Mr. Finn Patrick Krewer Profile

Finn Patrick Krewer

PhD Researcher

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Publications (2)

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Proceedings Article | 29 May 2014 Paper

Development of anatomically and dielectrically accurate breast phantoms for microwave imaging applications

M. O'Halloran, S. Lohfeld, G. Ruvio, J. Browne, F. Krewer, C. Ribeiro, V. Inacio Pita, R. Conceicao, E. Jones, M. Glavin

Proceedings Volume 9077, 90770Y (2014) https://doi.org/10.1117/12.2049853

KEYWORDS: Breast, Tissues, Dielectrics, Skin, Microwave imaging, Microwave radiation, Breast cancer, Breast imaging, Mammography, X-rays

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Breast cancer is one of the most common cancers in women. In the United States alone, it accounts for 31% of new cancer cases, and is second only to lung cancer as the leading cause of deaths in American women. More than 184,000 new cases of breast cancer are diagnosed each year resulting in approximately 41,000 deaths. Early detection and intervention is one of the most significant factors in improving the survival rates and quality of life experienced by breast cancer sufferers, since this is the time when treatment is most effective. One of the most promising breast imaging modalities is microwave imaging. The physical basis of active microwave imaging is the dielectric contrast between normal and malignant breast tissue that exists at microwave frequencies. The dielectric contrast is mainly due to the increased water content present in the cancerous tissue. Microwave imaging is non-ionizing, does not require breast compression, is less invasive than X-ray mammography, and is potentially low cost. While several prototype microwave breast imaging systems are currently in various stages of development, the design and fabrication of anatomically and dielectrically representative breast phantoms to evaluate these systems is often problematic. While some existing phantoms are composed of dielectrically representative materials, they rarely accurately represent the shape and size of a typical breast. Conversely, several phantoms have been developed to accurately model the shape of the human breast, but have inappropriate dielectric properties. This study will brie y review existing phantoms before describing the development of a more accurate and practical breast phantom for the evaluation of microwave breast imaging systems.

Proceedings Article | 29 May 2014 Paper

Development of a wearable microwave bladder monitor for the management and treatment of urinary incontinence

F. Krewer, F. Morgan, E. Jones, M. Glavin, M. O'Halloran

Proceedings Volume 9077, 90770X (2014) https://doi.org/10.1117/12.2049689

KEYWORDS: Bladder, Antennas, Tissues, Intestine, Signal to noise ratio, Dielectrics, Finite-difference time-domain method, Bone, Principal component analysis, 3D modeling

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Urinary incontinence is defined as the inability to stop the flow of urine from the bladder. In the US alone, the annual societal cost of incontinence-related care is estimated at 12.6 billion dollars. Clinicians agree that those suffering from urinary incontinence would greatly benefit from a wearable system that could continually monitor the bladder, providing continuous feedback to the patient. While existing ultrasound-based solutions are highly accurate, they are severely limited by form-factor, battery size, cost and ease of use. In this study the authors propose an alternative bladder-state sensing system, based on Ultra Wideband (UWB) Radar. As part of an initial proof-of-concept, the authors developed one of the first dielectrically and anatomically-representative Finite Difference Time Domain models of the pelvis. These models (one male and one female) are derived from Magnetic Resonance images provided by the IT'IS Foundation. These IT'IS models provide the foundation upon which an anatomically-plausible bladder growth model was constructed. The authors employed accurate multi-pole Debye models to simulate the dielectric properties of each of the pelvic tissues. Two-dimensional Finite Difference Time Domain (FDTD) simulations were completed for a range of bladder volumes. Relevant features were extracted from the FDTD-derived signals using Principle Component Analysis (PCA) and then classified using a k-Nearest-Neighbour and Support Vector Machine algorithms (incorporating the Leave-one-out cross-validation approach). Additionally the authors investigated the effects of signal fidelity, noise and antenna movement relative to the target as potential sources of error. The results of this initial study provide strong motivation for further research into this timely application, particularly in the context of an ageing population.

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