A partial augmented reality system with live ultrasound and registered preoperative MRI for guiding robot-assisted radical prostatectomy.
Med Image Anal
; 60: 101588, 2020 02.
Article
en En
| MEDLINE
| ID: mdl-31739281
ABSTRACT
We propose an image guidance system for robot assisted laparoscopic radical prostatectomy (RALRP). A virtual 3D reconstruction of the surgery scene is displayed underneath the endoscope's feed on the surgeon's console. This scene consists of an annotated preoperative Magnetic Resonance Image (MRI) registered to intraoperative 3D Trans-rectal Ultrasound (TRUS) as well as real-time sagittal 2D TRUS images of the prostate, 3D models of the prostate, the surgical instrument and the TRUS transducer. We display these components with accurate real-time coordinates with respect to the robot system. Since the scene is rendered from the viewpoint of the endoscope, given correct parameters of the camera, an augmented scene can be overlaid on the video output. The surgeon can rotate the ultrasound transducer and determine the position of the projected axial plane in the MRI using one of the registered da Vinci instruments. This system was tested in the laboratory on custom-made agar prostate phantoms. We achieved an average total registration accuracy of 3.2 ⯱⯠1.3 mm. We also report on the successful application of this system in the operating room in 12 patients. The average registration error between the TRUS and the da Vinci system for the last 8 patients was 1.4 ⯱⯠0.3 mm and average target registration error of 2.1 ⯱⯠0.8 mm, resulting in an in vivo overall robot system to MRI mean registration error of 3.5 mm or less, which is consistent with our laboratory studies.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Asunto principal:
Prostatectomía
/
Ultrasonografía
/
Laparoscopía
/
Cirugía Asistida por Computador
/
Procedimientos Quirúrgicos Robotizados
/
Realidad Aumentada
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2020
Tipo del documento:
Article