skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Keyhole Mini-Craniotomy Middle Fossa Approach for Tegmen Repair: A Case Series and Technical Instruction
Abstract Background and Importance Tegmen defects associated with cerebrospinal fluid (CSF) leaks are a rare pathology that can result in severe complications if left untreated. There is no universal optimal surgical algorithm for repair, although the most common techniques are the middle fossa craniotomy (traditionally 25 cm2 in area), the transmastoid approach, or both. Here, we describe successful use of a keyhole mini-craniotomy, only 6 cm2 in area, without mastoidectomy or days of lumbar drainage. Clinical Presentation Three patients presented with right-sided CSF otorrhea and hearing loss, with varying sizes of tegmen defects and associated encephaloceles. Keyhole craniotomies measuring 3 × 2 cm were used to perform a multilayer repair comprising an intradural collagen dural substitute, extradural fascial graft, extradural collagen dural substitute, fibrin sealant, and sometimes bony reconstruction using partial thickness craniotomy grafting. All patients were discharged on postoperative day 1 or 2, with no recurrence of symptoms at 6 months. Conclusion The keyhole craniotomy approach does not sacrifice the extent of operative access for this pathology. This minimally invasive approach can likely be used more often without need for concomitant mastoidectomy, ultimately enabling shorter hospital stays and more rapid recovery.  more » « less
Award ID(s):
2125528
PAR ID:
10651543
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
The American Association of Neurological Surgeons (AANS) publishes Neurosurgical Focus. The journal is published under the AANS's scholarly publication arm, the Journal of Neurosurgery Publishing Group (JNSPG).
Date Published:
Journal Name:
Journal of Neurological Surgery Reports
Volume:
86
Issue:
01
ISSN:
2193-6358
Page Range / eLocation ID:
e19 to e23
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets. 
    more » « less
  2. A new metric was developed to quantify the impact of surface-connected defects and internal pores of different morphologies, namely irregular lack of fusion (LoF) pores and spherical keyhole pores, on the mechanical properties and fracture location of AlSi10Mg tensile samples fabricated using laser powder bed fusion additive manufacturing. As defect volume alone has been shown to be insufficient to predict fracture location, the proposed defect impact metric (DIM) incorporates contributions from additional defect features, including proximity to the surface, interaction with neighboring defects, morphology, and reduction in load-bearing cross-sectional area to better assess a defect’s propensity for corresponding to fracture location. The fracture location of keyhole samples was captured by large surface-connected defects with numerous neighboring defects and resulted in increased losses in load-bearing area. In contrast, LoF samples fractured at regions with either large surface-connected defects or large internal pores with many defects in close proximity, high curvatures, and large projected areas. The proposed DIM outperformed existing defect-based frameworks in identifying fracture locations in both LoF and keyhole samples by incorporating surface roughness, defect projected area, and interactions between defects based on distance, volume, and configuration. Additionally, the maximum DIM value within the fracture range was more strongly correlated to strength and ductility than porosity or defect size for LoF samples, demonstrating the potential of the DIM to non-destructively assess the effects of defects on mechanical behavior. The broader applicability of the DIM framework was demonstrated in its ability to capture fracture in both PBF-LB AlSi10Mg and Alloy 718. 
    more » « less
  3. Laser powder bed fusion is a dominant metal 3D printing technology. However, porosity defects remain a challenge for fatigue-sensitive applications. Some porosity is associated with deep and narrow vapor depressions called keyholes, which occur under high-power, low–scan speed laser melting conditions. High-speed x-ray imaging enables operando observation of the detailed formation process of pores in Ti-6Al-4V caused by a critical instability at the keyhole tip. We found that the boundary of the keyhole porosity regime in power-velocity space is sharp and smooth, varying only slightly between the bare plate and powder bed. The critical keyhole instability generates acoustic waves in the melt pool that provide additional yet vital driving force for the pores near the keyhole tip to move away from the keyhole and become trapped as defects. 
    more » « less
  4. Abstract Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer’s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10–20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer’s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer’s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59–0.91) and Alzheimer’s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer’s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment. 
    more » « less
  5. Abstract Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two‐dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over‐ and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three‐dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape‐based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open‐top light‐sheet (OTLS) microscopy of 102 cancer‐containing biopsies extractedex vivofrom the prostatectomy specimens of 46 patients. A deep learning‐based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape‐based nuclear features were extracted, and a nested cross‐validation scheme was used to train a supervised machine classifier based on 5‐year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape‐based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape‐based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision‐support tools. © 2023 The Pathological Society of Great Britain and Ireland. 
    more » « less