Elasticity mapping for breast cancer diagnosis using tactile imaging and auxiliary sensor fusion

Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works.Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works

Towards robust 3D registration of non-invasive tactile elasticity images of breast tissue for cost-effective cancer screening

This paper presents current progress on the development of Tactile Imaging, a developing technology for breast cancer screening finding traction in the marketplace, towards non-invasive fully 3D elasticity imaging of the breast. The paper identifies the necessary steps required, and subsequent progress, to develop the technology to image the whole breast robustly which is to be used as a safe screening tool in walk-in clinics. Tactile Imaging has been shown to be capable of binary lesion classification and has seen extensive development, to where benign biopsy rates could be reduced by 23%, but further work is required to make this a clinically practical system for widespread use. Using a hybrid system of Tactile, orientation, and camera sensors it has been demonstrated that robust composite tactile image mosaicking is feasible using the breast vein network as a base map. This paper further outlines the remaining steps needed to turn the current state-of-the-art system from a 2D demonstrator into a fully 3D imaging system that is competitive with other imaging methods, and associated challenges. These being chiefly preparing a phantom reference structure for use in pre-clinical validation, making more stable tactile sensors to reliably perform the new imaging techniques, and building bodies of evidence to build clinical trust in tactile imaging. This work describes that 3D tactile breast imaging is feasible, but that additional work is required to clinically demonstrate these new developments

Tactile, orientation, and optical sensor fusion for tactile breast image mosaicking

Breast cancer screening using Tactile Imaging (TI) is an advancing field of low-cost non-invasive medical imaging. Utilizing arrays of capacitive pressure transducers to perform a differential stress measurement of suspicious tissue, TI has been shown to be effective in measuring lesion size and stiffness, and subsequent differentiation of malignant and benign conditions, in repeated clinical studies. In order to further improve the lesion classification accuracy of clinical TI, this paper presents a novel method of mosaicking tactile images to form a large composite tactile map using the vein structure within the breast to spatially register tactile data. This paper demonstrates practical non-rigid tactile image mosaicking, using probe contact force and relative orientation sensor fusion to correct for the tissue deformation during tactile scanning, miniaturized and applied to a pre-clinical TI prototype. Testing of the proposed TI prototype on representative, tissue-mimicking, silicone breast phantoms, with varying baseline elasticity and internal vein structure, yields typical image registration accuracies of 0.33% ± 0.15%. In similar testing, the proposed system measures the background elasticity of the samples with worst case error < 4.5% over the range 9 kPa to 60 kPa, required for accurate lesion characterization. This work will lead into further clinical validation of TI for measurement and classification of in-situ phantom and breast lesions, utilizing the delivered metrics from this work to improve differentiation accuracy

Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient's skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.Comment: Submitted to 2021 International Symposium on Medical Robotics (ISMR

Focal Spot, Fall/Winter 2003/2004

https://digitalcommons.wustl.edu/focal_spot_archives/1095/thumbnail.jp

Computer-aided detection in musculoskeletal projection radiography: A systematic review

This is the author accepted manuscript. The final version is available from WB Saunders via the DOI in this record.Objectives To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Key findings Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. Conclusion The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems

Systematic reviews as a “lens of evidence”: determinants of cost-effectiveness of breast cancer screening

Systematic reviews with economic components are important decision tools for stakeholders seeking to evaluate technologies, such as breast cancer screening (BCS) programs. This overview of systematic reviews explores the determinants of the cost‐effectiveness of BCS and assesses the quality of secondary evidence. The search identified 30 systematic reviews that reported on the determinants of the cost‐effectiveness of BCS, including the costs of breast cancer and BCS. While the quality of the reviews varied widely, only four out of 30 papers were considered to be of a high quality. We did not identify publication bias in the original evidence on the cost‐effectiveness of mammography screening; however, we highlight a need for improved clarity in both reporting and data verification. The reviews consisted mainly of studies from high‐income countries. Breast cancer costs varied widely among the studies. Factors leading to higher costs included: time (diagnosis and last months before death), later stage or metastases, recurrence of the disease, age below 64 years and type of follow‐up (more intensive or more specialized). Overall, screening with mammography was considered cost‐effective in the age range 50‐69 years in Western European and Northern American countries but not for older or younger women. Its cost‐effectiveness was questionable for low‐income settings and Asia. Mammography screening was more cost‐effective with biennial screening compared to annual screening and single reading using computer‐aided detection vs double reading. No information on the cost‐effectiveness of ultrasonography was found, and there is much uncertainty on the cost‐effectiveness of CBE because of methodological limitations

A Stepwise Compression-Relaxation Testing Method for Tissue Characterization and Tumor Detection Via a Two-Dimensional Tactile Sensor

This dissertation presents a stepwise compression-relaxation (SCR) testing method built upon a two-dimensional (2D) tactile sensor for mechanical characterization of soft tissues and tumor detection. The core of the 2D sensor entails one whole polydimethylsiloxane (PDMS) microstructure embedded with a 3×3 sensing-plate/transducer array. A soft sample was compressed by the 2D sensor with a step incremental depth at a ramp speed, and then relaxed for certain hold time. When a soft sample was compressed by the 2D sensor, the sensing-plates translated the sample response at different tissue sites to the sensor deflections, which were registered as resistance changes by the transducer array. Instant elasticity (Einstant) and loss factor (tan δ) extracted from the measured data were used to quantify the sample elasticity and viscoelasticity, respectively. First, a three-way ANOVA analysis was conducted on the data of soft materials (PDMS/silicone rubbers) to evaluate the influence of testing parameters (incremental depth, hold time, and ramp speed) on the measured results. The results revealed that both Einstant and tan δ were significantly dependent on testing parameters. Next, the measured results on the soft tissues showed different elasticity and viscoelasticity between muscle tissues and fat/skin tissues. The measured results on the tumor tissues indicated different elasticity and viscoelasticity among the five breast tumor (BT) tissues, and between the two pancreatic tumor (PT) tissues before and after treatment. Due to the larger sample size of the BT tissues, the elasticity distribution among the measure BT tissue sites was used to determine the location, shape and size of the tumor in a BT tissue. The correlation of stress drop (Δσ) (obtained from the difference between the instant and relaxed sensor deflections at each step incremental depth) with the applied strain (ε) was used for tumor detection. Pearson correlation analysis was conducted to quantitatively analyze the measured Δσ-ε relation as slope of stress drop versus applied strain (m=Δσ/ε) and coefficient of determination (R2) as a measure of the goodness of fit of the linear regression for distinguishing tumor tissue from normal tissue. The measured results on soft materials showed that m was significantly dependent on testing parameters, but R2 showed no significant dependency on testing parameters. The measured results on the tumor tissues indicated R2 was significantly varied among the center, edge and outside sites of the BT tissues. However, no difference was found between the BT outside sites and the normal tissues. R2 also revealed significant difference between before and after treatment of the PT tissues, while no difference between the PT tissues after treatment and the normal tissues. R2 of the PT tissues before treatment was significantly different from that of the BT center sites, but m failed to capture their difference. Furthermore, dummy tumors made of silicone rubbers were found to behave differently from the native tumors. In summary, the feasibility of the SCR testing method for tissue characterization and tumor detection was experimentally validated on the measured soft samples, including PDMS, silicone rubbers, porcine and bovine normal tissues, mouse BT and PT tissues. Future work will investigate the feasibility of the SCR testing method for differentiation between benign tumors and malignant tumors

A Patient-Specific Approach for Breast Cancer Detection and Tumor Localization Using Infrared Imaging

Breast cancer (BC) is the most common cancer among women in the United States; approximately one out of every 24 women die of related causes. BC screening is a critical factor for improving patient prognosis and survival rate. Infrared (IR) thermography is an accurate, inexpensive and operator independent modality that is not affected by tissue density as it captures surface temperature variations induced by the presence of tumors. A novel patient-specific approach for IR imaging and simulation is proposed. In this work, multi-view IR images of isolated breasts are obtained in the prone position (face down), which allows access to the entire breast surface because the breasts hang freely. The challenge of accurately determining size and location of tumors within the breasts is addressed through numerical simulations of a patient-specific digital breast model. The digital breast models for individual patients are created from clinical images of the breast, such as IR imaging, digital photographs or magnetic resonance images. The numerical simulations of the digital breast model are conducted using ANSYS Fluent, where computed temperature images are generated in the same corresponding views as clinical IRI images. The computed and clinical IRI images are aligned and compared to measure their match. The determination of tumor size and location was conducted through the Levenberg-Marquardt algorithm, which iteratively minimized the mean squared error. The methodology was tested on the breasts of seven patients with biopsy-proven breast cancer with tumor diameters ranging from 8 mm to 27 mm. The method successfully predicted the equivalent tumor diameter within 2 mm and the location was predicted within 6.3 mm in all cases. The time required for the estimation is 48 minutes using a 10-core, 3.41 GHz workstation. The method presented is accurate, fast and has potential to be used as an adjunct modality to mammography in BC screening, especially for dense breasts