNIRS data are mainly caused by the illposed nature of the optical inverse model. Aim We describe a Bayesian approach combining several lasso-based regularizations to apply anatomy-prior information to solving the inverse model. Approach We built a Bayesian hierarchical model to solve the Bayesian adaptive fused sparse overlapping group lasso (Ba-FSOGL) model. The method is evaluated and validated using simulation and experimental datasets. Results We apply this approach to the simulation and experimental datasets to reconstruct a known brain activity. The reconstructed images and statistical plots are shown. Conclusion We discuss the adaptation of this method to fNIRS data and demonstrate that this approach provides accurate image reconstruction with a low false-positive rate, through numerical simulations and application to experimental data collected during motor and sensory tasks.
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Affiliation(s)
- Xuetong Zhai
- University of Pittsburgh, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
| | - Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Robert T. Krafty
- Emory University, Department of Biostatistics and Bioinformatics, Atlanta, Georgia, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Department of Electrical and Computer Engineering, Department of Bioengineering, Pittsburgh, Pennsylvania, United States
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7
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Zhang W, Hu T, Li Z, Sun Z, Jia K, Dou H, Feng J, Pogue BW. Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:783-798. [PMID: 36874507 PMCID: PMC9979688 DOI: 10.1364/boe.480429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 06/18/2023]
Abstract
As an emerging imaging technique, Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of quantum emission fields within tissue using X-ray excitation for deep penetrance. However, its reconstruction is an ill-posed and under-conditioned inverse problem because of the diffuse optical emission signal. Deep learning based image reconstruction has shown very good potential for solving these types of problems, however they suffer from a lack of ground-truth image data to confirm when used with experimental data. To overcome this, a self-supervised network cascaded by a 3D reconstruction network and the forward model, termed Selfrec-Net, was proposed to perform CELST reconstruction. Under this framework, the boundary measurements are input to the network to reconstruct the distribution of the quantum field and the predicted measurements are subsequently obtained by feeding the reconstructed result to the forward model. The network was trained by minimizing the loss between the input measurements and the predicted measurements rather than the reconstructed distributions and the corresponding ground truths. Comparative experiments were carried out on both numerical simulations and physical phantoms. For singular luminescent targets, the results demonstrate the effectiveness and robustness of the proposed network, and comparable performance can be attained to a state-of-the-art deep supervised learning algorithm, where the accuracy of the emission yield and localization of the objects was far superior to iterative reconstruction methods. Reconstruction of multiple objects is still reasonable with high localization accuracy, although with limits to the emission yield accuracy as the distribution becomes more complex. Overall though the reconstruction of Selfrec-Net provides a self-supervised way to recover the location and emission yield of molecular distributions in murine model tissues.
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Affiliation(s)
- Wenqian Zhang
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Ting Hu
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Zhe Li
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Zhonghua Sun
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Kebin Jia
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Huijing Dou
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jinchao Feng
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Brian W. Pogue
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
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8
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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9
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Pacheco A, Jayet B, Svanberg EK, Dehghani H, Dempsey E, Andersson-Engels S. Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants. JOURNAL OF BIOPHOTONICS 2022; 15:e202200041. [PMID: 35340113 DOI: 10.1002/jbio.202200041] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
There is an urgent need for improved respiratory surveillance of preterm infants. Gas in scattering media absorption spectroscopy (GASMAS) is emerging as a potential clinical cutaneous monitoring tool of lung functions in neonates. A challenge in the clinical translation of GASMAS is to obtain sufficiently high signal-to-noise ratios in the measurements, since the light attenuation is high in human tissue. Previous GASMAS studies on piglets have shown higher signal quality with an internal source, as more light propagates through the lung and the loss due to scattering and absorption is less. In this article we simulated light propagation with an intratracheal and a dermal source, and investigated the signal quality and lung volume probed. The results suggest that GASMAS has the potential to measure respiratory volumes; and the sensitivity is higher for an intratracheal source which also enables to probe most of the lung.
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Affiliation(s)
- Andrea Pacheco
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Cork, Ireland
- Department of Physics, University College Cork, Cork, Ireland
| | - Baptiste Jayet
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Cork, Ireland
| | - Emilie Krite Svanberg
- Department of Clinical Sciences, Paediatric Anaesthesiology and Intensive Care Medicine, Skåne University Hospital, Lund University, Lund, Sweden
| | - Hamid Dehghani
- School of Computer Science, the University of Birmingham, Birmingham
| | - Eugene Dempsey
- INFANT Centre, Cork University Maternity Hospital, University College Cork, Ireland
| | - Stefan Andersson-Engels
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Cork, Ireland
- Department of Physics, University College Cork, Cork, Ireland
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10
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Lo Giudice A, Ronsivalle V, Gastaldi G, Leonardi R. Assessment of the accuracy of imaging software for 3D rendering of the upper airway, usable in orthodontic and craniofacial clinical settings. Prog Orthod 2022; 23:22. [PMID: 35691961 PMCID: PMC9189077 DOI: 10.1186/s40510-022-00413-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several semi-automatic software are available for the three-dimensional reconstruction of the airway from DICOM files. The aim of this study was to evaluate the accuracy of the segmentation of the upper airway testing four free source and one commercially available semi-automatic software. A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the upper airway. The software tested were Invesalius, ITK-Snap, Dolphin 3D, 3D Slicer and Seg3D. The same upper airway models were manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the upper airway model obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the upper airway models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed for software comparisons. RESULTS Statistically significant differences were found in the volumetric dimensions of the upper airway models and in the matching percentage among the tested software (p < 0.001). Invesalius was the most accurate software for 3D rendering of the upper airway (mean bias = 1.54 cm3; matching = 90.05%) followed by ITK-Snap (mean bias = - 2.52 cm3; matching = 84.44%), Seg 3D (mean bias = 3.21 cm3, matching = 87.36%), 3D Slicer (mean bias = - 4.77 cm3; matching = 82.08%) and Dolphin 3D (difference mean = - 6.06 cm3; matching = 78.26%). According to the color-coded map, the dis-matched area was mainly located at the most anterior nasal region of the airway. Volumetric data showed excellent inter-software reliability (GS vs semi-automatic software), with coefficient values ranging from 0.904 to 0.993, confirming proportional equivalence with manual segmentation. CONCLUSION Despite the excellent inter-software reliability, different semi-automatic segmentation algorithms could generate different patterns of inaccuracy error (underestimation/overestimation) of the upper airway models. Thus, is unreasonable to expect volumetric agreement among different software packages for the 3D rendering of the upper airway anatomy.
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Affiliation(s)
- Antonino Lo Giudice
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
| | - Vincenzo Ronsivalle
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
| | - Giorgio Gastaldi
- Department Orthodontics, Vita-Salute San Raffaele University, Milan, Italy
| | - Rosalia Leonardi
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
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11
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Projected cutting guides using an augmented reality system to improve surgical margins in maxillectomies: A preclinical study. Oral Oncol 2022; 127:105775. [DOI: 10.1016/j.oraloncology.2022.105775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/03/2022] [Accepted: 02/13/2022] [Indexed: 11/21/2022]
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12
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Yin W, Li X, Cao Q, Wang H, Zhang B. Bioluminescence tomography reconstruction in conjunction with an organ probability map as an anatomical reference. BIOMEDICAL OPTICS EXPRESS 2022; 13:1275-1291. [PMID: 35414991 PMCID: PMC8973175 DOI: 10.1364/boe.448862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/15/2022] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
To alleviate the ill-posedness of bioluminescence tomography (BLT) reconstruction, anatomical information from computed tomography (CT) or magnetic resonance imaging (MRI) is usually adopted to improve the reconstruction quality. With the anatomical information, different organs could be segmented and assigned with appropriate optical parameters, and the reconstruction could be confined into certain organs. However, image segmentation is a time-consuming and challenging work, especially for the low-contrast organs. In this paper, we present a BLT reconstruction method in conjunction with an organ probability map to effectively incorporate the anatomical information. Instead of using a segmentation with a fixed organ map, an organ probability map is established by registering the CT image of the mouse to the statistical mouse atlas with the constraints of the mouse surface and high-contrast organs (bone and lung). Then the organ probability map of the low-contrast organs, such as the liver and kidney, is determined automatically. After discretization of the mouse torso, a heterogeneous model is established as the input for reconstruction, in which the optical parameter of each node is calculated according to the organ probability map. To take the advantage of the sparse Bayesian Learning (SBL) method in recovering block sparse signals in inverse problems, which is common in BLT applications where the target distribution has the characteristic of sparsity and block structure, a two-step method in conjunction with the organ probability map is presented. In the first step, a fast sparse algorithm, L1-LS, is used to reveal the source distribution on the organ level. In the second step, the bioluminescent source is reconstructed on the pixel level based on the SBL method. Both simulation and in vivo experiments are conducted, and the results demonstrate that the organ probability map in conjunction with the proposed two-step BLT reconstruction method is feasible to accurately reconstruct the localization of the bioluminescent light source.
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Affiliation(s)
- Wanzhou Yin
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
- Contributed equally
| | - Xiang Li
- Department of Radiology, the Second Hospital of Dalian Medial University, Dalian, Liaoning 116023, China
- Contributed equally
| | - Qian Cao
- Department of Radiology, the Second Hospital of Dalian Medial University, Dalian, Liaoning 116023, China
- Contributed equally
| | - Hongkai Wang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Bin Zhang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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13
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Vanegas M, Mireles M, Fang Q. MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes. NEUROPHOTONICS 2022; 9:017801. [PMID: 36278785 PMCID: PMC8823693 DOI: 10.1117/1.nph.9.1.017801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 01/11/2022] [Indexed: 05/20/2023]
Abstract
SIGNIFICANCE The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe's performance a daunting task. AIM We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes. APPROACH Our software-modular optode configuration analyzer (MOCA)-implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities. RESULTS Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts. CONCLUSION Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.
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Affiliation(s)
- Morris Vanegas
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Address all correspondence to Qianqian Fang,
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14
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Deng Z, Xu X, Dehghani H, Sforza DM, Iordachita I, Lim M, Wong JW, Wang KKH. Quantitative Bioluminescence Tomography for In Vivo Volumetric-Guided Radiotherapy. Methods Mol Biol 2022; 2393:701-731. [PMID: 34837208 PMCID: PMC9098109 DOI: 10.1007/978-1-0716-1803-5_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Several groups, including ours, have initiated efforts to develop small-animal irradiators that mimic radiation therapy (RT) for human treatment. The major image modality used to guide irradiation is cone-beam computed tomography (CBCT). While CBCT provides excellent guidance capability, it is less adept at localizing soft tissue targets growing in a low image contrast environment. In contrast, bioluminescence imaging (BLI) provides strong image contrast and thus is an attractive solution for soft tissue targeting. However, commonly used 2D BLI on an animal surface is inadequate to guide irradiation, because optical transport from an internal bioluminescent tumor is highly susceptible to the effects of optical path length and tissue absorption and scattering. Recognition of these limitations led us to integrate 3D bioluminescence tomography (BLT) with the small animal radiation research platform (SARRP). In this chapter, we introduce quantitative BLT (QBLT) with the advanced capabilities of quantifying tumor volume for irradiation guidance. The detail of system components, calibration protocol, and step-by-step procedure to conduct the QBLT-guided irradiation are described.
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Affiliation(s)
- Zijian Deng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiangkun Xu
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Hamid Dehghani
- School of Computer Science, University of Birmingham, Birmingham, UK
| | - Daniel M Sforza
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Iulian Iordachita
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - John W Wong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Ken Kang-Hsin Wang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA.
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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15
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Taboni S, Ferrari M, Daly MJ, Chan HHL, Eu D, Gualtieri T, Jethwa AR, Sahovaler A, Sewell A, Hasan W, Berania I, Qiu J, de Almeida J, Nicolai P, Gilbert RW, Irish JC. Navigation-Guided Transnasal Endoscopic Delineation of the Posterior Margin for Maxillary Sinus Cancers: A Preclinical Study. Front Oncol 2021; 11:747227. [PMID: 34858824 PMCID: PMC8632239 DOI: 10.3389/fonc.2021.747227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 10/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background The resection of advanced maxillary sinus cancers can be challenging due to the anatomical proximity to surrounding critical anatomical structures. Transnasal endoscopy can effectively aid the delineation of the posterior margin of resection. Implementation with 3D-rendered surgical navigation with virtual endoscopy (3D-SNVE) may represent a step forward. This study aimed to demonstrate and quantify the benefits of this technology. Material and Method Four maxillary tumor models with critical posterior extension were created in four artificial skulls (Sawbones®). Images were acquired with cone-beam computed tomography and the tumor and carotid were contoured. Eight head and neck surgeons were recruited for the simulations. Surgeons delineated the posterior margin of resection through a transnasal approach and avoided the carotid while establishing an adequate resection margin with respect to tumor extirpation. Three simulations were performed: 1) unguided: based on a pre-simulation study of cross-sectional imaging; 2) tumor-guided: guided by real-time tool tracking with 3D tumor and carotid rendering; 3) carotid-guided: tumor-guided with a 2-mm alert cloud surrounding the carotid. Distances of the planes from the carotid and tumor were classified as follows and the points of the plane were classified accordingly: “red”: through the carotid artery; “orange”: <2 mm from the carotid; “yellow”: >2 mm from the carotid and within the tumor or <5 mm from the tumor; “green”: >2 mm from the carotid and 5–10 mm from the tumor; and “blue”: >2 mm from the carotid and >10 mm from the tumor. The three techniques (unguided, tumor-guided, and carotid-guided) were compared. Results 3D-SNVE for the transnasal delineation of the posterior margin in maxillary tumor models significantly improved the rate of margin-negative clearance around the tumor and reduced damage to the carotid artery. “Green” cuts occurred in 52.4% in the unguided setting versus 62.1% and 64.9% in the tumor- and carotid-guided settings, respectively (p < 0.0001). “Red” cuts occurred 6.7% of the time in the unguided setting versus 0.9% and 1.0% in the tumor- and carotid-guided settings, respectively (p < 0.0001). Conclusions This preclinical study has demonstrated that 3D-SNVE provides a substantial improvement of the posterior margin delineation in terms of safety and oncological adequacy. Translation into the clinical setting, with a meticulous assessment of the oncological outcomes, will be the proposed next step.
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Affiliation(s)
- Stefano Taboni
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Section of Otorhinolaryngology-Head and Neck Surgery, Department of Neurosciences, University of Padua-"Azienda Ospedaliera di Padova", Padua, Italy.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,University Health Network (UHN) Guided Therapeutics (GTx) Program International Scholar, Toronto, ON, Canada.,Artificial Intelligence in Medicine and Innovation in Clinical Research and Methodology (PhD Program), Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Ferrari
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Section of Otorhinolaryngology-Head and Neck Surgery, Department of Neurosciences, University of Padua-"Azienda Ospedaliera di Padova", Padua, Italy.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,University Health Network (UHN) Guided Therapeutics (GTx) Program International Scholar, Toronto, ON, Canada.,Technology for Health (PhD Program), Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Michael J Daly
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Harley H L Chan
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Donovan Eu
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Tommaso Gualtieri
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia-"ASST Spedali Civili di Brescia", Brescia, Italy
| | - Ashok R Jethwa
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Axel Sahovaler
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,Head & Neck Surgery, University College London Hospital, London, United Kingdom
| | - Andrew Sewell
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Wael Hasan
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Ilyes Berania
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Jimmy Qiu
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - John de Almeida
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Piero Nicolai
- Section of Otorhinolaryngology-Head and Neck Surgery, Department of Neurosciences, University of Padua-"Azienda Ospedaliera di Padova", Padua, Italy
| | - Ralph W Gilbert
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
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16
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Sahovaler A, Chan HHL, Gualtieri T, Daly M, Ferrari M, Vannelli C, Eu D, Manojlovic-Kolarski M, Orzell S, Taboni S, de Almeida JR, Goldstein DP, Deganello A, Nicolai P, Gilbert RW, Irish JC. Augmented Reality and Intraoperative Navigation in Sinonasal Malignancies: A Preclinical Study. Front Oncol 2021; 11:723509. [PMID: 34790568 PMCID: PMC8591179 DOI: 10.3389/fonc.2021.723509] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/12/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To report the first use of a novel projected augmented reality (AR) system in open sinonasal tumor resections in preclinical models and to compare the AR approach with an advanced intraoperative navigation (IN) system. Methods Four tumor models were created. Five head and neck surgeons participated in the study performing virtual osteotomies. Unguided, AR, IN, and AR + IN simulations were performed. Statistical comparisons between approaches were obtained. Intratumoral cut rate was the main outcome. The groups were also compared in terms of percentage of intratumoral, close, adequate, and excessive distances from the tumor. Information on a wearable gaze tracker headset and NASA Task Load Index questionnaire results were analyzed as well. Results A total of 335 cuts were simulated. Intratumoral cuts were observed in 20.7%, 9.4%, 1.2,% and 0% of the unguided, AR, IN, and AR + IN simulations, respectively (p < 0.0001). The AR was superior than the unguided approach in univariate and multivariate models. The percentage of time looking at the screen during the procedures was 55.5% for the unguided approaches and 0%, 78.5%, and 61.8% in AR, IN, and AR + IN, respectively (p < 0.001). The combined approach significantly reduced the screen time compared with the IN procedure alone. Conclusion We reported the use of a novel AR system for oncological resections in open sinonasal approaches, with improved margin delineation compared with unguided techniques. AR improved the gaze-toggling drawback of IN. Further refinements of the AR system are needed before translating our experience to clinical practice.
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Affiliation(s)
- Axel Sahovaler
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Harley H L Chan
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Tommaso Gualtieri
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,Unit of Otorhinolaryngology-Head and Neck Surgery, University of Brescia-ASST "Spedali Civili di Brescia, Brescia, Italy
| | - Michael Daly
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Marco Ferrari
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,Unit of Otorhinolaryngology-Head and Neck Surgery, University of Brescia-ASST "Spedali Civili di Brescia, Brescia, Italy.,Section of Otorhinolaryngology-Head and Neck Surgery, University of Padua-Azienda Ospedaliera di Padova, Padua, Italy
| | - Claire Vannelli
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Donovan Eu
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Mirko Manojlovic-Kolarski
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada
| | - Susannah Orzell
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada
| | - Stefano Taboni
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada.,Unit of Otorhinolaryngology-Head and Neck Surgery, University of Brescia-ASST "Spedali Civili di Brescia, Brescia, Italy.,Section of Otorhinolaryngology-Head and Neck Surgery, University of Padua-Azienda Ospedaliera di Padova, Padua, Italy
| | - John R de Almeida
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada
| | - David P Goldstein
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada
| | - Alberto Deganello
- Unit of Otorhinolaryngology-Head and Neck Surgery, University of Brescia-ASST "Spedali Civili di Brescia, Brescia, Italy
| | - Piero Nicolai
- Section of Otorhinolaryngology-Head and Neck Surgery, University of Padua-Azienda Ospedaliera di Padova, Padua, Italy
| | - Ralph W Gilbert
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, ON, Canada.,Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, ON, Canada
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17
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Zhang B, Rahmatullah B, Wang SL, Zhang G, Wang H, Ebrahim NA. A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis. J Appl Clin Med Phys 2021; 22:45-65. [PMID: 34453471 PMCID: PMC8504607 DOI: 10.1002/acm2.13394] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/29/2021] [Accepted: 07/31/2021] [Indexed: 02/01/2023] Open
Abstract
PURPOSE Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. METHODS This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. RESULTS The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. CONCLUSIONS Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.
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Affiliation(s)
- Bin Zhang
- Data Intelligence and Knowledge Management, Faculty of Arts, Computing and Creative IndustrySultan Idris Education University (UPSI)Tanjong MalimPerakMalaysia
- School of Computer ScienceBaoji University of Arts and SciencesBaojiP. R. China
| | - Bahbibi Rahmatullah
- Data Intelligence and Knowledge Management, Faculty of Arts, Computing and Creative IndustrySultan Idris Education University (UPSI)Tanjong MalimPerakMalaysia
| | - Shir Li Wang
- Data Intelligence and Knowledge Management, Faculty of Arts, Computing and Creative IndustrySultan Idris Education University (UPSI)Tanjong MalimPerakMalaysia
| | - Guangnan Zhang
- School of Computer ScienceBaoji University of Arts and SciencesBaojiP. R. China
| | - Huan Wang
- School of Computer ScienceBaoji University of Arts and SciencesBaojiP. R. China
| | - Nader Ale Ebrahim
- Research and Technology DepartmentAlzahra UniversityVanakTehranIran
- Office of the Deputy Vice‐Chancellor (Research & Innovation)University of MalayaKuala LumpurMalaysia
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18
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Ioussoufovitch S, Cohen DJF, Milej D, Diop M. Compressed sensing time-resolved spectrometer for quantification of light absorbers in turbid media. BIOMEDICAL OPTICS EXPRESS 2021; 12:6442-6460. [PMID: 34745748 PMCID: PMC8547999 DOI: 10.1364/boe.433427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/20/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Time-resolved (TR) spectroscopy is well-suited to address the challenges of quantifying light absorbers in highly scattering media such as living tissue; however, current TR spectrometers are either based on expensive array detectors or rely on wavelength scanning. Here, we introduce a TR spectrometer architecture based on compressed sensing (CS) and time-correlated single-photon counting. Using both CS and basis scanning, we demonstrate that-in homogeneous and two-layer tissue-mimicking phantoms made of Intralipid and Indocyanine Green-the CS method agrees with or outperforms uncompressed approaches. Further, we illustrate the superior depth sensitivity of TR spectroscopy and highlight the potential of the device to quantify absorption changes in deeper (>1 cm) tissue layers.
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Affiliation(s)
- Seva Ioussoufovitch
- Western University, Faculty of Engineering, School of Biomedical Engineering, Collaborative Training Program in Musculoskeletal Health Research, Bone & Joint Institute, 1151 Richmond St., London, N6A 5C1, Canada
| | - David Jonathan Fulop Cohen
- Western University, Schulich School of Medicine & Dentistry, Department of Medical Biophysics, 1151 Richmond St., London, N6A 5C1, Canada
| | - Daniel Milej
- Western University, Schulich School of Medicine & Dentistry, Department of Medical Biophysics, 1151 Richmond St., London, N6A 5C1, Canada
- Lawson Health Research Institute, Imaging Program, 268 Grosvenor St., London, N6A 4V2, Canada
| | - Mamadou Diop
- Western University, Faculty of Engineering, School of Biomedical Engineering, Collaborative Training Program in Musculoskeletal Health Research, Bone & Joint Institute, 1151 Richmond St., London, N6A 5C1, Canada
- Western University, Schulich School of Medicine & Dentistry, Department of Medical Biophysics, 1151 Richmond St., London, N6A 5C1, Canada
- Lawson Health Research Institute, Imaging Program, 268 Grosvenor St., London, N6A 4V2, Canada
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19
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Wojtkiewicz S, Liebert A. Parallel, multi-purpose Monte Carlo code for simulation of light propagation in segmented tissues. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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20
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Clinically translatable quantitative molecular photoacoustic imaging with liposome-encapsulated ICG J-aggregates. Nat Commun 2021; 12:5410. [PMID: 34518530 PMCID: PMC8438038 DOI: 10.1038/s41467-021-25452-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 08/11/2021] [Indexed: 02/08/2023] Open
Abstract
Photoacoustic (PA) imaging is a functional and molecular imaging technique capable of high sensitivity and spatiotemporal resolution at depth. Widespread use of PA imaging, however, is limited by currently available contrast agents, which either lack PA-signal-generation ability for deep imaging or their absorbance spectra overlap with hemoglobin, reducing sensitivity. Here we report on a PA contrast agent based on targeted liposomes loaded with J-aggregated indocyanine green (ICG) dye (i.e., PAtrace) that we synthesized, bioconjugated, and characterized to addresses these limitations. We then validated PAtrace in phantom, in vitro, and in vivo PA imaging environments for both spectral unmixing accuracy and targeting efficacy in a folate receptor alpha-positive ovarian cancer model. These study results show that PAtrace concurrently provides significantly improved contrast-agent quantification/sensitivity and SO2 estimation accuracy compared to monomeric ICG. PAtrace's performance attributes and composition of FDA-approved components make it a promising agent for future clinical molecular PA imaging.
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21
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Feng T, Zhu Y, Morris R, kozloff KM, Wang X. The feasibility study of the transmission mode photoacoustic measurement of human calcaneus bone in vivo. PHOTOACOUSTICS 2021; 23:100273. [PMID: 34745881 PMCID: PMC8552339 DOI: 10.1016/j.pacs.2021.100273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 05/04/2021] [Accepted: 05/12/2021] [Indexed: 05/26/2023]
Abstract
The photoacoustic (PA) technique is uniquely positioned for biomedical applications primarily due to its ability to visualize optical absorption contrast in deep tissue at ultrasound resolution. In this work, via both three-dimensional (3D) numerical simulations and in vivo experiments on human subjects, we investigated the possibility of PA measurement of human calcaneus bones in vivo in a non-invasive manner, as well as its feasibility to differentiate osteoporosis patients from normal subjects. The results from the simulations and the experiments both demonstrated that, when one side of the heel is illuminated by laser with light fluence under the ANSI safety limit, the PA signal generated in the human calcaneus bone can be detected by an ultrasonic transducer at the other side of the heel (i.e. transmission mode). Quantitative power spectral analyses of the calcaneus bone PA signals were also conducted, demonstrating that the microarchitectural changes in calcaneus bone due to osteoporosis can be detected, as reflected by enhanced high frequency components in detected PA bone signal. Further statistical analysis of the experimental results from 10 osteoporosis patients and 10 healthy volunteers showed that the weighted frequency as a quantified PA spectral parameter can differentiate the two subject groups with statistical significance.
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Affiliation(s)
- Ting Feng
- Department of Biomedical Engineering, University of Michigan Medical School, MI 48109, USA
| | - Yunhao Zhu
- Department of Biomedical Engineering, University of Michigan Medical School, MI 48109, USA
| | | | - Kenneth M. kozloff
- Department of Biomedical Engineering, University of Michigan Medical School, MI 48109, USA
- Department of Orthopaedic Surgery, University of Michigan Medical School, MI 48109, USA
| | - Xueding Wang
- Department of Biomedical Engineering, University of Michigan Medical School, MI 48109, USA
- Department of Radiology, University of Michigan Medical School, MI 48109, USA
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22
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Yazdani A, Agrawal S, Johnstonbaugh K, Kothapalli SR, Monga V. Simultaneous Denoising and Localization Network for Photoacoustic Target Localization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2367-2379. [PMID: 33939612 PMCID: PMC8526152 DOI: 10.1109/tmi.2021.3077187] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images.To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required. However, this is not guaranteed for deep targets, as optical scattering causes an exponential decay in optical fluence with respect to tissue depth. To address this, we develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data. More precisely, we describe and evaluate a deep neural network architecture consisting of a shared encoder and two parallel decoders. One decoder extracts the target coordinates from the input RF data while the other boosts the SNR and estimates clean RF data. The joint optimization of the shared encoder and dual decoders lends significant noise robustness to the features extracted by the encoder, which in turn enables the network to contain detailed information about deep targets that may be obscured by noise. Additional custom layers and newly proposed regularizers in the training loss function (designed based on observed RF data signal and noise behavior) serve to increase the SNR in the cleaned RF output and improve model performance. To account for depth-dependent strong optical scattering, our network was trained with simulated photoacoustic datasets of targets embedded at different depths inside tissue media of different scattering levels. The network trained on this novel dataset accurately locates targets in experimental PA data that is clinically relevant with respect to the localization of vessels, needles, or brachytherapy seeds. We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.
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23
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Wijnen N, Brouwers L, Jebbink EG, Heyligers JMM, Bemelman M. Comparison of segmentation software packages for in-hospital 3D print workflow. J Med Imaging (Bellingham) 2021; 8:034004. [PMID: 34222558 DOI: 10.1117/1.jmi.8.3.034004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 06/04/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose: In-hospital three-dimensional (3D) printing of patient-specific pathologies is increasingly being used in daily care. However, the efficiency of the current conversion from image to print is often obstructed due to limitations associated with segmentation software. Therefore, there is a need for comparison of several clinically available tools. A comparative study has been conducted to compare segmentation performance of Philips IntelliSpace Portal® (PISP), Mimics Innovation Suite (MIS), and DICOM to PRINT® (D2P). Approach: These tools were compared with respect to segmentation time and 3D mesh quality. The dataset consisted of three computed tomography (CT)-scans of acetabular fractures (ACs), three CT-scans of tibia plateau fractures (TPs), and three CTA-scans of abdominal aortic aneurysms (AAAs). Independent-samples t -tests were performed to compare the measured segmentation times. Furthermore, 3D mesh quality was assessed and compared according to representativeness and usability for the surgeon. Results: Statistically significant differences in segmentation time were found between PISP and MIS with respect to the segmentation of ACs ( p = < 0.001 ) and AAAs ( p = 0.031 ). Furthermore, statistically significant differences in segmentation time were found between PISP and D2P for segmentations of AAAs ( p = 0.008 ). There were no statistically significant differences in segmentation time for TPs. The accumulated mesh quality scores were highest for segmentations performed in MIS, followed by D2P. Conclusion: Based on segmentation time and mesh quality, MIS and D2P are capable of enhancing the in-hospital 3D print workflow. However, they should be integrated with the picture archiving and communication system to truly improve the workflow. In addition, these software packages are not open source and additional costs must be incurred.
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Affiliation(s)
- Niek Wijnen
- University of Twente, Technical Medicine, Enschede, The Netherlands
| | - Lars Brouwers
- Elisabeth-Tweesteden Hospital, Department of Surgery, Tilburg, Noord-Brabant, The Netherlands
| | - Erik Groot Jebbink
- University of Twente, Technical Medical Centre, Multi-Modality Medical Imaging Group, Enschede, The Netherlands
| | - Jan M M Heyligers
- Elisabeth-Tweesteden Hospital, Department of Surgery, Tilburg, Noord-Brabant, The Netherlands
| | - Mike Bemelman
- Elisabeth-Tweesteden Hospital, Department of Surgery, Tilburg, Noord-Brabant, The Netherlands
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24
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Cai L, Okada E, Minagawa Y, Kawaguchi H. Correlating functional near-infrared spectroscopy with underlying cortical regions of 0-, 1-, and 2-year-olds using theoretical light propagation analysis. NEUROPHOTONICS 2021; 8:025009. [PMID: 34079846 PMCID: PMC8166262 DOI: 10.1117/1.nph.8.2.025009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/18/2021] [Indexed: 05/03/2023]
Abstract
Significance: The establishment of a light propagation analysis-based scalp-cortex correlation (SCC) between the scalp location of the source-detector (SD) pair and brain regions is essential for measuring functional brain development in the first 2 years of life using functional near-infrared spectroscopy (fNIRS). Aim: We aimed to reveal the optics-based SCC of 0-, 1-, and 2-year-olds (yo) and the suitable SD distance for this age period. Approach: Light propagation analyses using age-appropriate head models were conducted on SD pairs at 10-10 fiducial points on the scalp to obtain optics-based SCC and its metrics: the number of corresponding brain regions ( N C B R ), selectivity and sensitivity of the most likely corresponding brain region (MLCBR), and consistency of the MLCBR across developmental ages. Moreover, we assessed the suitable SD distances for 0-, 1-, and 2-yo by simultaneously considering the selectivity and sensitivity of the MLCBR. Results: Age-related changes in the SCC metrics were observed. For instance, the N C B R of 0-yo was larger than that of 1- and 2-yo. Conversely, the selectivity of 0-yo was lower than that of 1- and 2-yo. The sensitivity of 1-yo was higher than that of 0-yo at 15- to 30-mm SD distances and higher than that of 2-yo at 10-mm SD distance. Notably, the MLCBR of the fiducial points around the longitudinal fissure was inconsistent across age groups. An SD distance between 15 and 25 mm was found to be appropriate for satisfying both sensitivity and selectivity requirements. In addition, this work provides reference tables of optics-based SCC for 0-, 1-, and 2-yo. Conclusions: Optics-based SCC will be informative in designing and explaining child developmental studies using fNIRS. The suitable SD distances were between 15 and 25 mm for the first 2 years of life.
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Affiliation(s)
- Lin Cai
- Keio University, Department of Electronics and Electrical Engineering, Yokohama, Japan
| | - Eiji Okada
- Keio University, Department of Electronics and Electrical Engineering, Yokohama, Japan
| | | | - Hiroshi Kawaguchi
- Keio University, Department of Electronics and Electrical Engineering, Yokohama, Japan
- National Institute of Advanced Industrial Science and Technology, Human Informatics and Interaction Research Institute, Tsukuba, Japan
- Address all correspondence to Hiroshi Kawaguchi,
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25
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Meng B, Strawbridge RR, Tichauer K, Samkoe KS, Davis SC. Monitoring cancer cell surface receptor expression during anti-angiogenesis therapy in vivo. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11625:116250Q. [PMID: 34446980 PMCID: PMC8386322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Concurrent administration of cancer therapeutics with tumor vasculature targeting treatment has been shown to improve overall survival in multiple human cancer types, as such combinations aim to destroy different compartments of tumors. Anti-angiogenesis therapeutics designed to inhibit tumor induced vessel sprouting have also been shown to re-model the tumor vasculature through a transient vessel normalization effect, which leads to improved perfusion of oxygen and drug in tumor. However, the effects that this normalized vasculature has on the availability of cancer receptor, such as EGFR, is unknown. Herein, we examined the use of MRI-PAFT to estimate cancer surface receptor availability in response to anti-angiogenesis therapy, using MRI-coupled paired agent fluorescence tomography. Bevacizumab treated tumors showed increase in RA compared to control tumors, but this was not statistically significant.
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Affiliation(s)
- Boyu Meng
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
| | | | - Kenneth Tichauer
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | | | - Scott C Davis
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
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26
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Dot A, Bettega G, Lartizien R, Berger M, Henry M, Perriollat M, Coll JL, Planat-Chretien A. Chromophore reconstruction at depth in bilayered media: a method for quantification. BIOMEDICAL OPTICS EXPRESS 2021; 12:1279-1294. [PMID: 33796353 PMCID: PMC7984786 DOI: 10.1364/boe.401108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/03/2020] [Accepted: 11/09/2020] [Indexed: 06/12/2023]
Abstract
We report a method for deriving the absolute value of absorption coefficients at depth in bilayered media. The method was simplified from that of time-resolved diffuse optical tomography (TR-DOT) into one dimension to validate and set up the main parameters with the help of simulations, and to test it in an easy preclinical model. The method was applied to buried flaps as used in reconstructive surgery, and absolute chromophore concentrations in the flap and in the upper (skin and fat) layer were derived. The encouraging results obtained lay a foundation for developing more complex multidimensional models.
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Affiliation(s)
- Audrey Dot
- INSERM UGA U1209, Institute For Advanced Biosciences, F- 38700, Grenoble, France
| | - Georges Bettega
- Centre Hospitalier Annecy Genevois, F- 74374, Pringy, France
| | - Rodolphe Lartizien
- INSERM UGA U1209, Institute For Advanced Biosciences, F- 38700, Grenoble, France
- Centre Hospitalier Annecy Genevois, F- 74374, Pringy, France
| | - Michel Berger
- Univ. Grenoble Alpes, CEA, LETI, DTBS, LS2P, F- 38000, Grenoble, France
| | - Maxime Henry
- INSERM UGA U1209, Institute For Advanced Biosciences, F- 38700, Grenoble, France
| | | | - Jean-Luc Coll
- INSERM UGA U1209, Institute For Advanced Biosciences, F- 38700, Grenoble, France
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27
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Cai L, Nitta T, Yokota S, Obata T, Okada E, Kawaguchi H. Targeting brain regions of interest in functional near-infrared spectroscopy-Scalp-cortex correlation using subject-specific light propagation models. Hum Brain Mapp 2021; 42:1969-1986. [PMID: 33621388 PMCID: PMC8046049 DOI: 10.1002/hbm.25367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/05/2020] [Accepted: 01/31/2021] [Indexed: 11/11/2022] Open
Abstract
Targeting specific brain regions of interest by the accurate positioning of optodes (emission and detection probes) on the scalp remains a challenge for functional near‐infrared spectroscopy (fNIRS). Since fNIRS data does not provide any anatomical information on the brain cortex, establishing the scalp‐cortex correlation (SCC) between emission‐detection probe pairs on the scalp and the underlying brain regions in fNIRS measurements is extremely important. A conventional SCC is obtained by a geometrical point‐to‐point manner and ignores the effect of light scattering in the head tissue that occurs in actual fNIRS measurements. Here, we developed a sensitivity‐based matching (SBM) method that incorporated the broad spatial sensitivity of the probe pair due to light scattering into the SCC for fNIRS. The SCC was analyzed between head surface fiducial points determined by the international 10–10 system and automated anatomical labeling brain regions for 45 subject‐specific head models. The performance of the SBM method was compared with that of three conventional geometrical matching (GM) methods. We reveal that the light scattering and individual anatomical differences in the head affect the SCC, which indicates that the SBM method is compulsory to obtain the precise SCC. The SBM method enables us to evaluate the activity of cortical regions that are overlooked in the SCC obtained by conventional GM methods. Together, the SBM method could be a promising approach to guide fNIRS users in designing their probe arrangements and in explaining their measurement data.
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Affiliation(s)
- Lin Cai
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Tomonori Nitta
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Sho Yokota
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Takayuki Obata
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Eiji Okada
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Hiroshi Kawaguchi
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan.,Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.,Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
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Kovacsova Z, Bale G, Mitra S, Lange F, Tachtsidis I. Absolute quantification of cerebral tissue oxygen saturation with multidistance broadband NIRS in newborn brain. BIOMEDICAL OPTICS EXPRESS 2021; 12:907-925. [PMID: 33680549 PMCID: PMC7901317 DOI: 10.1364/boe.412088] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 05/23/2023]
Abstract
Tissue oximetry with near-infrared spectroscopy (NIRS) is a technique for the measurement of absolute tissue oxygen saturation (StO2). Offering a real-time and non-invasive assessment of brain oxygenation and haemodynamics, StO2 has potential to be used for the assessment of newborn brain injury. Multiple algorithms have been developed to measure StO2, however, issues with low measurement accuracy or extracranial tissue signal contamination remain. In this work, we present a novel algorithm to recover StO2 in the neonate, broadband multidistance oximetry (BRUNO), based on a measurement of the gradient of attenuation against distance measured with broadband NIRS. The performance of the algorithm was compared to two other published algorithms, broadband fitting (BF) and spatially resolved spectroscopy (SRS). The median error when recovering StO2 in light transport simulations on a neonatal head mesh was 0.4% with BRUNO, 4.2% with BF and 9.5% with SRS. BRUNO was more sensitive to brain tissue oxygenation changes, shown in layered head model simulations. Comparison of algorithm performance during full oxygenation-deoxygenation cycles in a homogeneous dynamic blood phantom showed significant differences in the dynamic range of the algorithms; BRUNO recovered StO2 over 0-100%, BF over 0-90% and SRS over 39-80%. Recovering StO2 from data collected in a neonate treated at the neonatal intensive care showed different baseline values; mean StO2 was 64.9% with BRUNO, 67.2% with BF and 73.2% with SRS. These findings highlight the effect of StO2 algorithm selection on oxygenation recovery; applying BRUNO in the clinical care setting could reveal further insight into complex haemodynamic processes occurring during neonatal brain injury.
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Affiliation(s)
- Zuzana Kovacsova
- Department of Medical Physics & Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Gemma Bale
- Department of Medical Physics & Biomedical Engineering, University College London, London, WC1E 6BT, UK
- Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
| | - Subhabrata Mitra
- Institute for Women’s Health, University College London and Neonatal Unit, University College London Hospitals Trust, London, NW1 2BU, UK
| | - Frédéric Lange
- Department of Medical Physics & Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Ilias Tachtsidis
- Department of Medical Physics & Biomedical Engineering, University College London, London, WC1E 6BT, UK
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Jiang J, Di Costanzo Mata A, Lindner S, Wolf M, Kalyanov A. Localization of Deep Ischemia and Hemorrhage in Preterm Infants' Head with Near-Infrared Optical Tomography: A Numerical Case Study. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1269:131-136. [PMID: 33966207 DOI: 10.1007/978-3-030-48238-1_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND AIM Preterm infants have a high incidence of brain lesions that may lead to long-term disabilities. Early diagnosis of cerebral ischemia and hemorrhage may enable protection of the brain by prevention or neuroprotective treatment. Our recently developed time-domain near-infrared optical tomography (TD NIROT) system provides images to diagnose neonatal brain injury. Our aim is to study the image quality achievable from the TD NIROT signals perturbed by noise for two common cases: ischemia and hemorrhage. METHODS We implemented simulations on a spherical model of diameter 60 mm representing a typical neonatal head where the absorption μa = 0.08 cm-1 and the reduced scattering μ's = 4.1 cm-1. Injury-mimicking spherical inclusions of various diameters (1 ~ 10 mm) were placed at depths of 10 ~ 20 mm in the ischemia case (2.5 × μa) and 14 ~ 30 mm for the hemorrhage case (50 × μa). TD data were generated from a large number of source-detector pairs, i.e., 208 detectors placed within a circle of diameter 40 mm on the surface surrounded by 18 sources. Up to 5% Gaussian noise was added in the simulations. 3D images were reconstructed with the modified Tikhonov minimization with the initial guess of a homogeneous phantom, and the images were evaluated by positional error and Dice similarity. RESULTS The inclusions were localized correctly with low positional errors (<1 mm), and the segmented images share a high Dice similarity with the ground truth for both the ischemia and the hemorrhage case, even for tiny inclusions of 1 mm in deep tissue. The hemorrhage case with a high contrast tolerates a substantial level of noise even though the performance drops with higher noise as expected. CONCLUSIONS The large amount of data provided by our novel TD NIROT system provides rich enough information for correctly locating hemorrhage and ischemia in the neonatal brain.
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Affiliation(s)
- Jingjing Jiang
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich and University of Zürich, Zurich, Switzerland.
| | - Aldo Di Costanzo Mata
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich and University of Zürich, Zurich, Switzerland
| | - Scott Lindner
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich and University of Zürich, Zurich, Switzerland
| | - Martin Wolf
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich and University of Zürich, Zurich, Switzerland
| | - Alexander Kalyanov
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich and University of Zürich, Zurich, Switzerland
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Probe Design Optimization for Time-Domain NIROT "Pioneer" System for Imaging the Oxygenation of the Preterm Brain. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1269:359-363. [PMID: 33966243 DOI: 10.1007/978-3-030-48238-1_57] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In preterm infants, there is a risk of life-lasting impairments due to hemorrhagic/ischemic lesions. Our time-domain (TD) near-infrared optical tomography (NIROT) system "Pioneer" aims at detecting both disorders with high spatial resolution. Successfully tested on phantoms, "Pioneer" entered the phase of improvements and enhancements. The current probe (A-probe) was adapted for an optoacoustics instrument. A new probe (B-probe) optimized for TD measurements is required. Our aim is to determine the optimal arrangement of light sources in the B-probe to increase the sensitivity and the resolution of Pioneer and to improve the ability of the system to detect both ischemia and hemorrhage. To do this, we simulated TD-NIROT signals in NIRFAST, a MATLAB-based package used to model near-infrared light propagation through tissue. We used 16 × 16 detector array, with ~2.2 mm distance between the detectors. Light sources were arranged around the field of view (FoV). We performed forward simulations of light propagation through a "homogeneous case" (HC) tissue (μ's = 5.6 cm-1, μa = 0.07 cm-1). Next, we simulated light propagation through "inhomogeneous case" -tissue' (IC) tissue by adding ischemia (μa = μa · 2.5 cm-1) or hemorrhage (μa = μa · 50 cm-1) to HT as a spherical inclusion of 5 mm radius at different depths in the FoV center and identified the source location that provides the higher contrast on the FoV: maxi ∈ I (FoVContrastSOURCE). It was found that sources located closer to the FoV center generate greater contrast for late photons. This study suggests the light sources in B-probe should be closer to the FoV center. The higher sensitivity is expected to lead to a higher image quality.
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Johnstonbaugh K, Agrawal S, Durairaj DA, Fadden C, Dangi A, Karri SPK, Kothapalli SR. A Deep Learning Approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2020; 67:2649-2659. [PMID: 31944951 PMCID: PMC7769001 DOI: 10.1109/tuffc.2020.2964698] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Optical photons undergo strong scattering when propagating beyond 1-mm deep inside biological tissue. Finding the origin of these diffused optical wavefronts is a challenging task. Breaking through the optical diffusion limit, photoacoustic (PA) imaging (PAI) provides high-resolution and label-free images of human vasculature with high contrast due to the optical absorption of hemoglobin. In real-time PAI, an ultrasound transducer array detects PA signals, and B-mode images are formed by delay-and-sum or frequency-domain beamforming. Fundamentally, the strength of a PA signal is proportional to the local optical fluence, which decreases with the increasing depth due to depth-dependent optical attenuation. This limits the visibility of deep-tissue vasculature or other light-absorbing PA targets. To address this practical challenge, an encoder-decoder convolutional neural network architecture was constructed with custom modules and trained to identify the origin of the PA wavefronts inside an optically scattering deep-tissue medium. A comprehensive ablation study provides strong evidence that each module improves the localization accuracy. The network was trained on model-based simulated PA signals produced by 16 240 blood-vessel targets subjected to both optical scattering and Gaussian noise. Test results on 4600 simulated and five experimental PA signals collected under various scattering conditions show that the network can localize the targets with a mean error less than 30 microns (standard deviation 20.9 microns) for targets below 40-mm imaging depth and 1.06 mm (standard deviation 2.68 mm) for targets at a depth between 40 and 60 mm. The proposed work has broad applications such as diffused optical wavefront shaping, circulating melanoma cell detection, and real-time vascular surgeries (e.g., deep-vein thrombosis).
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Affiliation(s)
- Kerrick Johnstonbaugh
- Department of Biomedical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
| | - Sumit Agrawal
- Department of Biomedical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
| | - Deepit Abhishek Durairaj
- Department of Electrical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
| | - Christopher Fadden
- Department of Electrical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
| | - Ajay Dangi
- Department of Biomedical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
| | - Sri Phani Krishna Karri
- Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, AP, India 534102
| | - Sri-Rajasekhar Kothapalli
- Department of Biomedical Engineering, Pennsylvania State University, University Park, State College, Pennsylvania, USA, 16802
- Penn State Cancer Institute, Pennsylvania State University, Hershey, Pennsylvania, USA, 17033
- Graduate Program in Acoustics, The Pennsylvania State University, University Park, Pennsylvania, USA 16802
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A wireless, skin-interfaced biosensor for cerebral hemodynamic monitoring in pediatric care. Proc Natl Acad Sci U S A 2020; 117:31674-31684. [PMID: 33257558 PMCID: PMC7749320 DOI: 10.1073/pnas.2019786117] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The standard of clinical care in many pediatric and neonatal neurocritical care units involves continuous monitoring of cerebral hemodynamics using hard-wired devices that physically adhere to the skin and connect to base stations that commonly mount on an adjacent wall or stand. Risks of iatrogenic skin injuries associated with adhesives that bond such systems to the skin and entanglements of the patients and/or the healthcare professionals with the wires can impede clinical procedures and natural movements that are critical to the care, development, and recovery of pediatric patients. This paper presents a wireless, miniaturized, and mechanically soft, flexible device that supports measurements quantitatively comparable to existing clinical standards. The system features a multiphotodiode array and pair of light-emitting diodes for simultaneous monitoring of systemic and cerebral hemodynamics, with ability to measure cerebral oxygenation, heart rate, peripheral oxygenation, and potentially cerebral pulse pressure and vascular tone, through the utilization of multiwavelength reflectance-mode photoplethysmography and functional near-infrared spectroscopy. Monte Carlo optical simulations define the tissue-probing depths for source-detector distances and operating wavelengths of these systems using magnetic resonance images of the head of a representative pediatric patient to define the relevant geometries. Clinical studies on pediatric subjects with and without congenital central hypoventilation syndrome validate the feasibility for using this system in operating hospitals and define its advantages relative to established technologies. This platform has the potential to substantially enhance the quality of pediatric care across a wide range of conditions and use scenarios, not only in advanced hospital settings but also in clinics of lower- and middle-income countries.
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Meng B, Folaron MR, Strawbridge RR, Sadeghipour N, Samkoe KS, Tichauer K, Davis SC. Noninvasive quantification of target availability during therapy using paired-agent fluorescence tomography. Am J Cancer Res 2020; 10:11230-11243. [PMID: 33042280 PMCID: PMC7532673 DOI: 10.7150/thno.45273] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/06/2020] [Indexed: 12/13/2022] Open
Abstract
Immuno-oncological treatment strategies that target abnormal receptor profiles of tumors are an increasingly important feature of cancer therapy. Yet, assessing receptor availability (RA) and drug-target engagement, important determinants of therapeutic efficacy, is challenging with current imaging strategies, largely due to the complex nonspecific uptake behavior of imaging agents in tumors. Herein, we evaluate whether a quantitative noninvasive imaging approach designed to compensate for nonspecific uptake, MRI-coupled paired-agent fluorescence tomography (MRI-PAFT), is capable of rapidly assessing the availability of epidermal growth factor receptor (EGFR) in response to one dose of anti-EGFR antibody therapy in orthotopic brain tumor models. Methods: Mice bearing orthotopic brain tumor xenografts with relatively high EGFR expression (U251) (N=10) or undetectable human EGFR (9L) (N=9) were considered in this study. For each tumor type, mice were either treated with one dose of cetuximab, or remained untreated. All animals were scanned using MRI-PAFT, which commenced immediately after paired-agent administration, and values of RA were recovered using a model-based approach, which uses the entire dynamic sequence of agent uptake, as well as a simplified “snapshot” approach which requires uptake measurements at only two time points. Recovered values of RA were evaluated between groups and techniques. Hematoxylin & eosin (H&E) and immunohistochemical (IHC) staining was performed on tumor specimens from every animal to confirm tumor presence and EGFR status. Results: In animals bearing EGFR(+) tumors, a significant difference in RA values between treated and untreated animals was observed (RA = 0.24 ± 0.15 and 0.61 ± 0.18, respectively, p=0.027), with an area under the curve - receiver operating characteristic (AUC-ROC) value of 0.92. We did not observe a statistically significant difference in RA values between treated and untreated animals bearing EGFR(-) tumors (RA = 0.18 ± 0.19 and 0.27 ± 0.21, respectively; p = 0.89; AUC-ROC = 0.55), nor did we observe a difference between treated EGFR(+) tumors compared to treated and untreated EGFR(-) tumors. Notably, the snapshot paired-agent strategy quantified drug-receptor engagement within just 30 minutes of agent administration. Examination of the targeted agent alone showed no capacity to distinguish tumors either by treatment or receptor status, even 24h after agent administration. Conclusions: This study demonstrated that a noninvasive imaging strategy enables rapid quantification of receptor availability in response to therapy, a capability that could be leveraged in preclinical drug development, patient stratification, and treatment monitoring.
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Zhai X, Santosa H, Huppert TJ. Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy. NEUROPHOTONICS 2020; 7:035008. [PMID: 32995360 PMCID: PMC7509340 DOI: 10.1117/1.nph.7.3.035008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/14/2020] [Indexed: 05/27/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.
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Affiliation(s)
- Xuetong Zhai
- University of Pittsburgh, Department of Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Department of Electrical and Computer Engineering, Department of Bioengineering, Pittsburgh, Pennsylvania, United States
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Wang H, Wu N, Zhao Z, Han G, Zhang J, Wang J. Continuous monitoring method of cerebral subdural hematoma based on MRI guided DOT. BIOMEDICAL OPTICS EXPRESS 2020; 11:2964-2975. [PMID: 32637235 PMCID: PMC7316016 DOI: 10.1364/boe.388059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/14/2020] [Accepted: 04/26/2020] [Indexed: 05/18/2023]
Abstract
Cerebral subdural hematomas due to trauma can easily worsen suddenly due to the rupture of blood vessels in the brain after the condition is stabilized. Therefore, continuous monitoring of the size of cerebral subdural hematomas has important clinical significance. To achieve fast, real-time, noninvasive, and accurate monitoring of subdural hematomas, a cerebral subdural hematoma monitoring method combining brain magnetic resonance imaging (MRI) image guidance, diffusion optical tomography technology, and deep learning is proposed in this manuscript. First, an MRI brain image is segmented to obtain a three-dimensional multi-layer brain model with structures and parameters matching a real brain. Then, a near-infrared light source and detectors (source-detector separations ranging from 0.5 to 6.5 cm) were placed on the model to achieve fast, real-time and noninvasive acquisition of intracranial hematoma information. Finally, a deep learning method is used to obtain accurate reconstructed images of cerebral subdural hematomas. The experimental results show that the reconstruction effect of stacked auto-encoder with the mean volume error of 0.1 ml is better than the result reconstructed by algebraic reconstruction techniques with the mean volume error of 0.9 ml. Under different signal-to-noise ratios, the curve fitting R2 between the actual blood volume of a simulated hematoma and a reconstructed hematoma is more than 0.95. We conclude that the proposed monitoring method can realize fast, noninvasive, real-time, and accurate monitoring of subdural hematomas, and can provide a technical basis for continuous wearable subdural hematoma monitoring equipment.
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Affiliation(s)
- Huiquan Wang
- School of Life Sciences, Tiangong University, Tianjin 300387, China
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China
| | - Nian Wu
- School of Life Sciences, Tiangong University, Tianjin 300387, China
| | - Zhe Zhao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China
| | - Guang Han
- School of Life Sciences, Tiangong University, Tianjin 300387, China
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China
| | - Jun Zhang
- School of Life Sciences, Tiangong University, Tianjin 300387, China
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China
| | - Jinhai Wang
- School of Life Sciences, Tiangong University, Tianjin 300387, China
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China
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Wi W, Park SM, Shin BS. Computed Tomography-Based Preoperative Simulation System for Pedicle Screw Fixation in Spinal Surgery. J Korean Med Sci 2020; 35:e125. [PMID: 32383365 PMCID: PMC7211513 DOI: 10.3346/jkms.2020.35.e125] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 03/01/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND A preoperative planning system facilitates improving surgical outcomes that depend on the experience of the surgeons, thanks to real-time interaction between the system and surgeons. It visualizes intermediate surgical planning results to help surgeons discuss the planning. The purpose of this study was to demonstrate the use of a newly-developed preoperative planning system for surgeons less experienced in pedicle-screw fixation in spinal surgery, especially on patients with anatomical variations. METHODS The marching cubes algorithm, a typical surface extraction technique, was applied to computed tomography (CT) images of vertebrae to enable three-dimensional (3D) reconstruction of a spinal mesh. Real-time processing of such data is difficult, as the surface mesh extracted from high-resolution CT data is rough, and the size of the mesh is large. To mitigate these factors, Laplacian smoothing was applied, followed by application of a quadric error metric-based mesh simplification to reduce the mesh size for the level-of-detail (LOD) image. Taubin smoothing was applied to smooth out the rough surface. On a multiplanar reconstruction (MPR) cross-sectional image or a 3D model view, the insertion position and orientation of the pedicle screw were manipulated using a mouse. The results after insertion were then visualized in each image. RESULTS The system was used for pre-planning pedicle-screw fixation in spinal surgery. Using any pointing device such as a mouse, surgeons can manipulate the position and angle of the screws. The pedicle screws were easy to manipulate intuitively on the MPR images, and the accuracy of screw fixation was confirmed on a trajectory view and 3D images. After surgery, CT scans were performed again, and the CT images were checked to ensure that the screws were inserted properly. CONCLUSION The preoperative planning system allows surgeons and students who are not familiar with pedicle-screw fixation to safely undertake surgery following preoperative planning. It also provides opportunities for screw-fixation training and simulation.
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Affiliation(s)
- Woochan Wi
- Department of Computer Engineering, Inha University, Incheon, Korea
| | - Sang Min Park
- Department of Orthopaedic Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Byung Seok Shin
- Department of Computer Engineering, Inha University, Incheon, Korea.
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Ebrahimpour A, Zakariaee SS, Hejazi M. Sensitivity Laplacian Ratio-Based Optimization of the Projection Selection for Diffuse Optical Tomography. JOURNAL OF MEDICAL SIGNALS AND SENSORS 2020; 10:119-124. [PMID: 32676448 PMCID: PMC7359954 DOI: 10.4103/jmss.jmss_67_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 10/28/2019] [Accepted: 01/06/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND In diffuse optical tomography, determining the optimal angle between the source and detector is an effective method to reduce the number of projections while maintaining the quality of the reconstructed images. In this study, a new parameter is introduced to evaluate the source-detector geometries. METHODS A two-dimensional mesh with the radius of 20 mm and 7987 nodes were built. In each reconstruction, 0.5 mm heterogeneity with the absorption coefficient of 0.06 mm-1 and the dispersion coefficient of 0.6 mm-1 was added in different parts of the sample randomly. The relationship between the mean square error (MSE), sensitivity Laplacian ratio (SLR), and sensitivity standard deviation ratio (SSR) was evaluated based on their correlation coefficients. The quality of the images achieved using the optimized projections were compared with that of the full projections for the same depths. RESULTS MSE decreases by increasing the SLR magnitudes which indicate that the parameter could be used to evaluate the scanning geometries. There was a negative correlation coefficient (R = -0.76) with the inverse relationship between the SLR and MSE indices. SSR does not have a significant relationship with the quality of the reconstructed images. For each scanning depth, the comparison of the images obtained using the full and optimized-selective projections did not show any considerable difference despite the decrease of the projection numbers in scanning geometry with the optimized-selective projections. CONCLUSION The unnecessary projections could be eliminated by placing the detectors at the specific angles, which were determined using the SLR. Thus, a proper compromise between the quality of the reconstructed images and reconstruction time might establish.
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Affiliation(s)
- Anita Ebrahimpour
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Marjaneh Hejazi
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran,Address for correspondence: Dr. Marjaneh Hejazi, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran. E-mail:
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Performance assessment of high-density diffuse optical topography regarding source-detector array topology. PLoS One 2020; 15:e0230206. [PMID: 32208433 PMCID: PMC7092988 DOI: 10.1371/journal.pone.0230206] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 02/24/2020] [Indexed: 11/19/2022] Open
Abstract
Recent advances in optical neuroimaging systems as a functional interface enhance our understanding of neuronal activity in the brain. High density diffuse optical topography (HD-DOT) uses multi-distance overlapped channels to improve the spatial resolution of images comparable to functional magnetic resonance imaging (fMRI). The topology of the source and detector (SD) array directly impacts the quality of the hemodynamic reconstruction in HD-DOT imaging modality. In this work, the effect of different SD configurations on the quality of cerebral hemodynamic recovery is investigated by presenting a simulation setup based on the analytical approach. Given that the SD arrangement determines the elements of the Jacobian matrix, we conclude that the more individual components in this matrix, the better the retrieval quality. The results demonstrate that the multi-distance multi-directional (MDMD) arrangement produces more unique elements in the Jacobian array. Consequently, the inverse problem can accurately retrieve the brain activity of diffuse optical topography data.
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Meng B, Strawbridge RR, Tichauer K, Samkoe KS, Davis SC. Estimating paired-agent uptake in altered tumor vasculature using MRI-coupled fluorescence tomography. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11216:112160U. [PMID: 34446978 PMCID: PMC8386501 DOI: 10.1117/12.2546702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Angiogenesis inhibiting cancer therapy has become a standard treatment for many cancer types. The ability to examine the effects of these drugs in tumors noninvasively could help assess efficacy early in the treatment course or identify optimal times to introduce other combinatorial treatments. Herein, we examine whether a paired agent MRI-coupled fluorescence tomography approach can be used to monitor the effects of anti-angiogenesis therapy. Using small animal models bearing orthotopic glioma xenografts, we demonstrate noninvasive quantification of paired-agent uptake in response to anti-angiogenesis therapy in vivo. The result provides insights on receptor targeted drug delivery in altered vasculature, a potential important development for treatment monitoring and combinatorial strategies.
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Affiliation(s)
- Boyu Meng
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
| | | | - Kenneth Tichauer
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | | | - Scott C Davis
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
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Ren J, Han S, Proctor AR, Desa DE, Ramirez GA, Ching-Roa VRD, Majeski JB, Dar IA, Barber NE, Forti AM, Benoit DSW, Choe R. Longitudinal 3D Blood Flow Distribution Provided by Diffuse Correlation Tomography during Bone Healing in a Murine Fracture Model. Photochem Photobiol 2020; 96:380-387. [PMID: 31883385 DOI: 10.1111/php.13201] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 11/20/2019] [Indexed: 12/19/2022]
Abstract
Noninvasive monitoring of vascularization can potentially diagnose impaired bone healing earlier than current radiographic methods. In this study, a noncontact diffuse correlation tomography (DCT) technique was employed to measure longitudinal blood flow changes during bone healing in a murine femoral fracture model. The three-dimensional distribution of the relative blood flow was quantified from one day pre-fracture to 48 days post-fracture. For three mice, frequent DCT measurements were performed every other day for one week after fracture, and then weekly thereafter. A decrease in blood flow was observed in the bone fracture region at one day post-fracture, followed by a monotonic increase in blood flow beyond the pre-injury baseline until five to seven days post-fracture. For the remaining 12 mice, only weekly DCT measurements were performed. Data collected on a weekly basis show the blood flow for most mice was elevated above baseline during the first two post-fracture weeks, followed by a subsequent decrease. Torsional strength of the excised femurs was measured for all 15 mice after 7 weeks of healing. A metric based on the early blood flow changes shows a statistically significant difference between the high strength group and the low strength group.
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Affiliation(s)
- Jingxuan Ren
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Songfeng Han
- Institute of Optics, University of Rochester, Rochester, NY
| | - Ashley R Proctor
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Danielle E Desa
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Gabriel A Ramirez
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | | | - Joseph B Majeski
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Irfaan A Dar
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Nathaniel E Barber
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Amanda M Forti
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Danielle S W Benoit
- Department of Biomedical Engineering, University of Rochester, Rochester, NY.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester, Rochester, NY.,Department of Chemical Engineering, University of Rochester, Rochester, NY.,Department of Biomedical Genetics and Center for Oral Biology, University of Rochester, Rochester, NY.,Materials Science Program, University of Rochester, Rochester, NY
| | - Regine Choe
- Department of Biomedical Engineering, University of Rochester, Rochester, NY.,Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY
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Peditto M, Nucera R, Rubino E, Marcianò A, Bitto M, Catania A, Oteri G. Improving Oral Surgery: A Workflow Proposal to Create Custom 3D Templates for Surgical Procedures. Open Dent J 2020. [DOI: 10.2174/1874210602014010035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Computer-guided technologies are adopted in various fields of surgery to limit invasiveness and obtain patient benefits in terms of surgery duration and post-operative course. Surgical templates realized through CAD/CAM technologies are widely diffused in implant dentistry. The aim of this work is to propose, beyond implantology, the feasibility of application of 3D printed surgical templates in oral surgery procedures requiring osteotomies (like maxillary cyst enucleation and tooth disimpaction) in order to obtain accurate surgeries, avoid anatomical damage of surrounding structures and decrease patient’s morbidity, using a simple, low-cost protocol of fabrication.
Objective:
To provide a reliable CAD-CAM workflow for the realization of surgical templates in oral surgery.
Methods:
Three clinical scenarios are described: A maxillary canine disimpaction, a mandibular cyst removal, and an orthodontic miniscrew placement. Each one was managed using custom surgical templates realized using the proposed workflow. A Stereolithography (STL) file of maxillary structures was obtained by the use of a 3D medical image processing software (Materialise Mimics 20.0) a segmentation toolbox acquiring RX volumes by Cone-Beam Computed Tomography (CBCT). Digital models of the teeth, acquired as STL files directly, are imported in the same 3D medical image processing freeware (Materialise Mimics 20.0) to merge STL files of maxillary structures and teeth. Data are transported into Blue Sky Plan 4.0 (Blue Sky Bio, LLC), a software for 3D implant guides fabrication, together with the DICOM images package of maxillary volumes to carry out the pre-surgical treatment planning. Anatomical structures at risk are identified; a contour of ideal incision shape and bone osteotomy extent is drawn. Finally, the resulting three-dimensional guide is digitally generated and the surgical guide printed. The resulting 3D template shows the following major features: teeth support, flap management and bone osteotomy design.
Results:
The proposed work-flow aided the surgeon in both pre-operative and intra-operative work phases through accurate virtual planning and the fabrication of precise surgical guides to be used in oral surgery practice. In each clinical scenario, the use of custom 3D templates allowed better control of the osteotomy planes and flap management. No adverse events occurred during both surgical and healing phases.
Conclusion:
The proposed digital workflow represents a reliable and straightforward way to produce a surgical guide for oral surgery procedures. These templates represent a versatile tool in maxillary cyst enucleations, tooth disimpaction, and other surgical procedures, increasing accuracy, minimizing surgical complications, and decreasing patient’s morbidity.
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Tran AP, Yan S, Fang Q. Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models. NEUROPHOTONICS 2020; 7:015008. [PMID: 32118085 PMCID: PMC7035879 DOI: 10.1117/1.nph.7.1.015008] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 02/05/2020] [Indexed: 05/04/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models-the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model-and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between - 1.5 % to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes "Brain2Mesh" and "Iso2Mesh" are freely available at http://mcx.space/brain2mesh.
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Affiliation(s)
- Anh Phong Tran
- Northeastern University, Department of Chemical Engineering, Boston, Massachusetts, United States
| | - Shijie Yan
- Northeastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Address all correspondence to Qianqian Fang, E-mail:
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Daly MJ, Chan H, Muhanna N, Akens MK, Wilson BC, Irish JC, Jaffray DA. Intraoperative cone-beam CT spatial priors for diffuse optical fluorescence tomography. ACTA ACUST UNITED AC 2019; 64:215007. [DOI: 10.1088/1361-6560/ab4917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Ferrari M, Daly MJ, Douglas CM, Chan HHL, Qiu J, Deganello A, Taboni S, Thomas CM, Sahovaler A, Jethwa AR, Hasan W, Nicolai P, Gilbert RW, Irish JC. Navigation-guided osteotomies improve margin delineation in tumors involving the sinonasal area: A preclinical study. Oral Oncol 2019; 99:104463. [PMID: 31683173 DOI: 10.1016/j.oraloncology.2019.104463] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/16/2019] [Accepted: 10/22/2019] [Indexed: 11/20/2022]
Abstract
OBJECTIVES To demonstrate and quantify, in a preclinical setting, the benefit of three-dimensional (3D) navigation guidance for margin delineation during ablative open surgery for advanced sinonasal cancer. MATERIALS AND METHODS Seven tumor models were created. 3D images were acquired with cone beam computed tomography, and 3D tumor segmentations were contoured. Eight surgeons with variable experience were recruited for the simulation of osteotomies. Three simulations were performed: 1) Unguided, 2) Guided using real-time tool tracking with 3D tumor segmentation (tumor-guided), and 3) Guided by 3D visualization of both the tumor and 1-cm margin segmentations (margin-guided). Analysis of cutting planes was performed and distance from the tumor surface was classified as follows: "intratumoral" when 0 mm or negative, "close" when greater than 0 mm and less than or equal to 5 mm, "adequate" when greater than 5 mm and less than or equal to 15 mm, and "excessive" over 15 mm. The three techniques (unguided, tumor-guided, margin-guided) were statistically compared. RESULTS The use of 3D navigation for margin delineation significantly improved control of margins: unguided cuts had 18.1% intratumoral cuts compared to 0% intratumoral cuts with 3D navigation (p < 0.0001). CONCLUSION This preclinical study has demonstrated the significant benefit of navigation-guided osteotomies for sinonasal tumors. Translation into the clinical setting - with rigorous assessment of oncological outcomes - would be the proposed next step.
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Affiliation(s)
- Marco Ferrari
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada; Unit of Otorhinolaryngology - Head and Neck Surgery, University of Brescia, Brescia, Italy; Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Michael J Daly
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Catriona M Douglas
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada; Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Harley H L Chan
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Jimmy Qiu
- Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Alberto Deganello
- Unit of Otorhinolaryngology - Head and Neck Surgery, University of Brescia, Brescia, Italy
| | - Stefano Taboni
- Unit of Otorhinolaryngology - Head and Neck Surgery, University of Brescia, Brescia, Italy
| | - Carissa M Thomas
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Axel Sahovaler
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Ashok R Jethwa
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Wael Hasan
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Piero Nicolai
- Unit of Otorhinolaryngology - Head and Neck Surgery, University of Brescia, Brescia, Italy
| | - Ralph W Gilbert
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada
| | - Jonathan C Irish
- Department of Otolaryngology - Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada; Guided Therapeutics (GTx) Program, Techna Institute, University Health Network, Toronto, Ontario, Canada.
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Almajidy RK, Mankodiya K, Abtahi M, Hofmann UG. A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments. IEEE Rev Biomed Eng 2019; 13:292-308. [PMID: 31634142 DOI: 10.1109/rbme.2019.2944351] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.
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Daly MJ, Wilson BC, Irish JC, Jaffray DA. Navigated non-contact fluorescence tomography. ACTA ACUST UNITED AC 2019; 64:135021. [DOI: 10.1088/1361-6560/ab1f33] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Doulgerakis M, Eggebrecht AT, Dehghani H. High-density functional diffuse optical tomography based on frequency-domain measurements improves image quality and spatial resolution. NEUROPHOTONICS 2019; 6:035007. [PMID: 31482102 PMCID: PMC6702521 DOI: 10.1117/1.nph.6.3.035007] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 07/30/2019] [Indexed: 05/18/2023]
Abstract
Measurements of dynamic near-infrared (NIR) light attenuation across the human head together with model-based image reconstruction algorithms allow the recovery of three-dimensional spatial brain activation maps. Previous studies using high-density diffuse optical tomography (HD-DOT) systems have reported improved image quality over sparse arrays. These HD-DOT systems incorporated multidistance overlapping continuous wave measurements that only recover differential intensity attenuation. We investigate the potential improvement in reconstructed image quality due to the additional incorporation of phase shift measurements, which reflect the time-of-flight of the measured NIR light, within the tomographic reconstruction from high-density measurements. To evaluate image reconstruction with and without the additional phase information, we simulated point spread functions across a whole-scalp field of view in 24 subject-specific anatomical models using an experimentally derived noise model. The addition of phase information improves the image quality by reducing localization error by up to 59% and effective resolution by up to 21% as compared to using the intensity attenuation measurements alone. Furthermore, we demonstrate that the phase data enable images to be resolved at deeper brain regions where intensity data fail, which is further supported by utilizing experimental data from a single subject measurement during a retinotopic experiment.
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Affiliation(s)
- Matthaios Doulgerakis
- University of Birmingham, School of Computer Science, Birmingham, England, United Kingdom
| | - Adam T. Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, England, United Kingdom
- Address all correspondence to Hamid Dehghani, E-mail:
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Ren W, Isler H, Wolf M, Ripoll J, Rudin M. Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation. IEEE Trans Biomed Eng 2019; 67:16-26. [PMID: 30990170 DOI: 10.1109/tbme.2019.2907460] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Fluorescence molecular tomography (FMT) can provide valuable molecular information by mapping the bio-distribution of fluorescent reporter molecules in the intact organism. Various prototype FMT systems have been introduced during the past decade. However, none of them has evolved as a standard tool for routine biomedical research. The goal of this paper is to develop a software package that can automate the complete FMT reconstruction procedure. METHODS We present smart toolkit for fluorescence tomography (STIFT), a comprehensive platform comprising three major protocols: 1) virtual FMT, i.e., forward modeling and reconstruction of simulated data; 2) control of actual FMT data acquisition; and 3) reconstruction of experimental FMT data. RESULTS Both simulation and phantom experiments have shown robust reconstruction results for homogeneous and heterogeneous tissue-mimicking phantoms containing fluorescent inclusions. CONCLUSION STIFT can be used for optimization of FMT experiments, in particular for optimizing illumination patterns. SIGNIFICANCE This paper facilitates FMT experiments by bridging the gaps between simulation, actual experiments, and data reconstruction.
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Ebrahimpour A, Zakariaee SS, Hejazi M. Sensitivity Uniformity Ratio as a New Index to Optimize the Scanning Geometry for Fluorescent Molecular Tomography. JOURNAL OF MEDICAL SIGNALS & SENSORS 2019; 9:42-49. [PMID: 30967989 PMCID: PMC6419568 DOI: 10.4103/jmss.jmss_22_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background: Molecular fluorescence imaging is widely used as a noninvasive method to study the cellular and molecular mechanisms. In the optical imaging system, the sensitivity is the change of the intensity received by the detector for the changed optical characteristics (fluorescence) in each sample point. Sensitivity could be considered as a function of imaging geometry. A favor imaging system has a uniform and high-sensitivity coefficient for each point of the sample. In this study, a new parameter was proposed which the optimal angle between the source and detector could be determined based on this parameter. Methods: For evaluation of the new method, a two-dimensional mesh with a radius of 20 mm and 5133 nodes was built. In each reconstruction, 0.5-mm fluorescence heterogeneity with a contrast-to-purpose ratio of fluorescence yield of 10 was randomly added at different points of the sample. The source and the detector were simulated in different geometric conditions. The calculations were performed using the NIRFAST and MATLAB software. The relationship between mean squared error (MSE) and sensitivity uniformity ratio (SUR) was evaluated using the correlation coefficient. Finally, based on the new index, an optimal geometrical strategy was introduced. Results: There was a negative correlation coefficient (R = −0.78) with the inverse relationship between the SUR and MSE indices. The reconstructed images showed that the better image quality achieved using the optimal geometry for all scanning depths. For the conventional geometry, there is an artifact in the opposite side of the inhomogeneity at the shallow depths, which has been eliminated in the reconstructed images achieved using the optimal geometry. Conclusion: The SUR is a powerful computational tool which could be used to determine the optimal angles between the source and detector for each scanning depth.
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Affiliation(s)
- Anita Ebrahimpour
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Marjaneh Hejazi
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Naser MA, Sampaio DRT, Muñoz NM, Wood CA, Mitcham TM, Stefan W, Sokolov KV, Pavan TZ, Avritscher R, Bouchard RR. Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:561-571. [PMID: 30207951 PMCID: PMC6445252 DOI: 10.1109/tmi.2018.2867602] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO2) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO2) and in vivo rat arteries. Estimation error of absolute SO2 and change in SO2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO2 with PA imaging.
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Affiliation(s)
- Mohamed A. Naser
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Diego R. T. Sampaio
- Department of Physics, University of Sao Paulo, Ribeirao Preto, SP 14040-901, BRAZIL
| | - Nina M. Muñoz
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Cayla A. Wood
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Trevor M. Mitcham
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Wolfgang Stefan
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Konstantin V. Sokolov
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
| | - Theo Z. Pavan
- Department of Physics, University of Sao Paulo, Ribeirao Preto, SP 14040-901, BRAZIL
| | - Rony Avritscher
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
| | - Richard R. Bouchard
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA ().; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
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