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Origlia C, Rodriguez-Duarte DO, Tobon Vasquez JA, Bolomey JC, Vipiana F. Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:4515. [PMID: 39065913 PMCID: PMC11280878 DOI: 10.3390/s24144515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/05/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.
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Affiliation(s)
- Cristina Origlia
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (C.O.); (D.O.R.-D.); (J.A.T.V.)
| | - David O. Rodriguez-Duarte
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (C.O.); (D.O.R.-D.); (J.A.T.V.)
| | - Jorge A. Tobon Vasquez
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (C.O.); (D.O.R.-D.); (J.A.T.V.)
| | | | - Francesca Vipiana
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (C.O.); (D.O.R.-D.); (J.A.T.V.)
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Jiang Y, Iuanow E, Malik B, Klock J. A Multireader Multicase (MRMC) Receiver Operating Characteristic (ROC) Study Evaluating Noninferiority of Quantitative Transmission (QT) Ultrasound to Digital Breast Tomosynthesis (DBT) on Detection and Recall of Breast Lesions. Acad Radiol 2024; 31:2248-2258. [PMID: 38290888 DOI: 10.1016/j.acra.2023.12.038] [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: 08/16/2023] [Revised: 12/16/2023] [Accepted: 12/26/2023] [Indexed: 02/01/2024]
Abstract
RATIONALE AND OBJECTIVES Quantitative transmission (QT) imaging is an emerging volumetric ultrasound modality for women too young for mammography. QT images tissue without overlap seen in mammography, thereby can potentially improve breast mass detection and characterization and noncancer recall. We compared radiologists' interpretation of QT vs digital breast tomosynthesis (DBT) with a multireader multicase observer performance study. MATERIALS AND METHODS Study subjects received screening DBT and QT scans in HIPAA-compliant, institutional review board-approved prospective case-collection studies at four clinical sites. Twenty-four Mammography Quality Standards Act-qualified radiologists interpreted 177 cases (66 with cancer, atypia, or solid mass and 111 normal or with nonsolid benign abnormality), first QT, then 2 weeks later DBT synthesized 2D-views. Readers reported up to three findings per case and for each finding a recall or no recall decision and confidence of that decision. The study hypothesis was area under receiver operating characteristic curve (AUC) of QT was noninferior to DBT. Sensitivity and specificity were also compared. RESULTS AUC of QT (0.746 ± 0.028, mean ± SD) was noninferior to DBT (0.700 ± 0.028) for AUC difference margin of -0.05 (P < .05). AUC difference was 0.046 ± 0.028 (95% CI: [-0.008, 0.101]). Sensitivity was 70.6 ± 7.2% for QT and 85.2 ± 6.4% for DBT, specificity was 60.1 ± 12.3% vs 37.2 ± 11.0%, and both differences were statistically significant. Of a total of 21 cases of cysts, readers recommended recall, on average, in 1.1 ± 1.4 cases with QT, but not with DBT, and 10.6 ± 2.2 cases with DBT, but not with QT. CONCLUSION QT can be a potential alternative to mammography for breast cancer screening of women too young to undergo mammography.
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Affiliation(s)
- Yulei Jiang
- Department of Radiology, the University of Chicago, 5841 South Maryland Ave, MC2026, Chicago, IL 60637.
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Song H, Sasada S, Kadoya T, Arihiro K, Okada M, Xiao X, Ishikawa T, O'Loughlin D, Takada JI, Kikkawa T. Cross-Correlation of Confocal Images for Excised Breast Tissues of Total Mastectomy. IEEE Trans Biomed Eng 2024; 71:1705-1716. [PMID: 38163303 DOI: 10.1109/tbme.2023.3348480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
OBJECT The purpose of this study is to develop an image artifact removal method for radar-based microwave breast imaging and demonstrates the detectability on excised breast tissues of total mastectomy. METHODS A cross-correlation method was proposed and measurements were conducted. A hand-held radar-based breast cancer detector was utilized to measure a breast at different orientations. Images were generated by multiplying the confocal image data from two scans after cross-correlation. The optimum reconstruction permittivity values were extracted by the local maxima of the confocal image intensity as a function of reconstruction permittivity. RESULTS With the proposed cross-correlation method, the contrast of the imaging result was enhanced and the clutters were removed. The proposed method was applied to 50 cases of excised breast tissues and the detection sensitivity of 72% was achieved. With the limited number of samples, the dependency of detection sensitivity on the breast size, breast density, and tumor size were examined. CONCLUSION AND SIGNIFICANCE The detection sensitivity was strongly influenced by the breast density. The sensitivity was high for fatty breasts, whereas the sensitivity was low for heterogeneously dense breasts. In addition, it was observed that the sensitivity was high for extremely dense breast. This is the first detailed report on the excised breast tissues.
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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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Álvarez Sánchez-Bayuela D, Giovanetti González R, Aguilar Angulo PM, Cruz Hernández LM, Sánchez-Camacho González-Carrato MDP, Rodríguez Sánchez A, Tiberi G, Romero Castellano C. Integrating clinical research in an operative screening and diagnostic breast imaging department: First experience, results and perspectives using microwave imaging. Heliyon 2023; 9:e21904. [PMID: 38027895 PMCID: PMC10661199 DOI: 10.1016/j.heliyon.2023.e21904] [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: 07/27/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Rationale and objectives Clinical research is crucial for evaluating new medical procedures and devices. It is important for healthcare units and hospitals to minimize the disruptions caused by conducting clinical studies; however, complex clinical pathways require dedicated recruitment and study designs.This work presents the effective introduction of novel microwave breast imaging (MBI), via MammoWave apparatus, into the clinical routine of an operative screening and diagnostic breast imaging department for conducting a multicentric clinical study. Materials and methods Microwave breast imaging, using MammoWave apparatus, was performed on volunteers coming from different clinical pathways. Clinical data, comprising demographics and conventional radiologic reports (used as reference standard), was collected; a satisfaction questionnaire was filled by every volunteer. Microwave images were analyzed by an automatic clinical decision support system, which quantified their corresponding features to discriminate between breasts with no relevant radiological findings (NF) and breasts with described findings (WF). Results Conventional breast imaging (DBT, US, MRI) and MBI were performed and adapted to assure best clinical practices and optimum pathways. 180 volunteers, both symptomatic and asymptomatic, were enrolled in the study. After microwave images' quality assessment, 48 NF (15 dense) and 169 WF (88 dense) breasts were used for the prospective study; 48 (18 dense) breasts suffered from a histology-confirmed carcinoma. An overall sensitivity of 85.8 % in breasts lesions' detection was achieved by the microwave imaging apparatus. Conclusion An optimum recruitment strategy was implemented to assess MBI. Future trials may show the clinical usefulness of microwave imaging, which may play an important role in breast screening.
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Affiliation(s)
- Daniel Álvarez Sánchez-Bayuela
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
- Faculty of Chemical Science and Technology, Instituto Regional de Investigación Científica Aplicada, University of Castilla, La Mancha, 13001, Ciudad Real, Spain
| | - Rubén Giovanetti González
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
| | - Paul Martín Aguilar Angulo
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
| | - Lina Marcela Cruz Hernández
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
| | | | - Ana Rodríguez Sánchez
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
| | - Gianluigi Tiberi
- UBT—Umbria Bioengineering Technologies, 06081, Perugia, Italy
- School of Engineering, London South Bank University, London, SE1 0AA, United Kingdom
| | - Cristina Romero Castellano
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007, Toledo, Spain
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Smith K, Bourqui J, Wang Z, Besler B, Lesiuk M, Roumeliotis M, Quirk S, Grendarova P, Pinilla J, Price S, Docktor B, Fear E. Microwave imaging for monitoring breast cancer treatment: A pilot study. Med Phys 2023; 50:7118-7129. [PMID: 37800880 DOI: 10.1002/mp.16756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 08/06/2023] [Accepted: 08/06/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Microwave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring. PURPOSE This study aims to detect treatment-related changes in breast tissue with a low-resolution microwave scanner. METHODS Microwave scans of 15 patients undergoing treatment for early-stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post-treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6-week results are analyzed with a two-way ANOVA with blocking. RESULTS Consistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6-weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences (p < 0.05 $p < 0.05$ ) in the treated and untreated breast at these time points. CONCLUSIONS Microwave imaging has the potential to track treatment-related changes in breast tissues.
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Affiliation(s)
- Katrin Smith
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Jeremie Bourqui
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Zefang Wang
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Brendon Besler
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Mark Lesiuk
- Tom Baker Cancer Centre, Calgary, Alberta, Canada
| | - Michael Roumeliotis
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Sarah Quirk
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
- Radiation Oncology, Brigham and Women's Hospital, Harvard, Boston, Massachusetts, USA
| | - Petra Grendarova
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
- BC Cancer Victoria, Victoria, British Columbia, Canada
| | | | - Sarah Price
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Bobbie Docktor
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Elise Fear
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
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Saleh GA, Batouty NM, Gamal A, Elnakib A, Hamdy O, Sharafeldeen A, Mahmoud A, Ghazal M, Yousaf J, Alhalabi M, AbouEleneen A, Tolba AE, Elmougy S, Contractor S, El-Baz A. Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review. Cancers (Basel) 2023; 15:5216. [PMID: 37958390 PMCID: PMC10650187 DOI: 10.3390/cancers15215216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
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Affiliation(s)
- Gehad A. Saleh
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Nihal M. Batouty
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Abdelrahman Gamal
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elnakib
- Electrical and Computer Engineering Department, School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA;
| | - Omar Hamdy
- Surgical Oncology Department, Oncology Centre, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Marah Alhalabi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Amal AbouEleneen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elsaid Tolba
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
- The Higher Institute of Engineering and Automotive Technology and Energy, New Heliopolis, Cairo 11829, Egypt
| | - Samir Elmougy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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Wiskin J, Malik B, Klock J. Low frequency 3D transmission ultrasound tomography: technical details and clinical implications. Z Med Phys 2023; 33:427-443. [PMID: 37295982 PMCID: PMC10517404 DOI: 10.1016/j.zemedi.2023.04.006] [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: 07/01/2022] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 06/12/2023]
Abstract
A novel 3D ultrasound tomographic (3D UT) method (called volography) that creates a speed of sound (SOS) map and a reflection modality that is co-registered are reviewed and shown to be artifact free even in the presence of high contrast and thus shown to be applicable for breast, orthopedic and pediatric clinical use cases. The 3D UT images are almost isotropic with mm resolution and the reflection image is compounded over 360 degrees to create sub-mm resolution in plane. METHODS The physics of ultrasound scattering requires 3D modeling and the concomitant high computational cost is ameliorated with a bespoke algorithm (paraxial approximation - discussed here) and Nvidia GPUs. The resulting reconstruction times are tabulated for clinical relevance. The resulting SOS map is used to create a refraction corrected reflection image at ∼3.6 MHz center frequency. The transmission data are highly redundant, collected over 360 degrees and at 2 mm levels by true matrix receiver arrays yielding 3D data. The high resolution SOS and attenuation maps and reflection images are used in a segmentation algorithm that optimally utilizes this information to segment out glandular, ductal, connective tissue, fat and skin. These volumes are used to estimate breast density, an important correlate to cancer. RESULTS Multiple SOS images of breast, knee and segmentations of breast glandular and ductal tissue are shown. Spearman rho is calculated between our volumetric breast density estimates and Volpara™ from mammograms, as 0.9332. Multiple timing results are shown and indicate the variability of the reconstruction times with breast size and type but are ∼30 minutes for average size breast. The timing results with the 3D algorithm indicate ∼60 minute reconstruction times for pediatrics with two Nvidia GPUs. Characteristic variations of the glandular and ductal volumes over time are shown. The SOS from QT images are compared with literature values. The results of a multi-reader multi-case (MRMC) study are shown that compares the 3D UT with full field digital mammography and resulted in an average increase in ROC AUC of 10%. Orthopedic (knee) 3D UT images compared with MRI indicate regions of zero signal in the MRI are clearly displayed in the QT image. Explicit representation of the acoustic field is shown, indicating its 3D nature. An image of in vivo breast with the chest muscle is shown and speed of sound agreement with literature values are tabulated. Reference is made to a recently published paper validating pediatric imaging. CONCLUSIONS The high Spearman rho indicates a monotonic (not necessarily linear) relation between our method and industry gold standard Volpara™ density. The acoustic field verifies the need for 3D modeling. The MRMC study, the orthopedic images, breast density study, and references, all indicate the clinical utility of the SOS and reflection images. The QT image of the knee shows its ability to monitor tissue the MRI cannot. The included references and images herein indicate the proof of concept for 3D UT as a viable and valuable clinical adjunct in pediatric and orthopedic situations in addition to the breast imaging.
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Affiliation(s)
- James Wiskin
- QT Imaging, Inc, 3 Hamilton Landing, Suite 160, CA 94949, USA.
| | - Bilal Malik
- QT Imaging, Inc, 3 Hamilton Landing, Suite 160, CA 94949, USA
| | - John Klock
- QT Imaging, Inc, 3 Hamilton Landing, Suite 160, CA 94949, USA
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Sánchez-Bayuela DÁ, Ghavami N, Tiberi G, Sani L, Vispa A, Bigotti A, Raspa G, Badia M, Papini L, Ghavami M, Castellano CR, Bernardi D, Calabrese M, Tagliafico AS. A multicentric, single arm, prospective, stratified clinical investigation to evaluate MammoWave's ability in breast lesions detection. PLoS One 2023; 18:e0288312. [PMID: 37450545 PMCID: PMC10348515 DOI: 10.1371/journal.pone.0288312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023] Open
Abstract
Microwave imaging is a safe and promising new technology in breast radiology, avoiding discomfort of breast compression and usage of ionizing radiation. This paper presents the first prospective microwave breast imaging study during which both symptomatic and asymptomatic subjects were recruited. Specifically, a prospective multicentre international clinical trial was performed in 2020-2021, to investigate the capability of a microwave imaging device (MammoWave) in allowing distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e., with benign or malignant lesions. Each breast scan was performed with the volunteers lying on a dedicated examination table in a comfortable prone position. MammoWave output was compared to reference standard (i.e., radiologic study obtained within the last month and integrated with histological one if available and deemed necessary by responsible investigator) to classify breasts into NF/WF categories. MammoWave output consists of a selection of microwave images' features (determined prior to trials' start), which allow distinction between NF and WF breasts (using statistical significance p<0.05). 353 women were enrolled in the study (mean age 51 years ± 12 [SD], minimum age 19, maximum age 78); MammoWave data from the first 15 women of each site, all with NF breasts, were used for calibration. Following central assessor evaluation, 111 NF (48 dense) and 272 WF (136 dense) breasts were used for comparison with MammoWave output. 272 WF comprised 182 benign findings and 90 malignant histology-confirmed cancer. A sensitivity of 82.3% was achieved (95%CI: 0.78-0.87); sensitivity is maintained when limiting the investigation to histology-confirmed breasts cancer only (90 histology-confirmed breasts cancer have been included in this analysis, having sizes ranging from 3 mm to 60 mm). Specificity value of approximately 50% was achieved as expected, since thresholds were calculated (for each feature) using median value obtained after recruiting the first 15 women (of each site), all NF. This prospective trial may represent another step for introducing microwave imaging into clinical practice, for helping in breast lesion identification in asymptomatic women.
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Affiliation(s)
- Daniel Álvarez Sánchez-Bayuela
- UBT—Umbria Bioengineering Technologies, Perugia, Italy
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, Spain
- Faculty of Chemical Science and Technology, Instituto Regional de Investigación Científica Aplicada, University of Castilla, La Mancha, Spain
| | - Navid Ghavami
- UBT—Umbria Bioengineering Technologies, Perugia, Italy
| | - Gianluigi Tiberi
- UBT—Umbria Bioengineering Technologies, Perugia, Italy
- School of Engineering, London South Bank University, London, United Kingdom
| | - Lorenzo Sani
- UBT—Umbria Bioengineering Technologies, Perugia, Italy
| | | | | | | | - Mario Badia
- UBT—Umbria Bioengineering Technologies, Perugia, Italy
| | | | - Mohammad Ghavami
- School of Engineering, London South Bank University, London, United Kingdom
| | | | - Daniela Bernardi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Humanitas University, Milan, Italy
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Álvarez Sánchez-Bayuela D, Ghavami N, Romero Castellano C, Bigotti A, Badia M, Papini L, Raspa G, Palomba G, Ghavami M, Loretoni R, Calabrese M, Tagliafico A, Tiberi G. A Multicentric, Single Arm, Prospective, Stratified Clinical Investigation to Confirm MammoWave's Ability in Breast Lesions Detection. Diagnostics (Basel) 2023; 13:2100. [PMID: 37370995 DOI: 10.3390/diagnostics13122100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/06/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, we aim to investigate through a multicentric European clinical trial (ClinicalTrials.gov Identifier NCT05300464) the effectiveness of the MammoWave microwave imaging device, which uses a Huygens-principle-based radar algorithm for image reconstruction and comprises dedicated image analysis software. A detailed clinical protocol has been prepared outlining all aspects of this study, which will involve adult females having a radiologist study output obtained using conventional exams (mammography and/or ultrasound and/or magnetic resonance imaging) within the previous month. A maximum number of 600 volunteers will be recruited at three centres in Italy and Spain, where they will be asked to sign an informed consent form prior to the MammoWave scan. Conductivity weighted microwave images, representing the homogeneity of the tissues' dielectric properties, will be created for each breast, using a conductivity = 0.3 S/m. Subsequently, several microwave image parameters (features) will be used to quantify the images' non-homogenous behaviour. A selection of these features is expected to allow for distinction between breasts with lesions (either benign or malignant) and those without radiological findings. For all the selected features, we will use Welch's t-test to verify the statistical significance, using the gold standard output of the radiological study review.
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Affiliation(s)
- Daniel Álvarez Sánchez-Bayuela
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007 Toledo, Spain
- Faculty of Chemical Science and Technology, Instituto Regional de Investigación Científica Aplicada, University of Castilla-La Mancha, 13001 Ciudad Real, Spain
| | - Navid Ghavami
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy
| | - Cristina Romero Castellano
- Breast Imaging Department, Radiology Service, Complejo Hospitalario Universitario de Toledo, 45007 Toledo, Spain
| | | | - Mario Badia
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy
| | - Lorenzo Papini
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy
| | - Giovanni Raspa
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy
| | | | - Mohammad Ghavami
- School of Engineering, London South Bank University, London SE1 0AA, UK
| | | | | | - Alberto Tagliafico
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
- Department of Health Sciences, University of Genoa, 16126 Genoa, Italy
| | - Gianluigi Tiberi
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy
- School of Engineering, London South Bank University, London SE1 0AA, UK
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11
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Sethi AK, Muddaloor P, Anvekar P, Agarwal J, Mohan A, Singh M, Gopalakrishnan K, Yadav A, Adhikari A, Damani D, Kulkarni K, Aakre CA, Ryu AJ, Iyer VN, Arunachalam SP. Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:5514. [PMID: 37420680 DOI: 10.3390/s23125514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.
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Affiliation(s)
- Arshia K Sethi
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pratyusha Muddaloor
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Joshika Agarwal
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Anmol Mohan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashima Yadav
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Aakriti Adhikari
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Kanchan Kulkarni
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, U1045, F-33000 Bordeaux, France
- IHU Liryc, Heart Rhythm Disease Institute, Fondation Bordeaux Université, F-33600 Pessac, France
| | | | - Alexander J Ryu
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vivek N Iyer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Shivaram P Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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12
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Reimer T, Pistorius S. Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115123. [PMID: 37299852 DOI: 10.3390/s23115123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/12/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023]
Abstract
This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.
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Affiliation(s)
- Tyson Reimer
- Department of Physics & Astronomy, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Stephen Pistorius
- Department of Physics & Astronomy, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- CancerCare Manitoba Research Institute, Winnipeg, MB R3E 0V9, Canada
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13
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Zerrad FE, Taouzari M, Makroum EM, Aoufi JE, Qanadli SD, Karaaslan M, Al-Gburi AJA, Zakaria Z. Microwave Imaging Approach for Breast Cancer Detection Using a Tapered Slot Antenna Loaded with Parasitic Components. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16041496. [PMID: 36837126 PMCID: PMC9960075 DOI: 10.3390/ma16041496] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 05/27/2023]
Abstract
In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < -10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom.
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Affiliation(s)
- Fatima-ezzahra Zerrad
- Laboratory IMII, Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco
| | - Mohamed Taouzari
- Laboratory LISA, National School of Applied Sciences, Hassan First University of Settat, Berrechid 26100, Morocco
- Laboratory of Aeronautical & Telecommunication, Mohammed VI, International Academy of Civil Aviation, Casablanca 20000, Morocco
| | - El Mostafa Makroum
- Laboratory IMII, Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco
| | - Jamal El Aoufi
- Laboratory of Aeronautical & Telecommunication, Mohammed VI, International Academy of Civil Aviation, Casablanca 20000, Morocco
| | - Salah D. Qanadli
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Muharrem Karaaslan
- Electrical-Electronics Engineering, Iskenderun Technical University, 31200 İskenderun, Turkey
| | - Ahmed Jamal Abdullah Al-Gburi
- Center for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tungal, Malacca 76100, Malaysia
| | - Zahriladha Zakaria
- Center for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tungal, Malacca 76100, Malaysia
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14
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Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation. Diagnostics (Basel) 2022; 12:diagnostics12123151. [PMID: 36553158 PMCID: PMC9777022 DOI: 10.3390/diagnostics12123151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/29/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022] Open
Abstract
(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.
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15
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Guerrero Orozco L, Peterson L, Fhager A. Microwave Antenna System for Muscle Rupture Imaging with a Lossy Gel to Reduce Multipath Interference. SENSORS 2022; 22:s22114121. [PMID: 35684742 PMCID: PMC9185596 DOI: 10.3390/s22114121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/13/2022] [Accepted: 05/26/2022] [Indexed: 12/03/2022]
Abstract
Injuries to the hamstring muscles are an increasing problem in sports. Imaging plays a key role in diagnosing and managing athletes with muscle injuries, but there are several problems with conventional imaging modalities with respect to cost and availability. We hypothesized that microwave imaging could provide improved availability and lower costs and lead to improved and more accurate diagnostics. In this paper, a semicircular microwave imaging array with eight antennae was investigated. A key component in this system is the novel antenna design, which is based on a monopole antenna and a lossy gel. The purpose of the gel is to reduce the effects of multipath signals and improve the imaging quality. Several different gels have been manufactured and evaluated in imaging experiments. For comparison, corresponding simulations were performed. The results showed that the gels can effectively reduce the multipath signals and the imaging experiments resulted in significantly more stable and repeatable reconstructions when a lossy gel was used compared to when an almost non-lossy gel was used.
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Affiliation(s)
- Laura Guerrero Orozco
- Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden;
- MedTech West, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
- Correspondence:
| | - Lars Peterson
- Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden;
| | - Andreas Fhager
- Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden;
- MedTech West, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
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16
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Alkhodari M, Zakaria A, Qaddoumi N. Using prior information to enhance microwave tomography images in bone health assessment. Biomed Eng Online 2022; 21:8. [PMID: 35109851 PMCID: PMC8812250 DOI: 10.1186/s12938-021-00966-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 11/29/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Osteoporosis is the major cause of bone weakness and fragility in more than 10 million people in the United States. This disease causes bone fractures in the hip or spine, which result in increasing the risk of disabilities or even death. The current gold standard in osteoporosis diagnostics, X-ray, although reliable, it uses ionizing radiations that makes it unfeasible for early and continuous monitoring applications. Recently, microwave tomography (MWT) has been emerging as a biomedical imaging modality that utilizes non-ionizing electromagnetic signals to screen bones' electrical properties. These properties are highly correlated to bones' density, which makes MWT to be an effective and safe alternative for frequent testing in osteoporosis diagnostics. RESULTS Both the conventional and wearable simulated systems were successful in localizing the tibia and fibula bones in the enhanced MWT images. Furthermore, structure extraction of the leg's model from the blind MWT images had a minimal error compared to the original one (L2-norm: 15.60%). Under five sequentially incremental bone volume fraction (BVF) scenarios simulating bones' treatment procedure, bones were detected successfully and their densities were found to be inversely proportional to the real part of the relative permittivity values. CONCLUSIONS This study paves the way towards implementing a safe and user-friendly MWT system that can be wearable to monitor bone degradation or treatment for osteoporosis cases. METHODS An anatomically realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues' (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method-contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Finally, the enhanced images were used to monitor variations in BVF.
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Affiliation(s)
- Mohanad Alkhodari
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates. .,Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
| | - Amer Zakaria
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
| | - Nasser Qaddoumi
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
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17
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Adachi M, Nakagawa T, Fujioka T, Mori M, Kubota K, Oda G, Kikkawa T. Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection. Diagnostics (Basel) 2021; 12:diagnostics12010027. [PMID: 35054193 PMCID: PMC8774784 DOI: 10.3390/diagnostics12010027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting breast cancer.
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Affiliation(s)
- Mio Adachi
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.A.); (G.O.)
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.A.); (G.O.)
- Correspondence:
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (M.M.); (K.K.)
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (M.M.); (K.K.)
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (M.M.); (K.K.)
- Department of Radiology, Dokkyo Medical University, Tochigi 321-0293, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.A.); (G.O.)
| | - Takamaro Kikkawa
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima 739-8527, Japan;
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18
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Moloney BM, McAnena PF, Elwahab SM, Fasoula A, Duchesne L, Gil Cano JD, Glynn C, O'Connell A, Ennis R, Lowery AJ, Kerin MJ. The Wavelia Microwave Breast Imaging system-tumour discriminating features and their clinical usefulness. Br J Radiol 2021; 94:20210907. [PMID: 34581186 PMCID: PMC8631021 DOI: 10.1259/bjr.20210907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE The Wavelia Microwave Breast Imaging (MBI) system, based on non-ionising imaging technology, has demonstrated exciting potential in the detection and localisation of breast pathology in symptomatic patients. In this study, the ability of the system to accurately estimate the size and likelihood of malignancy of breast lesions is detailed, and its clinical usefulness determined. METHODS Institutional review board and Health Products Regulatory Authority (HPRA) approval were obtained. Patients were recruited from the symptomatic unit to three groups; breast cancer (Group-1), unaspirated cysts (Group-2) and biopsied benign lesions (Group-3). MBI, radiological and histopathological findings were reviewed. MBI size estimations were compared with the sizes determined by conventional imaging and histopathology. A Quadratic Discriminant Analysis (QDA) classifier was trained in a 3D feature space to discriminate malignant from benign lesions. An independent review was performed by two independent breast radiologists. RESULTS 24 patients (11 Group-1, 8 Group-2 and 5 Group-3) underwent MBI. The Wavelia system was more accurate than conventional imaging in size estimation of breast cancers. The QDA accurately separated benign from malignant breast lesions in 88.5% of cases. The addition of MBI and the Wavelia malignancy risk calculation was deemed useful by the two radiologists in 70.6% of cases. CONCLUSION The results from this MBI investigation demonstrate the potential of this novel system in estimating size and malignancy risk of breast lesions. This system holds significant promise as a potential non-invasive, comfortable, and harmless adjunct for breast cancer diagnosis. Further larger studies are under preparation to validate the findings of this study. ADVANCES IN KNOWLEDGE This study details the potential of the Wavelia MBI system in delineating size and malignancy risk of benign and malignant breast lesions in a symptomatic cohort. The usefulness of the Wavelia system is assessed in the clinical setting.
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Affiliation(s)
- Brian M Moloney
- Discipline of Surgery, Lambe Institute for Translational Research, School of Medicine, National University of Ireland Galway, Galway, Ireland.,Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, Toronto, Canada
| | - Peter F McAnena
- Discipline of Surgery, Lambe Institute for Translational Research, School of Medicine, National University of Ireland Galway, Galway, Ireland.,Department of Surgery, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | - Sami M Elwahab
- Department of Surgery, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | | | | | | | - Catherine Glynn
- Department of Radiology, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | - AnnaMarie O'Connell
- Department of Radiology, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | - Rachel Ennis
- Department of Radiology, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | - Aoife J Lowery
- Discipline of Surgery, Lambe Institute for Translational Research, School of Medicine, National University of Ireland Galway, Galway, Ireland.,Department of Surgery, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
| | - Michael J Kerin
- Discipline of Surgery, Lambe Institute for Translational Research, School of Medicine, National University of Ireland Galway, Galway, Ireland.,Department of Surgery, Galway University Hospital, Saolta University Healthcare Group, Galway, Ireland
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