Michael Loser
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Featured researches published by Michael Loser.
medical image computing and computer assisted intervention | 2000
Michael Loser; Nassir Navab
Minimally invasive CT-guided interventions are an attractive option for diagnostic biopsy and localized therapy delivery. This paper describes the concept of a new prototypical robotic tool, developed in a preliminary study for radiological image-guided interventions. Its very compact and light design is optimized for usage inside a CT-gantry, where a bulky robot is inappropriate, especially together with a stout patient and long stiff instruments like biopsy needles or a trocar. Additionally, a new automatic image-guided control based on “visual servoing” is presented for automatic and uncalibrated needle place-ment under CT-fluoroscopy. Visual servoing is well established in the field of industrial robotics, when using CCD cameras. We adapted this approach and optimized it for CT-fluoroscopy-guided interventions. It is a simple and accurate method which requires no prior calibration or registration. Therefore, no additional sensors (infrared, laser, ultrasound, etc), no stereotactic frame and no additional calibration phantom is needed. Our technique provides accurate 3D alignment of the needle with respect to an anatomic target. A first evaluation of the robot using CT fluoroscopy showed an accuracy in needle placement of ±0.4 mm (principle accuracy) and ±1.6 mm in a small pig study. These first promising results present our method as a possible alternative to other needle placement techniques requiring cumbersome and time consuming calibration procedures.
Medical Imaging 2000: Image Display and Visualization | 2000
Michael Loser; Nassir Navab; Benedicte Bascle; Russell H. Taylor
Visual servoing is well established in the field of industrial robotics, when using CCD cameras. This paper describes one of the first medical implementations of uncalibrated visual servoing. To our knowledge, this is the first time that visual servoing is done using x-ray fluoroscopy. In this paper we present a new image based approach for semi-automatically guidance of a needle or surgical tool during percutaneous procedures and is based on a series of granted and pending US patent applications. It is a simple and accurate method which requires no prior calibration or registration. Therefore, no additional sensors, no stererotactic frame and no additional calibration phantom is needed. Our techniques provides accurate 3D alignment of the tool with respect to an anatomic target and estimates the required insertion depth. We implemented and verified this method with three different medical robots at the Computer Integrated Surgery (CIS) Lab at the Johns Hopkins University. First tests were performed using a CCD-camera and a mobile uniplanar x-ray fluoroscope as imaging modality. We used small metal balls of 4 mm in diameter as target points. These targets were placed 60 to 70 mm deep inside a test-phantom. Our method led to correct insertions with mean deviation of 0.20 mm with CCD camera and mean deviation of about 1.5 mm in clinical surrounding with an old x-ray imaging system, where the images were not of best quality. These promising results present this method as a serious alternative to other needle placement techniques, which require cumbersome and time consuming calibration procedures.
Archive | 2003
Dieter Cherek; Robert Kagermeier; Michael Loser; Donal Medlar; Hendrik Steinmann; Uwe Urmoneit
computer vision and pattern recognition | 2000
Nassir Navab; Benedicte Bascle; Michael Loser; Bernhard Geiger; Russell H. Taylor
Archive | 2003
Dieter Cherek; Robert Kagermeier; Michael Loser; Donal Medlar; Hendrik Steinmann; Uwe Urmoneit
Archive | 2002
Dieter Cherek; Robert Kagermeier; Michael Loser; Donal Medlar; Hendrik Steinmann; Uwe Urmoneit
Archive | 2001
Michael Loser
Archive | 2001
Michael Loser
Archive | 2005
Hans-Jürgen Kröner; Michael Loser
Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737) | 2000
Benedicte Bascle; Nassir Navab; Michael Loser; Bernhard Geiger; Russell H. Taylor