Accurately targeting multi-adjacent points (MAPs) during image-guided percutaneous procedures is challenging due to needle deflection and misalignment. The associated errors can result in inadequate treatment of cancer in the case of prostate brachytherapy, or inaccurate diagnosis during biopsy, while repeated insertions increase procedure time, radiation dose, and complications. To address these challenges, we present an image-guided robotic system capable of MAP targeting of irregularly shaped volumes after a single insertion of a percutaneous instrument. The design of the compact CT-compatible drive mechanism is based on a nested screw and screw-spline combination that actuates a straight outer cannula and a curved inner stylet that can be repeatedly straightened when retracted inside the cannula. The stylet translation and cannula rotation/translation enable a 3-D workspace to be reached with the stylet's tip. A closed-form inverse kinematics and image-to-robot registration are implemented in an image-guided system including a point-and-click user interface. The complete system is successfully evaluated with a phantom under a Siemens Definition Flash CT scanner. We demonstrate that the system is capable of MAP targeting for a 2-D shape of the letter “H” and a 3-D helical pattern with an average targeting error of 2.41 mm. These results highlight the benefit and efficacy of the proposed robotic system in seed placement during image-guided brachytherapy.

MRI-guided percutaneous liver interventions have been investigated by researchers as an alternative to CT-guided procedures as it is non-invasive and provides greater soft tissue details. In practice, however, repeated needle insertion is still required to reach desired positions on trial-and-error basis. To minimize the needle attempt and procedural time, we designed a robotic needle guidance device that provides needle insertion angle guidance at skin entry using two rotational joints structured for remote-center-of-motion manipulation. To evaluate the mechanism and clinical feasibility, we fabricated a proof-of-concept prototype that can be manually operated. As preliminary design evaluation, we conducted a retrospective clinical study of 13 MRI-guided abdominal biopsies to determine if the proposed mechanism and device can provide necessary needle insertion angles in MRI-guided liver biopsy procedures. The number of needle insertion attempts per biopsy was also measured. To confirm the kinematic design of the double ring remote-center-of-motion mechanism and to identify any procedural difficulties, we conducted a phantom targeting experiment. The retrospective clinical study showed that the 80 degree insertion angle coverage of the device is sufficient for clinical cases, and an average of five needle insertion attempts per biopsy in conventional MRI-guided biopsy can be reduced by the proposed device. A phantom targeting experiment confirmed that the unique kinematic design was successfully implementation in the targeting.

This paper addresses the use of robotic tissue manipulation in medical needle insertion procedures to improve targeting accuracy and to help avoid damaging sensitive tissues. To control these multiple, potentially competing objectives, we present a phased controller that operates one manipulator at a time using closed-loop imaging feedback. We present an automated procedure planning technique that uses tissue geometry to select the needle insertion location, manipulation locations, and controller parameters. The planner uses a stochastic optimization of a cost function that includes tissue stress and robustness to disturbances. We demonstrate the system on 2D tissues simulated with a mass-spring model, including a simulation of a prostate brachytherapy procedure. It can reduce targeting errors from more than 2 cm to less than 1 mm, and can also shift obstacles by over 1 cm to clear them away from the needle path.

In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by multiple sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the principal component analysis (PCA) and have been pushed to an artificial neural network (ANN) that is a feed-forward type. According to changing in position of defected parts of brain, we have analyzed four different MRI datasets in different stages of MS progression, including 101 MRIs of normal and abnormal brain images. In all cases, certain diagnosis is gained. Meantime, 40 percent of the datasets have been reserved as the "test data ".