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Lazarus J, Asselin M, Kaestner L. Optically tracked needle for ultrasound guided percutaneous nephrolithotomy (PCNL) puncture: A preliminary report. J Endourol 2021; 35:1733-1737. [PMID: 34114486 DOI: 10.1089/end.2021.0136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction Precise needle puncture of the renal collecting system is an essential step for successful percutaneous nephrolithotomy (PCNL). The use of ultrasound for puncture is receiving increased attention. Ultrasound has recognised limitations related to poor visualisation of the needle tip. We aimed to assess if an affordable open source computerized needle navigation training system, using optically tracked ultrasonography, could improve performance of simulated PCNL puncture by urological trainees, compared to conventional free hand manual sonographic puncture. Methods This study describes a PCNL navigation system which can be recreated with any standard ultrasound machine using relatively inexpensive components. The system allows the needle tip to be precisely appreciated in the ultrasound image, its trajectory planned, and the appreciation of needle tip to target calyx proximity aided by sound. Eight urology trainees participated in assessment of the PCNL training model. Alternating freehand (control) and tracked needle (experimental) punctures were performed on a phantom kidney. Total procedure and the number of reinsertions required were recorded. Results The mean time for freehand puncture was 89 seconds (range 13 - 173), while that of the optically tracked needle was 36 seconds (range 12 - 72). Thus, puncture time was significantly reduced by an average of 53 seconds (p=0.045) in the experimental arm. The mean number of needle reinsertions was 3,3 with freehand compared to 1,3 in the optically tracked puncture (p = 0.005). The mean square root error (MSRE) of the system was 1,8 mm. Conclusion This study demonstrates that affordable hardware and open source software can be used to construct an optically tracked ultrasound navigation system for PCNL training. Statistically significant reduced puncture time and number of passes required for successful puncture was demonstrated. We feel that computerised needle tracking during PCNL puncture deserves further evaluation in a training, and potentially, a clinical setting.
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Affiliation(s)
- John Lazarus
- University of Cape Town, Division of Urology, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa, 8001;
| | - Mark Asselin
- Queen's University Faculty of Health Sciences, 12363, Laboratory for Percutaneous Surgery, Goodwin Hall Room 557, 25 Union St, Kingston, Ontario, Canada, K7L 2N8;
| | - Lisa Kaestner
- University of Cape Town (UCT), Urology, Cape Town, South Africa;
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Wang C, Calle P, Tran Ton NB, Zhang Z, Yan F, Donaldson AM, Bradley NA, Yu Z, Fung KM, Pan C, Tang Q. Deep-learning-aided forward optical coherence tomography endoscope for percutaneous nephrostomy guidance. BIOMEDICAL OPTICS EXPRESS 2021; 12:2404-2418. [PMID: 33996237 PMCID: PMC8086467 DOI: 10.1364/boe.421299] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 05/18/2023]
Abstract
Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.
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Affiliation(s)
- Chen Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
- These authors contributed equally to this work
| | - Paul Calle
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
- These authors contributed equally to this work
| | - Nu Bao Tran Ton
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
| | - Zuyuan Zhang
- School of Computer Science, University of Oklahoma, Norman, OK 73072, USA
| | - Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
| | - Anthony M Donaldson
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
| | - Nathan A Bradley
- Department of Urology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Zhongxin Yu
- Children's Hospital, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Kar-Ming Fung
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Chongle Pan
- School of Computer Science, University of Oklahoma, Norman, OK 73072, USA
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA
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Nguyen DD, Luo JW, Tailly T, Bhojani N. Percutaneous Nephrolithotomy Access: A Systematic Review of Intraoperative Assistive Technologies. J Endourol 2019; 33:358-368. [DOI: 10.1089/end.2019.0085] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
| | - Jack W. Luo
- Faculty of Medicine, McGill University, Montreal, Canada
| | - Thomas Tailly
- Urology Department, University Hospital Ghent, Ghent, Belgium
| | - Naeem Bhojani
- Division of Urology, University of Montreal Health Center (CHUM), Montreal, Canada
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Gomes-Fonseca J, Queirós S, Morais P, Pinho ACM, Fonseca JC, Correia-Pinto J, Lima E, Vilaça JL. Surface-based registration between CT and US for image-guided percutaneous renal access - A feasibility study. Med Phys 2019; 46:1115-1126. [DOI: 10.1002/mp.13369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/13/2018] [Accepted: 12/19/2018] [Indexed: 12/30/2022] Open
Affiliation(s)
- João Gomes-Fonseca
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
| | - Sandro Queirós
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
| | - Pedro Morais
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
| | - António C. M. Pinho
- Department of Mechanical Engineering; School of Engineering; University of Minho; Guimarães Portugal
| | - Jaime C. Fonseca
- Algoritmi Center; School of Engineering; University of Minho; Guimarães Portugal
- Department of Industrial Electronics; School of Engineering; University of Minho; Guimarães Portugal
| | - Jorge Correia-Pinto
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- Department of Pediatric Surgery; Hospital of Braga; Braga Portugal
| | - Estêvão Lima
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- Deparment of Urology; Hospital of Braga; Braga Portugal
| | - João L. Vilaça
- Life and Health Sciences Research Institute (ICVS); School of Medicine; University of Minho; Braga Portugal
- ICVS/3B's-PT; Government Associate Laboratory; Braga/Guimarães 4710-057 Portugal
- 2Ai; Polytechnic Institute of Cávado and Ave; Barcelos Portugal
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Ma L, Zhao Z, Chen F, Zhang B, Fu L, Liao H. Augmented reality surgical navigation with ultrasound-assisted registration for pedicle screw placement: a pilot study. Int J Comput Assist Radiol Surg 2017; 12:2205-2215. [DOI: 10.1007/s11548-017-1652-z] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 07/21/2017] [Indexed: 11/28/2022]
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