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Sorger JM. How to objectively evaluate the impact of image-guided surgery technologies. Eur J Nucl Med Mol Imaging 2024; 51:2869-2877. [PMID: 37971499 DOI: 10.1007/s00259-023-06504-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE This manuscript aims to provide a better understanding of methods and techniques with which one can better quantify the impact of image-guided surgical technologies. METHODS A literature review was conducted with regard to economic and technical methods of medical device evaluation in various countries. Attention was focused on applications related to image-guided interventions that have enabled procedures to be performed in a minimally invasive manner, produced superior clinical outcomes, or have become standard of care. RESULTS The review provides examples of successful implementations and adoption of image-guided surgical techniques, mostly in the field of neurosurgery. Failures as well as newly developed technologies still undergoing cost-efficacy analysis are discussed. CONCLUSION The field of image-guided surgery has evolved from solely using preoperative images to utilizing highly specific tools and software to provide more information to the interventionalist in real time. While deformations in soft tissue often preclude the use of such instruments outside of neurosurgery, recent developments in optical and radioactive guidance have enabled surgeons to better account for organ motion and provide feedback to the surgeon as tissue is cut. These technologies are currently undergoing value assessments in many countries and hold promise to improve outcomes for patients, surgeons, care teams, payors, and society in general.
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Asfaw ZK, Young T, Brown C, Germano IM. Charting the success of neuronavigation in brain tumor surgery: from inception to adoption and evolution. J Neurooncol 2024:10.1007/s11060-024-04778-0. [PMID: 39048723 DOI: 10.1007/s11060-024-04778-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE Neuronavigation, explored as an intra-operative adjunct for brain tumor surgery three decades ago, has become globally utilized with a promising upward trajectory. This study aims to chart its success from idea to adoption and evolution within the US and globally. METHODS A three-pronged methodology included a systematic literature search, impact analysis using NIH relative citation ratio (RCR) and Altmetric scores, and assessment of patent holdings. Data was dichotomized for US and international contexts. RESULTS The first neuronavigation publication stemmed from Finland in 1993, marking its inception. Over three decades, the cumulative number of 323 studies, along with the significantly increasing publication trend (r = 0.74, p < 0.05) and distribution across 34 countries, underscored its progressive and global adoption. Neuronavigation, mostly optical systems (58%), was utilized in over 19,000 cases, predominantly for brain tumor surgery (84%). Literature impact showed a robust cumulative median RCR score surpassing that for NIH-funded studies (1.37 vs. 1.0), with US studies having a significantly higher median RCR than international (1.71 vs. 1.21, p < 0.05). Technological evolution was characterized by adjuncts, including micro/exo/endoscope (21%), MRI (17%), ultrasound (10%), and CT (7%). Patent analysis demonstrated academic and industrial representation with an interdisciplinary convergence of medical and computational sciences. CONCLUSION Since its inception thirty years ago, neuronavigation has been adopted worldwide, and it has evolved with adjunct technology integration to enhance its meaningful use. The current neuronavigation innovation pipeline is progressing, with academic and industry partnering to advance its further application in treating brain tumor patients.
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Affiliation(s)
- Zerubabbel K Asfaw
- Department of Neurosurgery, Icahn School of Medicine, 1 Gustave Levy Place, New York, NY, 10029, USA
| | - Tirone Young
- Department of Neurosurgery, Icahn School of Medicine, 1 Gustave Levy Place, New York, NY, 10029, USA
| | - Cole Brown
- Department of Neurosurgery, Icahn School of Medicine, 1 Gustave Levy Place, New York, NY, 10029, USA
| | - Isabelle M Germano
- Department of Neurosurgery, Icahn School of Medicine, 1 Gustave Levy Place, New York, NY, 10029, USA.
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Sadahiro H, Fujitsuku S, Sugimoto K, Kawano A, Fujii N, Nomura S, Takahashi M, Ishihara H. Bony Surface-Matching Registration of Neuronavigation with Sectioned 3-Dimensional Skull in Prone Position. World Neurosurg 2024; 187:236-242.e1. [PMID: 38750893 DOI: 10.1016/j.wneu.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Neuronavigation has become an essential system for brain tumor resections. It is sometimes difficult to obtain accurate registration of the neuronavigation with the patient in the prone position. Bony surface-matching registration should be more precise than skin surface-matching registration; however, it is difficult to establish bony registration with limited exposed bone. We created a new bony surface-matching method to a sectioned 3-dimensional (3D) virtual skull in a neuronavigation system and registered with a sectioned 3D skull. In this study, the bony surface-matching with sectioned 3D registration is applied to provide precise registration for brain tumor resection in the prone position. METHODS From May 2023 to April 2024, 17 patients who underwent brain tumor resection in the prone position were enrolled. The navigation system StealthStation S8 (Medtronic, Dublin, Ireland) was used. Bony surface-matching registration with a whole 3D skull in a neuronavigation system was performed. Next, a sectioned 3D skull was made according to the surgical location to compare with the whole 3D skull registration. A phantom model was also used to validate the whole and sectioned 3D skull registration. RESULTS Whole 3D skull registration was successful for only 2 patients (11.8%). However, sectioned 3D skull registration was successful for 16 patients (94.1%). The examinations with a phantom skull model also showed superiority of sectioned 3D skull registration to whole 3D skull registration. CONCLUSIONS Sectioned 3D skull registration was superior to whole 3D skull registration. The sectioned 3D skull method could provide accurate registration with limited exposed bone.
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Affiliation(s)
- Hirokazu Sadahiro
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan.
| | - Shunsuke Fujitsuku
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
| | - Kazutaka Sugimoto
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
| | - Akiko Kawano
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
| | - Natsumi Fujii
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
| | - Sadahiro Nomura
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
| | - Masakazu Takahashi
- Graduate School of Innovation of Technology Management, Yamaguchi University, Yamaguchi, Japan
| | - Hideyuki Ishihara
- Department of Neurosurgery and Clinical Neuroscience, Yamaguchi University School of Medicine, Yamaguchi, Japan
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Lambert T, Brunner C, Kil D, Wuyts R, D'Hondt E, Montaldo G, Urban A. A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging. Heliyon 2024; 10:e27432. [PMID: 38495198 PMCID: PMC10943389 DOI: 10.1016/j.heliyon.2024.e27432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 02/28/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. In this study, we present a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. To evaluate its performance, we conducted experiments using a dataset of 51 rat brain scans. The training positions were extracted at intervals of 375 μm, resulting in a positioning error of 176 μm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method's reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but rather on conserved vascular patterns.
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Affiliation(s)
- Théo Lambert
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neuroscience, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Clément Brunner
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neuroscience, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Dries Kil
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neuroscience, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | | | | | - Gabriel Montaldo
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neuroscience, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Alan Urban
- Neuro-Electronics Research Flanders, Leuven, Belgium
- VIB, Leuven, Belgium
- Imec, Leuven, Belgium
- Department of Neuroscience, Faculty of Medicine, KU Leuven, Leuven, Belgium
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5
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Li CL, Fisher CJ, Komolibus K, Lu H, Burke R, Visentin A, Andersson-Engels S. Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications. Sci Data 2024; 11:136. [PMID: 38278822 PMCID: PMC10817894 DOI: 10.1038/s41597-024-02972-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.
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Affiliation(s)
- Celina L Li
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland.
| | - Carl J Fisher
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Katarzyna Komolibus
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Huihui Lu
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Ray Burke
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Andrea Visentin
- Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, Cork, Ireland
| | - Stefan Andersson-Engels
- Biophotonics@Tyndall, IPIC, Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland.
- Department of Physics, University College Cork, Cork, Ireland.
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Chumnanvej S, Chumnanvej S, Tripathi S. Assessing the benefits of digital twins in neurosurgery: a systematic review. Neurosurg Rev 2024; 47:52. [PMID: 38236336 DOI: 10.1007/s10143-023-02260-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
Digital twins are virtual replicas of their physical counterparts, and can assist in delivering personalized surgical care. This PRISMA guideline-based systematic review evaluates current literature addressing the effectiveness and role of digital twins in many stages of neurosurgical management. The aim of this review is to provide a high-quality analysis of relevant, randomized controlled trials and observational studies addressing the neurosurgical applicability of a variety of digital twin technologies. Using pre-specified criteria, we evaluated 25 randomized controlled trials and observational studies on the applications of digital twins, including navigation, robotics, and image-guided neurosurgeries. All 25 studies compared these technologies against usual surgical approaches. Risk of bias analyses using the Cochrane risk of bias tool for randomized trials (Rob 2) found "low" risk of bias in the majority of studies (23/25). Overall, this systematic review shows that digital twin applications have the potential to be more effective than conventional neurosurgical approaches when applied to brain and spinal surgery. Moreover, the application of these novel technologies may also lead to fewer post-operative complications.
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Affiliation(s)
- Sorayouth Chumnanvej
- Neurosurgery Division, Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Siriluk Chumnanvej
- Department of Anesthesiology and Operating Room, Phramongkutklao Hospital, Bangkok, Thailand
| | - Susmit Tripathi
- Department of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, NY, USA.
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Solbakken AM, Sellevold S, Spasojevic M, Julsrud L, Emblemsvåg HL, Reims HM, Sørensen O, Thorgersen EB, Fauske L, Ågren JSM, Brennhovd B, Ryder T, Larsen SG, Flatmark K. Navigation-Assisted Surgery for Locally Advanced Primary and Recurrent Rectal Cancer. Ann Surg Oncol 2023; 30:7602-7611. [PMID: 37481493 PMCID: PMC10562504 DOI: 10.1245/s10434-023-13964-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/03/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND In some surgical disciplines, navigation-assisted surgery has become standard of care, but in rectal cancer, indications for navigation and the utility of different technologies remain undetermined. METHODS The NAVI-LARRC prospective study (NCT04512937; IDEAL Stage 2a) evaluated feasibility of navigation in patients with locally advanced primary (LARC) and recurrent rectal cancer (LRRC). Included patients had advanced tumours with high risk of incomplete (R1/R2) resection, and navigation was considered likely to improve the probability of complete resection (R0). Tumours were classified according to pelvic compartmental involvement, as suggested by the Royal Marsden group. The BrainlabTM navigation platform was used for preoperative segmentation of tumour and pelvic anatomy, and for intraoperative navigation with optical tracking. R0 resection rates, surgeons' experiences, and adherence to the preoperative resection plan were assessed. RESULTS Seventeen patients with tumours involving the posterior/lateral compartments underwent navigation-assisted procedures. Fifteen patients required abdominosacral resection, and 3 had resection of the sciatic nerve. R0 resection was obtained in 6/8 (75%) LARC and 6/9 (69%) LRRC cases. Preoperative segmentation was time-consuming (median 3.5 h), but intraoperative navigation was accurate. Surgeons reported navigation to be feasible, and adherence to the resection plan was satisfactory. CONCLUSIONS Navigation-assisted surgery using optical tracking was feasible. The preoperative planning was time-consuming, but intraoperative navigation was accurate and resulted in acceptable R0 resection rates. Selected patients are likely to benefit from navigation-assisted surgery.
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Affiliation(s)
- Arne M Solbakken
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Simen Sellevold
- Department of Orthopaedic Oncology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Milan Spasojevic
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Lars Julsrud
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Hanne-Line Emblemsvåg
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Henrik M Reims
- Department of Pathology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Olaf Sørensen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Ebbe B Thorgersen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Lena Fauske
- Department of Oncology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
- Department of Interdisciplinary Health Sciences, Institute of Health and Society, University of Oslo, Oslo, Norway
| | | | - Bjørn Brennhovd
- Department of Urology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Truls Ryder
- Department of Oncologic Plastic Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Stein G Larsen
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Kjersti Flatmark
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Tumour Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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8
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Zhang X, Sisniega A, Zbijewski WB, Lee J, Jones CK, Wu P, Han R, Uneri A, Vagdargi P, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain. Med Phys 2023; 50:2607-2624. [PMID: 36906915 PMCID: PMC10175241 DOI: 10.1002/mp.16351] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/03/2023] [Accepted: 02/27/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention. PURPOSE To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality. METHODS The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data. RESULTS CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images. CONCLUSIONS DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery.
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Affiliation(s)
- Xiaoxuan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Wojciech B. Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Junghoon Lee
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Craig K. Jones
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Pengwei Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Runze Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ali Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Prasad Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Mark Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
| | - William S. Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
| | - Jeffrey H. Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
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Watanabe G, Conching A, Nishioka S, Steed T, Matsunaga M, Lozanoff S, Noh T. Themes in neuronavigation research: A machine learning topic analysis. World Neurosurg X 2023; 18:100182. [PMID: 37013107 PMCID: PMC10066551 DOI: 10.1016/j.wnsx.2023.100182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/22/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Objective To understand trends in neuronavigation we employed machine learning methods to perform a broad literature review which would be impractical by manual inspection. Methods PubMed was queried for articles with "Neuronavigation" in any field from inception-2020. Articles were designated neuronavigation-focused (NF) if "Neuronavigation" was a major MeSH. The latent dirichlet allocation topic modeling technique was used to identify themes of NF research. Results There were 3896 articles of which 1727 (44%) were designated as NF. Between 1999-2009 and 2010-2020, the number of NF publications experienced 80% growth. Between 2009-2014 and 2015-2020, there was a 0.3% decline. Eleven themes covered 1367 (86%) NF articles. "Resection of Eloquent Lesions" comprised the highest number of articles (243), followed by "Accuracy and Registration" (242), "Patient Outcomes" (156), "Stimulation and Mapping" (126), "Planning and Visualization" (123), "Intraoperative Tools" (104), "Placement of Ventricular Catheters" (86), "Spine Surgery" (85), "New Systems" (80), "Guided Biopsies" (61), and "Surgical Approach" (61). All topics except for "Planning and Visualization", "Intraoperative Tools", and "New Systems" exhibited a monotonic positive trend. When analyzing subcategories, there were a greater number of clinical assessments or usage of existing neuronavigation systems (77%) rather than modification or development of new apparatuses (18%). Conclusion NF research appears to focus on the clinical assessment of neuronavigation and to a lesser extent on the development of new systems. Although neuronavigation has made significant strides, NF research output appears to have plateaued in the last decade.
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Gruionu LG, Udriștoiu AL, Iacob AV, Constantinescu C, Stan R, Gruionu G. Feasibility of a lung airway navigation system using fiber-Bragg shape sensing and artificial intelligence for early diagnosis of lung cancer. PLoS One 2022; 17:e0277938. [PMID: 36476838 PMCID: PMC9728835 DOI: 10.1371/journal.pone.0277938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022] Open
Abstract
Currently early diagnosis of malignant lesions at the periphery of lung parenchyma requires guidance of the biopsy needle catheter from the bronchoscope into the smaller peripheral airways via harmful X-ray radiation. Previously, we developed an image-guided system, iMTECH which uses electromagnetic tracking and although it increases the precision of biopsy collection and minimizes the use of harmful X-ray radiation during the interventional procedures, it only traces the tip of the biopsy catheter leaving the remaining catheter untraceable in real time and therefore increasing image registration error. To address this issue, we developed a shape sensing guidance system containing a fiber-Bragg grating (FBG) catheter and an artificial intelligence (AI) software, AIrShape to track and guide the entire biopsy instrument inside the lung airways, without radiation or electromagnetic navigation. We used a FBG fiber with one central and three peripheral cores positioned at 120° from each other, an array of 25 draw tower gratings with 1cm/3nm spacing, 2 mm grating length, Ormocer-T coating, and a total outer diameter of 0.2 mm. The FBG fiber was placed in the working channel of a custom made three-lumen catheter with a tip bending mechanism (FBG catheter). The AIrShape software determines the position of the FBG catheter by superimposing its position to the lung airway center lines using an AI algorithm. The feasibility of the FBG system was tested in an anatomically accurate lung airway model and validated visually and with the iMTECH platform. The results prove a viable shape-sensing hardware and software navigation solution for flexible medical instruments to reach the peripheral airways. During future studies, the feasibility of FBG catheter will be tested in pre-clinical animal models.
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Affiliation(s)
| | | | | | - Cătălin Constantinescu
- Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania
| | - Răzvan Stan
- Faculty of Mechanics, University of Craiova, Craiova, Romania
| | - Gabriel Gruionu
- Faculty of Mechanics, University of Craiova, Craiova, Romania
- Division of Cardiology, Department of Medicine, Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- * E-mail: (GG); (LGG)
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11
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Waqar M, Van Houdt PJ, Hessen E, Li KL, Zhu X, Jackson A, Iqbal M, O’Connor J, Djoukhadar I, van der Heide UA, Coope DJ, Borst GR. Visualising spatial heterogeneity in glioblastoma using imaging habitats. Front Oncol 2022; 12:1037896. [PMID: 36505856 PMCID: PMC9731157 DOI: 10.3389/fonc.2022.1037896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/31/2022] [Indexed: 11/26/2022] Open
Abstract
Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.
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Affiliation(s)
- Mueez Waqar
- Department of Neurosurgery, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Petra J. Van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Eline Hessen
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Ka-Loh Li
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Xiaoping Zhu
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Alan Jackson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Neuroradiology, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Mudassar Iqbal
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - James O’Connor
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Ibrahim Djoukhadar
- Department of Neuroradiology, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Uulke A. van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - David J. Coope
- Department of Neurosurgery, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Gerben R. Borst
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
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12
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The Current State of Visualization Techniques in Endoscopic Skull Base Surgery. Brain Sci 2022; 12:brainsci12101337. [PMID: 36291271 PMCID: PMC9599272 DOI: 10.3390/brainsci12101337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022] Open
Abstract
Skull base surgery has undergone significant progress following key technological developments. From early candle-lit devices to the modern endoscope, refinements in visualization techniques have made endoscopic skull base surgery (ESBS) a standard practice for treating a variety of conditions. The endoscope has also been integrated with other technologies to enhance visualization, including fluorescence agents, intraoperative neuronavigation with augmented reality, and the exoscope. Endoscopic approaches have allowed neurosurgeons to reevaluate skull base neuroanatomy from new perspectives. These advances now serve as the foundation for future developments in ESBS. In this narrative review, we discuss the history and development of ESBS, current visualization techniques, and future innovations.
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13
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Gosal JS, Tiwari S, Sharma T, Agrawal M, Garg M, Mahal S, Bhaskar S, Sharma RK, Janu V, Jha DK. Simulation of surgery for supratentorial gliomas in virtual reality using a 3D volume rendering technique: a poor man's neuronavigation. Neurosurg Focus 2021; 51:E23. [PMID: 34333461 DOI: 10.3171/2021.5.focus21236] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Different techniques of performing image-guided neurosurgery exist, namely, neuronavigation systems, intraoperative ultrasound, and intraoperative MRI, each with its limitations. Except for ultrasound, other methods are expensive. Three-dimensional virtual reconstruction and surgical simulation using 3D volume rendering (VR) is an economical and excellent technique for preoperative surgical planning and image-guided neurosurgery. In this article, the authors discuss several nuances of the 3D VR technique that have not yet been described. METHODS The authors included 6 patients with supratentorial gliomas who underwent surgery between January 2019 and March 2021. Preoperative clinical data, including patient demographics, preoperative planning details (done using the VR technique), and intraoperative details, including relevant photos and videos, were collected. RadiAnt software was used for generating virtual 3D images using the VR technique on a computer running Microsoft Windows. RESULTS The 3D VR technique assists in glioma surgery with a preoperative simulation of the skin incision and craniotomy, virtual cortical surface marking and navigation for deep-seated gliomas, preoperative visualization of morbid cortical surface and venous anatomy in surfacing gliomas, identifying the intervenous surgical corridor in both surfacing and deep-seated gliomas, and pre- and postoperative virtual 3D images highlighting the exact spatial geometric residual tumor location and extent of resection for low-grade gliomas (LGGs). CONCLUSIONS Image-guided neurosurgery with the 3D VR technique using RadiAnt software is an economical, easy-to-learn, and user-friendly method of simulating glioma surgery, especially in resource-constrained countries where expensive neuronavigation systems are not readily available. Apart from cortical sulci/gyri anatomy, FLAIR sequences are ideal for the 3D visualization of nonenhancing diffuse LGGs using the VR technique. In addition to cortical vessels (especially veins), contrast MRI sequences are perfect for the 3D visualization of contrast-enhancing high-grade gliomas.
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Affiliation(s)
| | - Sarbesh Tiwari
- 2Diagnostic & Interventional Radiology, All India Institute of Medical Sciences (AIIMS), Jodhpur, Rajasthan, India
| | | | | | | | - Sayani Mahal
- 2Diagnostic & Interventional Radiology, All India Institute of Medical Sciences (AIIMS), Jodhpur, Rajasthan, India
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14
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Bernard F, Haemmerli J, Zegarek G, Kiss-Bodolay D, Schaller K, Bijlenga P. Augmented reality-assisted roadmaps during periventricular brain surgery. Neurosurg Focus 2021; 51:E4. [PMID: 34333465 DOI: 10.3171/2021.5.focus21220] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 11/06/2022]
Abstract
Visualizing major periventricular anatomical landmarks intraoperatively during brain tumor removal is a decisive measure toward preserving such structures and thus the patient's postoperative quality of life. The aim of this study was to describe potential standardized preoperative planning using standard landmarks and procedures and to demonstrate the feasibility of using augmented reality (AR) to assist in performing surgery according to these "roadmaps." The authors have depicted stepwise AR surgical roadmaps applied to periventricular brain surgery with the aim of preserving major cognitive function. In addition to the technological aspects, this study highlights the importance of using emerging technologies as potential tools to integrate information and to identify and visualize landmarks to be used during tumor removal.
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Affiliation(s)
- Florian Bernard
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland.,2Division of Neurosurgery, Angers University Hospitals.,3Laboratory of Anatomy, University of Angers; and.,4CRCINA, UMR 1232 INSERM/CNRS and EA7315 team, Angers, France
| | - Julien Haemmerli
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Gregory Zegarek
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Daniel Kiss-Bodolay
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Karl Schaller
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Philippe Bijlenga
- 1Division of Neurosurgery, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
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15
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Canada, Neurosurgery, and 5-Aminolevulinic Acid (5-ALA): The Long and Winding Road. Can J Neurol Sci 2020; 47:734-735. [PMID: 32475358 DOI: 10.1017/cjn.2020.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Fan Y, Yao X, Xu X. A robust automated surface-matching registration method for neuronavigation. Med Phys 2020; 47:2755-2767. [PMID: 32187386 DOI: 10.1002/mp.14145] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/20/2020] [Accepted: 03/07/2020] [Indexed: 11/10/2022] Open
Abstract
PURPOSE The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration. METHODS A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. RESULTS Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. CONCLUSIONS The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.
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Affiliation(s)
- Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, PR China
| | - Xufeng Yao
- College of Medical Imaging, Shanghai University of Medicine & Healthy Science, Shanghai, PR China
| | - Xiufang Xu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, PR China
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Surface-Registration Frameless Stereotactic Navigation Is Less Accurate During Prone Surgeries: Intraoperative Near-Infrared Visualization Using Second Window Indocyanine Green Offers an Adjunct. Mol Imaging Biol 2020; 22:1572-1580. [PMID: 32232627 DOI: 10.1007/s11307-020-01495-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Frameless neuronavigation allows neurosurgeons to visualize and relate the position of surgical instruments to intracranial pathologies based on preoperative tomographic imaging. However, neuronavigation can often be inaccurate. Multiple factors have been proposed as potential causes, and new technologies are needed to overcome these challenges. OBJECTIVE To evaluate the accuracy of neuronavigation systems compared to near-infrared (NIR) fluorescence imaging using Second Window Indocyanine Green, a novel technique, and to determine factors that lead to neuronavigation errors. METHODS A retrospective analysis was conducted on 56 patients who underwent primary resections of intracranial tumors. Patients received 5 mg/kg ICG approximately 24 h preoperatively. Intraoperatively, neuronavigation was used to plan craniotomies to place the tumors in the center. After craniotomy, NIR imaging visualized tumor-specific NIR signals. The accuracy of neuronavigation and NIR fluorescence imaging for delineating the tumor boundary prior to durotomy was compared. RESULTS The neuronavigation centers and NIR centers were 23.0 ± 7.7 % and 2.6 ± 1.1 % deviated from the tumor centers, respectively, relative to the craniotomy sizes. In 12 cases, significant changes were made to the planned durotomy based on NIR imaging. Patient position was a significant predictor of neuronavigation inaccuracy on both univariate and multivariate analysis, with the prone position having significantly higher inaccuracy (29.2 ± 8.1 %) compared to the supine (16.2 ± 8.1 %, p value < 0.001) or the lateral (17.9 ± 5.1 %, p value = 0.003) positions. CONCLUSION Patient position significantly affects neuronavigation accuracy. Intraoperative NIR fluorescence imaging before durotomy offers an opportunity to readjust the neuronavigation image space to better align with the patient space.
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18
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An Automatic Spatial Registration Method for Image-Guided Neurosurgery System. J Craniofac Surg 2019; 30:e344-e350. [PMID: 30817512 DOI: 10.1097/scs.0000000000005330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVE This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS). METHODS A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method. RESULTS In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration. CONCLUSIONS It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.
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Achey RL, Karsy M, Azab MA, Scoville J, Kundu B, Bowers CA, Couldwell WT. Improved Surgical Safety via Intraoperative Navigation for Transnasal Transsphenoidal Resection of Pituitary Adenomas. J Neurol Surg B Skull Base 2019; 80:626-631. [PMID: 31754596 DOI: 10.1055/s-0039-1677677] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 12/15/2018] [Indexed: 10/27/2022] Open
Abstract
Objectives Intraoperative navigation during neurosurgery can aid in the detection of critical structures and target lesions. The safety and efficacy of intraoperative, stereotactic computed tomography (CT) in the transnasal transsphenoidal resection of pituitary adenomas were explored. Design Retrospective chart review Setting Tertiary care hospital Participants Patients who underwent transsphenoidal resection of pituitary adenomas from February 2002 to May 2017. Intraoperative stereotactic CT navigation was used for all patients after mid-October 2013. Main Outcome Measures Operative time, estimated blood loss, gross total resection rate. Results Of 634 patients included, 175 underwent surgery with intraoperative navigation and 444 had no intraoperative navigation during surgery. There was no difference in mean age, sex, tumor type, or tumor size between the two groups. Operative time, endoscope use, cerebrospinal fluid diversion, and estimated blood loss were also similar. Two patients showed intraoperative, iatrogenic misdirection in the absence of stereotactic CT navigation ( p = 0.99) but similar numbers of patients having navigated and non-navigated surgery returned to the operating room, underwent gross total resection, and showed endocrinological normalization. Conclusions These results suggest that intraoperative navigation can reduce injury without resulting in increased operative time, estimated blood loss, or reduction in gross total resection.
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Affiliation(s)
- Rebecca L Achey
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, United States
| | - Michael Karsy
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
| | - Mohammed A Azab
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
| | - Jonathan Scoville
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
| | - Bornali Kundu
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
| | - Christian A Bowers
- Department of Neurosurgery, New York Medical College, Valhalla, New York, United States
| | - William T Couldwell
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, United States
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20
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Miner RC. Image-Guided Neurosurgery. J Med Imaging Radiat Sci 2017; 48:328-335. [PMID: 31047466 DOI: 10.1016/j.jmir.2017.06.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 06/27/2017] [Indexed: 01/21/2023]
Abstract
Image-guided surgery provides more precise targeting, is less invasive, and has improved outcomes when compared with conventional surgical approaches. Imaging is used to plan, monitor progress, and assess results. Because no one modality offers real-time physiological and anatomical information, a wide range of imaging modalities are used at each phase of the surgery. This article will discuss how various modalities are used in image-guided neurosurgery for common brain pathologies.
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Affiliation(s)
- Robert C Miner
- Carleton University, Ottawa, Ontario, Canada; Ottawa Heart Institute, Ottawa, Ontario, Canada.
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