Ultrasound phantom with solids mimicking cancerous tissue for needle breast biopsy

This study aimed at synthesizing hydrogels to simulate opaque breast tissue (BT) and coloured cancerous tissues (CT) at different densities of the designed phantom to improve the biopsy-related skills along with ultrasonography. Both tissues are tear-resistant and therefore, the phantom can be trained multiple times in order to lower the price and improve the eye-hand coordination of users. For this purpose, self-healing (SH) polyacrylamide (PAAm) hydrogels (SH hydrogel) obtained by free-radical polymerization of AAm, in the presence of chemical cross-linker, BAAm, physical cross-linker stearyl methacrylate, C18, and ammonium persulfate APS as initiator were used in the design of phantoms. Psyllium was added to the BT to differentiate density and obtain human skin color and it could be distinguished from the CT which was also colored with methyl violet. BT and CTs were characterized with FTIR spectroscopy, mechanical, swelling, and refractive index measurements. Designing phantoms from BT and CT were characterized by ultrasonography, mechanical tests, observation of needle track after biopsy, and stabilization tests to follow the self-healing behaviours of tissues with time. As a result of this study, self-healing, low-cost, and suitable for multi-usage ultrasonographic phantom for needle breast biopsy was designed and cancerous tissue was successfully detected.[214S357]Acknowledgements We thank T?B?TAK, which supported our work with project number of 214S357. Many thanks to Dentist Dr. Hayri Bingeli for their help with molding the hydrogels

On uncertainty propagation in image-guided renal navigation: Exploring uncertainty reduction techniques through simulation and in vitro phantom evaluation

Image-guided interventions (IGIs) entail the use of imaging to augment or replace direct vision during therapeutic interventions, with the overall goal is to provide effective treatment in a less invasive manner, as an alternative to traditional open surgery, while reducing patient trauma and shortening the recovery time post-procedure. IGIs rely on pre-operative images, surgical tracking and localization systems, and intra-operative images to provide correct views of the surgical scene. Pre-operative images are used to generate patient-specific anatomical models that are then registered to the patient using the surgical tracking system, and often complemented with real-time, intra-operative images. IGI systems are subject to uncertainty from several sources, including surgical instrument tracking / localization uncertainty, model-to-patient registration uncertainty, user-induced navigation uncertainty, as well as the uncertainty associated with the calibration of various surgical instruments and intra-operative imaging devices (i.e., laparoscopic camera) instrumented with surgical tracking sensors. All these uncertainties impact the overall targeting accuracy, which represents the error associated with the navigation of a surgical instrument to a specific target to be treated under image guidance provided by the IGI system. Therefore, understanding the overall uncertainty of an IGI system is paramount to the overall outcome of the intervention, as procedure success entails achieving certain accuracy tolerances specific to individual procedures. This work has focused on studying the navigation uncertainty, along with techniques to reduce uncertainty, for an IGI platform dedicated to image-guided renal interventions. We constructed life-size replica patient-specific kidney models from pre-operative images using 3D printing and tissue emulating materials and conducted experiments to characterize the uncertainty of both optical and electromagnetic surgical tracking systems, the uncertainty associated with the virtual model-to-physical phantom registration, as well as the uncertainty associated with live augmented reality (AR) views of the surgical scene achieved by enhancing the pre-procedural model and tracked surgical instrument views with live video views acquires using a camera tracked in real time. To better understand the effects of the tracked instrument calibration, registration fiducial configuration, and tracked camera calibration on the overall navigation uncertainty, we conducted Monte Carlo simulations that enabled us to identify optimal configurations that were subsequently validated experimentally using patient-specific phantoms in the laboratory. To mitigate the inherent accuracy limitations associated with the pre-procedural model-to-patient registration and their effect on the overall navigation, we also demonstrated the use of tracked video imaging to update the registration, enabling us to restore targeting accuracy to within its acceptable range. Lastly, we conducted several validation experiments using patient-specific kidney emulating phantoms using post-procedure CT imaging as reference ground truth to assess the accuracy of AR-guided navigation in the context of in vitro renal interventions. This work helped find answers to key questions about uncertainty propagation in image-guided renal interventions and led to the development of key techniques and tools to help reduce optimize the overall navigation / targeting uncertainty

Tissue mimicking materials for imaging and therapy phantoms: a review

Tissue mimicking materials (TMMs), typically contained within phantoms, have been used for many decades in both imaging and therapeutic applications. This review investigates the specifications that are typically being used in development of the latest TMMs. The imaging modalities that have been investigated focus around CT, mammography, SPECT, PET, MRI and ultrasound. Therapeutic applications discussed within the review include radiotherapy, thermal therapy and surgical applications. A number of modalities were not reviewed including optical spectroscopy, optical imaging and planar x-rays. The emergence of image guided interventions and multimodality imaging have placed an increasing demand on the number of specifications on the latest TMMs. Material specification standards are available in some imaging areas such as ultrasound. It is recommended that this should be replicated for other imaging and therapeutic modalities. Materials used within phantoms have been reviewed for a series of imaging and therapeutic applications with the potential to become a testbed for cross-fertilization of materials across modalities. Deformation, texture, multimodality imaging and perfusion are common themes that are currently under development

Theoretical and Experimental Tools for Clinical Translation of Quantitative Tissue Optical Sensing.

Quantitative tissue optical spectroscopy has been considered as a promising method for clinical diagnosis, owing to its ability to non-invasively give an objective assessment of biological tissues at cellular and sub-cellular levels. In spite of recent advances in optics and the computational power, not many quantitative tissue optical sensing technologies have been translated into clinical practice. In order to translate this technology in the clinics, we need to further improve the technology. To name a few, we need accurate and rapid quantification method for a real-time diagnostic feedback. Next, we need computational methods for complex tissue-optics problems. Also, we need a novel approach in probe design for the inaccessible organs. This dissertation focuses on the development, verification and validation of theoretical (mathematical and computational) and experimental (instrumental) tool set to promote the translation of quantitative tissue optical spectroscopy into clinical diagnostic applications. For the mathematical tool, a direct-fit photon tissue interaction (DF-PTI) model that could rapidly and accurately extract the parameters associated biophysical features was developed and validated to characterize the precursor lesions of pancreatic cancer. A rapid scattering model on pancreatic tissue reflectance based on principal components analysis (PCA) results was also developed. The diagnostic capability of scattering properties obtained was demonstrated on an 18-patient data set using a rigorous statistical method, which implied the potential of reflectance spectroscopy for real-time detection of pancreatic cancer. For the computational tool, a ray-traced Monte Carlo (RTMC) simulation for the design of fluorescence spectroscopy or imaging system utilizing complex optics to probe turbid biological tissues was devised. This new method was verified computationally with epithelial tissue models and experimentally using tissue-simulating optical phantoms. For the instrumental tool, the design and development of minimally-invasive diagnostic technologies employing optoelectronic components were discussed. In this dissertation, we focused on detection of pancreatic cancer, which has the worst prognosis among other major cancers. Pancreatic tissues were employed as our model system to validate our developed tools. The developed technology and tools can be applied to a variety of other human tissue sites to help in the translation of quantitative tissue optical sensing in a clinical setting.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111401/1/paulslee_1.pd

Patient-Specific Polyvinyl Alcohol Phantoms for Applications in Minimally Invasive Surgery

In biomedical engineering, phantoms are physical models of known geometric and material composition that are used to replicate biological tissues. Phantoms are vital tools in the testing and development of novel minimally invasive devices, as they can simulate the conditions in which devices will be used. Clinically, phantoms are also highly useful as training tools for minimally invasive procedures, such as those performed in regional anaesthesia, and for patient-specific surgical planning. Despite their widespread utility, there are many limitations with current phantoms and their fabrication methods. Commercial phantoms are often prohibitively expensive and may not be compatible with certain imaging modalities, such as ultrasound. Much of the phantom literature is complicated or hard to follow, making it difficult for researchers to produce their own models and it is highly challenging to create anatomically realistic phantoms that replicate real patient pathologies. Therefore, the aim of this work is to address some of the challenges with current phantoms. Novel fabrication methods and frameworks are presented to enable the creation of phantoms that are suitable for use in both the development of novel devices and as clinical training tools, for applications in minimally invasive surgery. This includes regional anaesthesia, brain tumour resection, and percutaneous coronary interventions. In such procedures, imaging is of key importance, and the phantoms developed are demonstrated to be compatible across a range of modalities, including ultrasound, computed tomography, MRI, and photoacoustic imaging

Translation of Intravascular Optical Ultrasound Imaging

ances in the field of intravascular imaging have provided clinicians with power ful tools to aid in the assessment and treatment of vascular pathology. Optical Ultra sound (OpUS) is an emerging modality with the potential to offer significant bene fits over existing commercial technologies such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT). With this paradigm ultrasound (US) is generated using pulsed or modulated light and received by a miniaturised fibre-optic hydrophone (FOH). The US generation is facilitated through the use of engineered optically-absorbing nanocomposite materials. To date pre-clinical benchtop stud ies of OpUS have shown significant promise however further study is needed to facilitate clinical translation. The overall aim of this PhD was to develop a pathway to clinical translation of OpUS, enabled by the development of a catheter-based device capable of high resolution vascular tissue imaging during an in-vivo setting. A forward-viewing OpUS imaging probe was developed using a 400 µm mul timode optical fibre, dip-coated in a multi-walled carbon nanotube-PDMS com posite, paired with a FOH comprising a 125 µm single mode fibre tipped with a Fabry-Perot cavity. With this high US pressures were generated (21.5 MPa at the transducer surface) and broad corresponding bandwidths were achieved (−6 dB of 39.8MHz). Using this probe, OpUS imaging was performed of an ex-vivo human coronary artery. The results demonstrated excellent correspondence, in the detec tion of calcification and lipid infiltration, with IVUS, OCT and histological analysis. A side-viewing OpUS imaging probe, employing a reflective 45 °angle at the dis tal fibre surface, was used to demonstrate rotational B-mode imaging of a vascular structure for the first time. This provided high-resolution imaging (54 µm axial resolution) with deep depth penetration (>10.5 mm). Finally the clinical utility of this technology was demonstrated during an in-vivo endovascular procedure. An OpUS imaging probe, incorporated into an interventional device, allowed guidance of in-situ fenestration of an endograft during a complex abdominal aortic aneurysm repair. Through this work the potential clinical utility of OpUS, to assess pathology and guide vascular intervention, has been demonstrated. These results pave the way for translation of this technology and a first in man study

Quantitative micro-elastography: imaging of tissue elasticity using compression optical coherence elastography

Probing the mechanical properties of tissue on the microscale could aid in the identification of diseased tissues that are inadequately detected using palpation or current clinical imaging modalities, with potential to guide medical procedures such as the excision of breast tumours. Compression optical coherence elastography (OCE) maps tissue strain with microscale spatial resolution and can delineate microstructural features within breast tissues. However, without a measure of the locally applied stress, strain provides only a qualitative indication of mechanical properties. To overcome this limitation, we present quantitative micro-elastography, which combines compression OCE with a compliant stress sensor to image tissue elasticity. The sensor consists of a layer of translucent silicone with well-characterized stress-strain behaviour. The measured strain in the sensor is used to estimate the two-dimensional stress distribution applied to the sample surface. Elasticity is determined by dividing the stress by the strain in the sample. We show that quantification of elasticity can improve the ability of compression OCE to distinguish between tissues, thereby extending the potential for inter-sample comparison and longitudinal studies of tissue elasticity. We validate the technique using tissue-mimicking phantoms and demonstrate the ability to map elasticity of freshly excised malignant and benign human breast tissues.Kelsey M. Kennedy, Lixin Chin, Robert A. McLaughlin, Bruce Latham, Christobel M. Saunders, David D. Sampson and Brendan F. Kenned

Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation–maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 (±0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods

Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets

Photoacoustic imaging has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included the generation of semi-synthetic training datasets combining both simulated data to represent features from the needles and in vivo measurements for tissue background. Evaluation of the trained neural network was performed with needle insertions into blood-vessel-mimicking phantoms, pork joint tissue ex vivo and measurements on human volunteers. This deep learning-based framework substantially improved the needle visibility in photoacoustic imaging in vivo compared to conventional reconstruction by suppressing background noise and image artefacts, achieving 5.8 and 4.5 times improvements in terms of signal-to-noise ratio and the modified Hausdorff distance, respectively. Thus, the proposed framework could be helpful for reducing complications during percutaneous needle insertions by accurate identification of clinical needles in photoacoustic imaging