Françoise Jeanette Siepel

Controlling the Stormram 2: An MRI-compatible robotic system for breast biopsy

Breast cancer is the most frequently life-threatening diagnosed type of cancer among women. Early and accurate diagnosis by acquiring a tissue sample using biopsy techniques is essential. However, small lesions only visible by MRI are often missed in standard methods, indicating the need for a robotic-assisted biopsy system that is MRI-compatible. Existing proof-of-concepts are difficult to employ due to large sizes and/or actuation complexities. Therefore, a compact pneumatically-actuated 5 DOF MRI-compatible robot was further developed and controlled by a computerized valve manifold. Accuracy and efficiency measurements have been performed using two different PVC breast phantoms with embedded fish oil capsules (mimicking lesions) inside a 0.25T MRI scanner. Preliminary results show that the end-effector was able to hit the targeted capsules, and that the position accuracy is in the range of 4.7–7.3 mm. The developed robotic system has potential to perform MRI-guided breast biopsies accurately and improve the clinical workflow.

Design and characterization of Stormram 4: An MRI-compatible robotic system for breast biopsy

Targeting of small lesions with high precision is essential in an early phase of breast cancer for diagnosis and accurate follow up, and subsequently determines prognosis. Current techniques to diagnose breast cancer are suboptimal, and there is a need for a small, MRI-compatible robotic system able to target lesions with high precision and direct feedback of MRI. Therefore, the design and working mechanism of the new Stormram 4, an MRI-compatible needle manipulator with four degrees of freedom, will be presented to take biopsies of small lesions in the MRI scanner. Its dimensions (excluding racks and needle) are 72×51×40 mm, and the system is driven by two linear and two curved pneumatic stepper motors. The T-26 linear motor measures 26×21×16 mm, has a nominal step size of 0.25 mm and the measured maximum force is 63N at 0.65 MPa. The workspace has a total volume of 2.2 L. Accuracy measurements have shown that the mean positioning error is 0.7 mm, with a reproducibility of 0.1 mm. Velocity measurements with 5 m long tubes show a maximum stepping frequency of 8 Hz (maximum force) to 30 Hz (unloaded). These results show that the robot might be able to target lesions with sub-millimeter accuracy within reasonable time for the MRI-guided breast biopsy procedure.

Stormram 3: A Magnetic Resonance Imaging-Compatible Robotic System for Breast Biopsy

Stormram 3 is a magnetic resonance imaging (MRI)-compatible robotic system that can perform MR-guided breast biopsies of suspicious lesions. The base of the robot measures 160 # 180 # 90 mm, and it is actuated by five custom pneumatic linear stepper motors, driven by a valve manifold outside the Faraday cage of the MRI scanner. All parts can be rapidly prototyped with three-dimensional (3-D) printing or laser cutting, making the design suitable for other applications, such as actuation in hazardous environments. Based on the choice of materials, the robot (with the exception of the needle) is inherently MR safe. Measurements show that the maximum force of the T-49 actuator is 70 N, at a pressure of 0.3 MPa. The Stormram 3 has an optimized repeatability that is lower than 0.5 mm, and it can achieve a positional accuracy on the order of 2 mm.

Analytical derivation of elasticity in breast phantoms for deformation tracking

PurposePatient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms.MethodsAn analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages.ResultsApplication of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods.ConclusionIt can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.

MRI and stereo vision surface reconstruction and fusion

Breast cancer, the most commonly diagnosed cancer in women worldwide, is mostly detected through a biopsy where tissue is extracted and chemically examined or pathologist assessed. Medical imaging plays a valuable role in targeting malignant tissue accurately and guiding the radiologist during needle insertion in a biopsy. This paper proposes a computer software that can process and combine 3D reconstructed surfaces from different imaging modalities, particularly Magnetic Resonance Imaging (MRI) and camera, showing a visualization of important features and investigates its feasibility. The development of this software aims to combine the detectability of MRI with the physical space of the camera. It demonstrates that the registration accuracy of the proposed system is acceptable and has potential for clinical application.