A Spherical Cap Model of Epidural Hematomas.

Background Epidural hematomas (EDHs), which have a characteristic biconvex shape, are a type of post-traumatic intracranial mass. EDHs and other types of intracranial hematomas are often diagnosed with computed tomography (CT). The volumes of EDHs are important in treatment decisions and prognosis. Their volumes are usually estimated on CT using the "ABC" method, which is based on the ellipsoid shape rather than their biconvex shape. Objective To simulate the biconvex shape, we modeled the geometry of EDHs with two spherical caps. We aim to provide simpler estimation of EDH volumes in clinical settings, and eventually recommend a threshold for surgical evacuation. Methods Applying the relationship between the sphere radius, spherical cap height, and base circle radius, we derived formulas for the shape of an EDH, relating its largest diameter and location to the other two diameters. We also estimated EDH volumes using the spherical cap volume and conventional ABC formulas and then constructed a lookup table accordingly. Results Validation of the model was performed using 14 CT image sets from previously reported patients with EDHs. Our geometric model demonstrated accurate predictions. The model also allows reducing the number of parameters to be measured in the ABC method from three to one, the hematoma length, showcasing its potential as a reliable tool for clinical decision-making. Based on our model, an EDH longer than 7 cm would occupy more than 30 mL of the intracranial volume. Conclusion The proposed model offers a streamlined approach to estimating EDH volumes, reducing the complexity of parameters required for clinical assessments. We recommend a length of 7 cm as a threshold for surgical evacuation of EDHs. This acceleration in decision-making is crucial for managing critically injured patients with traumatic brain injuries. Further validation across diverse patient populations will enhance the generalizability and utility of this geometric modeling approach in clinical settings.

Diseases That Occur Prior to Spontaneous Intracerebral Hemorrhage: Identification of Predisposing and Risk Factors Using Lag Sequential Analysis.

Spontaneous intracerebral hemorrhage (sICH) has many predisposing/risk factors. Lag sequential analysis (LSA) is a method of analyzing sequential patterns and their associations within categorical data in different system states. The results of this study will assist in preventing sICH and improving the patient outcome after sICH. The correlations between a first sICH and previous clinic visits were examined using LSA with data obtained from the Taiwan National Health Insurance Research Database (NHIRD). In this study, LSA was employed to examine the data in the Taiwan NHIRD in order to identify predisposing and risk factors related to sICH, and the results increased our knowledge of the temporal relationships between diseases. This study employed LSA to identify predisposing/risk factors prior to the first occurrence of sICH using a healthcare administrative database in Taiwan. The data were managed using the clinical classification software (CCS). All cases of traumatic ICH were excluded. Ten disease groups were identified using CCS. Hypertension and dizziness/vertigo were identified as two important predisposing/risk factors for sICH, and early treatment of hypertension resulted in a greater survival rate. Five disease groups were found to have occurred prior to other diseases and affected mostly the elderly, resulting in subsequent sICH. The results of this study also showed that nutritional status and tooth health were highly associated with the occurrence of sICH owing to a poor state of the digestive system. In conclusion, there are many diseases that influence the risk of a subsequent sICH. This study demonstrated that LSA is a very useful tool for future study of healthcare administrative databases.

Effects of thoracic sympathetic stimulation on palmar perfusion: a preliminary study in pigs.

OBJECTIVE: Ablation of the upper thoracic sympathetic ganglia that innervates the hands is the most effective and permanent cure of palmar hyperhidrosis. However, this type of sympathectomy causes irreversible neural damage and may result in severe compensatory hyperhidrosis. This experiment is designed to confirm the hypothesis, in which the stimulation of T2 sympathetic chain leads to increased palmar microcirculation, and thus results in treating hyperhidrosis. METHODS: In this study, we used electric stimulation to induce reversible blockade of the sympathetic ganglion in pigs and investigated its effect on palmar perfusion. An electrode was inserted to the T2 sympathetic ganglion of the pig through three different approaches: open dorsal, thoracoscopic, and fluoroscopy-guided approaches. Electric stimulation was delivered through the electrode using clinically available pulse generators. Palmar microcirculation was evaluated by laser speckle contrast imaging. RESULTS: The T2 sympathetic ganglion of the pig was successfully accessed by all the three approaches, as confirmed by changes in palmar microcirculation during electric stimulation. Similar effects were not observed when the electrode was placed on the T4 sympathetic ganglion or off the sympathetic trunk. CONCLUSION: We established a large animal model to verify the effect of thoracic sympathetic stimulation. Electric stimulation can be used for sympathetic blockade, as confirmed by increased blood perfusion of the palm. Our work suggests that sympathetic stimulation is a potential solution for palmar hyperhidrosis.

The intracranial tumor segmentation challenge: Contour tumors on brain MRI for radiosurgery.

We report the set-up of the Intracranial Tumor Segmentation (ICTS) dataset. This dataset was retrieved from clinical work of radiosurgery, contoured by qualified neurosurgeons and radiation oncologists. It contains contrast-enhanced T1-weighted images of 1500 patients, together with the labels of tumors to be treated. The ICTS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

A nomogram for estimating intracranial pressure using acute subdural hematoma thickness and midline shift.

Although criteria for surgical treatment of acute subdural hematoma (SDH) have been proposed, interaction exists between SDH, midline shift (MLS), and intracranial pressure (ICP). Based on our half sphere finite-element model (FEM) of the supratentorial brain parenchyma, tools for ICP estimation using SDH thickness (SDHx) and MLS were developed. We performed 60 single load step, structural static analyses, simulating a left-sided SDH compressing the cerebral hemispheres. The Young's modulus was taken as 10,000 Pa. The ICP loads ranged from 10 to 80 mmHg with Poisson's ratios between 0.25 and 0.49. The SDHx and the MLS results were stored in a lookup table. An ICP estimation equation was derived from these data and then was converted into a nomogram. Numerical convergence was achieved in 49 model analyses. Their SDHx ranged from 0.79 to 28.3 mm, and the MLS ranged from 1.5 to 16.9 mm. The estimation formula was log(ICP) = 0.614-0.520 log(SDHx) + 1.584 log(MLS). Good correlations were observed between invasive ICP measurements and those estimated from preoperative SDHx and MLS data on images using our model. These tools can be used to estimate ICP noninvasively, providing additional information for selecting the treatment strategy in patients with SDH.

Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms.

Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.

Atlas-Free Cervical Spinal Cord Segmentation on Midsagittal T2-Weighted Magnetic Resonance Images.

An automatic atlas-free method for segmenting the cervical spinal cord on midsagittal T2-weighted magnetic resonance images (MRI) is presented. Pertinent anatomical knowledge is transformed into constraints employed at different stages of the algorithm. After picking up the midsagittal image, the spinal cord is detected using expectation maximization and dynamic programming (DP). Using DP, the anterior and posterior edges of the spinal canal and the vertebral column are detected. The vertebral bodies and the intervertebral disks are then segmented using region growing. Then, the anterior and posterior edges of the spinal cord are detected using median filtering followed by DP. We applied this method to 79 noncontrast MRI studies over a 3-month period. The spinal cords were detected in all cases, and the vertebral bodies were successfully labeled in 67 (85%) of them. Our algorithm had very good performance. Compared to manual segmentation results, the Jaccard indices ranged from 0.937 to 1, with a mean of 0.980 ± 0.014. The Hausdorff distances between the automatically detected and manually delineated anterior and posterior spinal cord edges were both 1.0 ± 0.5 mm. Used alone or in combination, our method lays a foundation for computer-aided diagnosis of spinal diseases, particularly cervical spondylotic myelopathy.

Subdural effusion protects the aging brain from harmful ventriculomegaly.

The human brain loses its volume and its function during aging. The solid part of the brain within the intracranial space, the brain parenchyma, decreases in volume with age; while the cerebrospinal fluid (CSF) volume increases. With progressive loss of brain parenchymal volume (BPV), CSF may shift from cerebral ventricles to the subdural space, forming subdural effusion (SDE), whose role in the brain aging process remains unclear. We hypothesize that damages associated with ventriculomegaly can be lessened after formation of SDE. As the BPV decreases, the enlarged ventricular surface area causes dysfunction of its lining ependymal cells, followed by damages to the periventricular tissue. The periventricular nerve fibers are stretched by the enlarged ventricles. We hypothesize that after the formation of SDE, ventriculomegaly can be stopped or even reversed. By allowing the atrophic brain to reside in a smaller fraction of the intracranial volume, damages associated with ventriculomegaly can be alleviated. If our hypothesis is correct, physicians should continue to maintain a conservative approach for uncomplicated SDE. For focal or global brain parenchymal loss caused by various pathologies, intracranial spacers can be employed to simulate the effect of SDE to protect the brain. For treatment of idiopathic normal pressure hydrocephalus, aggressive ventricular size reduction should be pursued. Finally, the protective effects of SDE have its limits. Extremely enlarged subdural volume can cause acute or chronic subdural hematoma, further damaging the brain.

Transcalvarial brain herniation volume after decompressive craniectomy is the difference between two spherical caps.

Decompressive craniectomy (DC) is a surgical procedure used to relieve severely increased intracranial pressure (ICP) by removing a portion of the skull. Following DC, the brain expands through the skull defect created by DC, resulting in transcalvarial herniation (TCH). Traditionally, people measure only changes in the ICP but not in the intracranial volume (ICV), which is equivalent to the volume of TCH (V(TCH)), in patients undergoing DC. We constructed a simple model of the cerebral hemispheres, assuming the shape of the upper half of a sphere with a radius of 8 cm. We hypothesized that the herniated brain following DC also conforms to the shape of a spherical cap. Considering that a circular piece of the skull with a radius of a was removed, V(TCH) is the volume difference between 2 spherical caps at the operated side and the corresponding non-operated side, which represents the pre-DC volume underneath the removed skull due to the bilateral symmetry of the skull and the brain. Subsequently, we hypothesized that the maximal extent of TCH depends on a because of the biomechanical limitations imposed by the inelastic scalp. The maximum value of V(TCH) is 365.0 mL when a is 7.05 cm and the height difference between the spherical caps (Δh) at its maximum is 2.83 cm. To facilitate rapid calculation of V(TCH), we proposed a simplified estimation formula, Vˆ(TCH)=1/2A(2)Δh, where A=2a. With the a value ranging between 0 and 7 cm, the ratio between Vˆ(TCH) and V(TCH) ranges between 0.77 and 1.27, with different Δh values. For elliptical skull defects with base diameters of A and C, the formula changes to Vˆ(TCH)=1/2ACΔh. If our hypothesis is correct, surgeons can accurately calculate V(TCH) after DC. Furthermore, this can facilitate volumetric comparisons between the effects of DCs in skulls of varying sizes, allowing quantitative comparisons between ICVs in addition to ICPs.

PICO element detection in medical text without metadata: are first sentences enough?

Efficient identification of patient, intervention, comparison, and outcome (PICO) components in medical articles is helpful in evidence-based medicine. The purpose of this study is to clarify whether first sentences of these components are good enough to train naive Bayes classifiers for sentence-level PICO element detection. We extracted 19,854 structured abstracts of randomized controlled trials with any P/I/O label from PubMed for naive Bayes classifiers training. Performances of classifiers trained by first sentences of each section (CF) and those trained by all sentences (CA) were compared using all sentences by ten-fold cross-validation. The results measured by recall, precision, and F-measures show that there are no significant differences in performance between CF and CA for detection of O-element (F-measure=0.731±0.009 vs. 0.738±0.010, p=0.123). However, CA perform better for I-elements, in terms of recall (0.752±0.012 vs. 0.620±0.007, p<0.001) and F-measures (0.728±0.006 vs. 0.662±0.007, p<0.001). For P-elements, CF have higher precision (0.714±0.009 vs. 0.665±0.010, p<0.001), but lower recall (0.766±0.013 vs. 0.811±0.012, p<0.001). CF are not always better than CA in sentence-level PICO element detection. Their performance varies in detecting different elements.

Estimating postoperative skull defect volume from CT images using the ABC method.

OBJECTIVES: Surgeons often perform decompressive craniectomy to alleviate a medically-refractory increase of intracranial pressure. The frequency of this type of surgery is on the rise. The goal of this study is to develop a simple formula for clinicians to estimate the volume of the skull defect, based on postoperative computed tomography (CT) studies. METHODS: We collected thirty sets of postoperative CT images from patients undergoing craniectomy. We measured the skull defect volume by computer-assisted volumetric analysis (V(m)) and our own ABC technique (V(abc)). We then compared the volumes measured by these two methods. RESULTS: The V(m) ranged from 3.2 to 76.4 mL, with a mean of 38.9 mL. The V(abc) ranged from 3.8 to 71.5 mL, with a mean of 38.5 mL. The absolute differences between V(abc) and V(m) ranged from 0.05 to 17.5 mL (mean: 3.8±4.2). There was no statistically significant difference between V(abc) and V(m) (p=0.961). The correlation coefficient between V(abc) and V(m) was 0.969. In linear regression analysis, the slope was 1.00086 and the intercept was -0.0035 mL (r(2)=0.939). The residual was 5.7 mL. CONCLUSION: We confirmed that the ABC technique is a simple and accurate method for estimating skull defect volume, and we recommend routine application of this formula for all decompressive craniectomies.

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing.

BACKGROUND: In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. METHODS: The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. RESULTS: The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. CONCLUSIONS: Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.

Automatic measurement of midline shift on deformed brains using multiresolution binary level set method and Hough transform.

Midline shift (MLS) is an important quantitative feature clinicians use to evaluate the severity of brain compression by various pathologies. The midline consists of many anatomical structures including the septum pellucidum (SP), a thin membrane between the frontal horns (FH) of the lateral ventricles. We proposed a procedure that can measure MLS by recognizing the SP within the given CT study. The FH region is selected from all ventricular regions by expert rules and the multiresolution binary level set method. The SP is recognized using Hough transform, weighted by repeated morphological erosion. Our system is tested on images from 80 patients admitted to the neurosurgical intensive care unit. The results are evaluated by human experts. The mean difference between automatic and manual MLS measurements is 0.23 ± 0.52 mm. Our method is robust and can be applied in emergency and routine settings.

Automated assessment of midline shift in head injury patients.

OBJECTIVES: Midline shift (MLS) is an important quantitative feature for evaluating severity of brain compression by various pathologies, including traumatic intracranial hematomas. In this study, we sought to determine the accuracy and the prognostic value of our computer algorithm that automatically measures the MLS of the brain on computed tomography (CT) images in patients with head injury. PATIENTS AND METHODS: Modelling the deformed midline into three segments, we had designed an algorithm to estimate the MLS automatically. We retrospectively applied our algorithm to the initial CT images of 53 patients with head injury to determine the automated MLS (aMLS) and validated it against that measured by human (hMLS). Both measurements were separately used to predict the neurological outcome of the patients. RESULTS: The hMLS ranged from 0 to 30 mm. It was greater than 5 mm in images of 17 patients (32%). In 49 images (92%), the difference between hMLS and aMLS was <1 mm. To detect MLS >5 mm, our algorithm achieved sensitivity of 94% and specificity of 100%. For mortality prediction, aMLS was no worse than hMLS. CONCLUSION: In summary, automated MLS was accurate and predicted outcome as well as that measured manually. This approach might be useful in constructing a fully automated computer-assisted diagnosis system.

Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography.

Physicians evaluate computed tomography (CT) of the brain to quantitatively and qualitatively identify various types of intracranial hematomas for patients with neurological emergencies. We propose a novel method that can perform this task in a totally automatic fashion, based on a multiresolution binary level set method. The skull regions are segmented in downsized images generated with a maximum filter. The intracranial regions are located using the average gray levels and connectivity. These regions compose the regions of interest (ROIs) for segmenting the hematoma from the normal brain. The gray levels of the voxels within these ROIs are generated with an averaging filter in a multiresolution fashion. After identifying the candidate hematoma voxels using adaptive thresholds and connectivity, binary level set algorithm is applied repeatedly until the original resolution is reached. We apply our method to non-volumetric non-contrast CT images of 15 surgically proven intracranial hematomas and the results were quantitatively evaluated by a human expert. The correlation coefficient between the volumes measured manually and automatically is 0.97. The overlap metrics ranged from 0.97 to 0.74, with an average of 0.88. The average precision and recall are 0.89 and 0.87, respectively. We use decision rules to classify these hematomas and were able to make correct diagnoses in all cases.

Automatic recognition of midline shift on brain CT images.

Midline shift is one of the most important quantitative features clinicians use to evaluate the severity of brain compression by various pathologies. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain and the cerebrospinal fluid spaces. This paper proposes a novel method to identify the deformed midline according to the above hypothesis. In this model, the deformed midline is decomposed into three segments: the upper and the lower straight segments representing parts of the tough dura mater separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline pixels, to simulate maximal bilateral symmetry. A genetic algorithm is applied to derive the optimal values of the control points of the Bezier curve. Our algorithm was evaluated on pathological images from 81 consecutive patients treated in a single institute over a period of one year. Our algorithm is able to recognize the deformed midlines in 65 (80%) of the patients with an accuracy of 95%, making it a useful tool for clinical decision-making.

A multiresolution binary level set method and its application to intracranial hematoma segmentation.

We propose a multiresolution binary level set method for image segmentation. The binary level set formulation is based on the Song-Chan algorithm, which cannot compute the edge length when the margin of the image is irregular. We modify the edge length approximation so that it can work everywhere in a single-connected image, make it suitable to segment objects at any position, especially near the margin of the image. For multiresolution processing, we use image pyramids. The binary level set method works on images with reduced resolution and size. A point at the image with lower resolution is processed instead of a block or a strip at the original resolution, therefore improving the efficiency. Our multiresolution binary level set method is applied to segmentation of intracranial hematomas on brain CT slices. Segmentation of epidural and subdural hematomas, which have been not done previously, is performed successfully in seconds with results comparable to human experts.

Normal pressure hydrocephalus: cerebral hemodynamic, metabolism measurement, discharge score, and long-term outcome.

BACKGROUND: Regional CBF study has been reported effective in the selection of patient with NPH. However, controversial outcome had been reported. We sought to determine if the combination of rCBF measurement, cerebrovascular reactivity, and regional metabolism were positive predictors of shunt responsiveness in NPH syndrome. METHODS: Twenty-eight patients with clinical diagnosis of NPH were enrolled to study their rCBF in CSWM before and after the ACT challenge test, the regional CSWM metabolism by MRSI, and the clinical grading by the CSRIH defined by the Ministry of Health and Welfare of Japan in 1996. All the patients received VP shunting procedure by the same neurosurgical team. The pre- and postoperative clinical conditions were recorded. A patient was considered as "responder" when the patient's CSRIH total score decreased by one or more points. Patients have been followed for a median duration of 40.6 months (range, 28-67 months) with Karnofsky performance scale. RESULTS: Twenty-three responders had significant improvement after VP shunting in clinical grading; 5 nonresponders were stationary after VP shunting. During the 3 years of follow-up, 5 of the 28 patients died, the other 6 were lost to follow-up (including telephone contact), and 3 had progressive deterioration. The prechallenge rCBF decreased in all the 28 subjects. In the 23 responders, the rCBF after challenge were greater than 20 mL/min per 100 g (P=.008), had a significantly better CRC in the anterior CSWM than the nonresponders (1.40 vs 1.06), and had normal NAA/Cre ratio in the anterior, middle, and posterior CSWM in MRSI study. In those nonresponders, the NAA/Cre ratio was less than 0.8 in at least 2 regions of CSWM, and in 23 patients with symptoms other than ataxia (dementia, incontinence), the NAA/Cre ratio was less than 1.5 at frontal CSWM area. Discharge CSRIH scale was well correlated with CRC (P<.03), the average ACT challenge CBF (P<.005), and the average rCBF (P<.02). There was a statistically significant correlation between discharge CSRIH scale and follow-up performance at 3 months (P=.017), 2 years (P=.018), and 3 years (P=.038). CONCLUSION: Measurement of cerebrovascular hemodynamic and regional metabolism can be a good predictor of outcome after shunting in patients with NPH. Magnetic resonance spectroscopic imaging at frontal CSWM has good correlation with clinical symptoms. After VP shunting procedure, the discharge CSRIH scale is a good predictor of long-term outcome of patients with NPH.

Automatic MRI meningioma segmentation using estimation maximization.

With the advancement of the imaging facility and image processing technique, computer assisted surgical planning and image guided technology have become increasingly used in neurosurgery. For MRI has the characteristic of multi-spectral image data., so knowledge-base techniques is widely used in brain MRI segmentation. Here we recognize the location of the tumor automatically and provide an accurate result by Estimation Maximization method. Simultaneously, promote the efficiency of reading image as well.