Computer-assisted neurosurgery has become so successful that it is rapidly becoming indistinguishable from, quite simply, neurosurgery. This trend promises to accelerate over the next several decades, bringing considerable benefit to the patients we care for. From a pragmatic point of view, can we identify specific instances in which clinical practice has been altered by computer assistance? During craniotomies for the resection of brain tumors, this technology has led to a greater standardization within and among practitioners for the expected degree of resection and the risk of morbidity and mortality. Minimally invasive approaches are transforming the practice of cranial base surgery. This technological trend has made craniotomy for biopsy virtually obsolete in the face of frameless stereotactic techniques. Functional neurosurgery has benefited from these technologies, as deep brain stimulation surgery has become the standard of care for most cases of movement disorder surgery. Extratemporal epilepsy due to cortical dysplasia has proven especially amenable to image-guided surgical techniques that integrate electrophysiological monitoring to refine the target of resection. New surgical procedures made possible by computer assistance include minimally invasive spine surgery, endovascular procedures, resections of low-grade nonenhancing gliomas, and stereotactic radiosurgery. A program for future research and development in this field would include: Electronic patient medical records. Automatic dynamic and elastic registration Novel surgical instrumentation guided by augmented reality Real-time feedback using anatomic and functional information Active robotic servo control systems to amplify neurosurgical capabilities Outcomes analysis-driven refinement of neurosurgical interventions. It is apparent that using computer assistance in neurosurgery has begun a process that will irrevocably transform all of neurosurgical practice itself. It must be neurosurgeons themselves who provide the leadership to transcend the potentially distracting aspects of this technological revolution. What shall not change is the commitment that we, as neurosurgeons, have to the welfare of our patients.
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Tomography
November 2024
Department of Diagnostic Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara 259-1193, Japan.
Photon-counting detector computed tomography (PCD-CT) offers energy-resolved CT data with enhanced resolution, reduced electronic noise, and improved tissue contrast. This study aimed to evaluate the visibility of intracranial perforating arteries on ultra-high-resolution (UHR) CT angiography (CTA) on PCD-CT. A retrospective analysis of intracranial UHR PCD-CTA was performed for 30 patients.
View Article and Find Full Text PDFCurr Oncol
December 2024
Specialty Hospital Radiochirurgia Zagreb, 10431 Sveta Nedelja, Croatia.
We present a patient treated with personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) for non-small cell lung cancer (NSCLC) using the adaptive Varian Ethos™ system equipped with the novel HyperSight imaging platform. Three pulses of 12 Gy were separated by a pause of four weeks during which the tumor was given enough time to respond to treatment. Only initial planning computed tomography (CT) was acquired on a CT simulator (Siemens Somatom Definition Edge), whereas other pulses were adapted using online cone beam computed tomography (CBCT) images (iCBCT Acuros reconstruction) acquired while the patient was lying on the treatment couch and delivered immediately.
View Article and Find Full Text PDFHum Brain Mapp
December 2024
Department of Psychology, Northeastern University, Boston, Massachusetts, USA.
Diffusion-weighted imaging (DWI) has been frequently used to examine age-related deterioration of white matter microstructure and its relationship to cognitive decline. However, typical tensor-based analytical approaches are often difficult to interpret due to the challenge of decomposing and (mis)interpreting the impact of crossing fibers within a voxel. We hypothesized that a novel analytical approach capable of resolving fiber-specific changes within each voxel (i.
View Article and Find Full Text PDFInt J Med Robot
December 2024
School of Mechanical Engineering, Tianjin University, Tianjin, China.
Background: In order to achieve spatial registration for surgical navigation, a spatial registration method based on point cloud and deep learning is proposed.
Methods: Neural networks are used to register medical image point clouds and patient surface point clouds to complete spatial registration of surgical navigation. An image processing method is designed to convert medical images into point clouds, and a structured light robot is used to extract patient surface point clouds.
Childs Nerv Syst
December 2024
Department of Neurosurgery, Osaka Women's and Children's Hospital, Izumi, Osaka, 594-1101, Japan.
Purpose: This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods.
Methods: Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume.
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