An uncommon cause of vaporiser failure.
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Anesthesia, Inhalation↗
Biomedical subjects
D J Peck
Publications and source records attributed to D J Peck.
Feature space analysis: effects of MRI protocols.
We present a method for exploring the relationship between the image segmentation results obtained by an optimal feature space method and the MRI protocols used. The steps of the work accomplished are as follows. (1) Patients with brain tumors were imaged on a 1.5 T General Electric Signa MRI System using multiple protocols (T1 and T2-weighted fast spin-echo and FLAIR). T1-weighted images were acquired before and after gadolinium injection. (2) Image volumes were co-registered, and images of a slice through the center of the tumor were selected for processing. (3) For each patient, several image sets were defined by selecting certain MR images (e.g., 4T2's+ IT1, 4T2's+FLAIR, 2T2's+ 1T1). (4) Using each image set, the optimal feature space was generated and images were segmented into normal tissues and different tumor zones. (5) Segmentation results obtained using the different MRI sets were compared. Based on the analysis results from 27 image sets, we found that the locations of the clusters for the tumor zones and their corresponding regions in the image domain changed as a function of the MR images (MRI protocols) used. However, the segmentation results for the total lesion and normal tissues remained relatively unchanged.
Brain Neoplasms↗
Evaluation of a quality control phantom for digital chest radiography.
RATIONALE AND OBJECTIVES: To examine the effectiveness and suitability of a quality control (QC) phantom for a routine QC program in digital radiography. MATERIALS AND METHODS: The chest phantom consists of copper and aluminum cutouts arranged to resemble the appearance of a chest. Performance of the digital radiography (DR) system is evaluated using high and low contrast resolution objects placed in the "heart," "lung," and "subdiaphragm" areas of the phantom. In addition, the signal levels from these areas were compared to similar areas from clinical chest radiographs. RESULTS: The test objects included within the phantom were effective in assessing image quality except within the subdiaphragm area, where most of the low contrast disks were visible. Spatial resolution for the DR systems evaluated with the phantom ranged from 2.6 lp/mm to 4 lp/mm, falling within the middle of the line pair range provided. The signal levels of the heart and diaphragm regions relative to the lung region of the phantom were significantly higher than in clinical chest radiographs (0.67 versus 0.21 and 0.28 versus 0.10 for the heart and diaphragm regions, respectively). The heart-to-diaphragm signal level ratio, however, was comparable to those in clinical radiographs. CONCLUSION: The findings suggest that the attenuation characteristics of the phantom are somewhat different from actual chests, but this did not appear to affect the post-processing used by the imaging systems and usefulness for QC of these systems. The qualitative and quantitative measurements on the phantom for different systems were similar, suggesting that a single phantom can be used to evaluate system performance in a routine QC program for a wide range of digital radiography systems. This makes the implementation of a uniform QC program easier for institutions with a mixture of different digital radiography systems.
Humans↗
A model for multiparametric mri tissue characterization in experimental cerebral ischemia with histological validation in rat: part 1.
BACKGROUND AND PURPOSE: After stroke, brain tissue undergoes time-dependent heterogeneous histopathological change. These tissue alterations have MRI characteristics that allow segmentation of ischemic from nonischemic tissue. Moreover, MRI segmentation generates different zones within the lesion that may reflect heterogeneity of tissue damage. METHODS: A vector tissue signature model is presented that uses multiparametric MRI for segmentation and characterization of tissue. An objective (unsupervised) computer segmentation algorithm was incorporated into this model with the use of a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). The ability of the model to characterize ischemic tissue after permanent middle cerebral ischemia occlusion in the rat was tested. Multiparametric ISODATA measurements of the ischemic tissue were compared with quantitative histological characterization of the tissue from 4 hours to 1 week after stroke. RESULTS: The ISODATA segmentation of tissue identified a gradation of cerebral tissue damage at all time points after stroke. The histological scoring of ischemic tissue from 4 hours to 1 week after stroke on all the animals was significantly correlated with ISODATA segmentation (r=0.78, P<0.001; n=20) when a multiparametric (T2-, T1-, diffusion-weighted imaging) data set was used, less correlated (r=0.70, P<0.01; n=20) when a T2- and T1-weighted data set was used, and not correlated (r=-0.12, P>0.47; n=20) when only a diffusion-weighted imaging data set was used. CONCLUSIONS: Our data indicate that an integrated set of MRI parameters can distinguish and stage ischemic tissue damage in an objective manner.
Algorithms↗
Multiparametric MRI tissue characterization in clinical stroke with correlation to clinical outcome: part 2.
BACKGROUND AND PURPOSE: Multiparametric MRI generates different zones within the lesion that may reflect heterogeneity of tissue damage in cerebral ischemia. This study presents the application of a novel model of tissue characterization based on an angular separation between tissues obtained with the use of an objective (unsupervised) computer segmentation algorithm implementing a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). We test the utility of this model to identify ischemic tissue in clinical stroke. METHODS: MR parameters diffusion-, T2-, and T1-weighted imaging (DWI, T2WI, and T1WI, respectively) were obtained from 10 patients at 3 time points (30 studies) after stroke: acute (</=12 hours), subacute (3 to 5 days), and chronic (3 months). The National Institutes of Health Stroke Scale (NIHSS) was measured, and volumes were obtained from the ISODATA, DWI, and T2WI maps on patients at each time point. RESULTS: The acute (</=12 hours) multiparametric ISODATA volume was significantly correlated with the acute (</=12 hours) DWI (r=0.96, P<0.05; n=10) and chronic (3 months) T2WI volume (r=0.69, P<0.05; n=10). The ISODATA-defined tissue regions exhibited MR indices consistent with ischemic and/or infarcted tissue at each time point. The acute (</=12 hours) multiparametric ISODATA volumes were significantly correlated (r=0.82, P<0.009; n=10) with the final NIHSS score. In comparison, the acute (</=12 hours) DWI volumes were less correlated (r=0.77, P<0.05; n=10) and T2WI volume (</=12h) exhibited a marginal correlation (r=0.66, P<0.05; n=10) with the final NIHSS score. CONCLUSIONS: The integrated ISODATA approach to tissue segmentation and classification discriminated abnormal from normal tissue at each time point. The ISODATA volume was significantly correlated with the current MR standards used in the clinical setting and the 3-month clinical status of the patient.
Adult↗
Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, diffusion, and ADC MRI parameters and histopathological validation.
This study presents histological validation of an objective (unsupervised) computer segmentation algorithm, the iterative self-organizing data analysis technique (ISODATA), for analysis of multiparameter magnetic resonance imaging (MRI) data in experimental focal cerebral ischemia. T2-, T1-, and diffusion (DWI) weighted coronal images were acquired from 4 to 168 hours after stroke on separate groups of animals. Animals were killed immediately after MRI for histological analysis. MR images were coregistered/warped to histology. MRI lesion areas were defined using DWI, apparent diffusion coefficient (ADC) maps, T2-weighted images, and ISODATA. The last techniques clearly discriminated between ischemia-altered and morphologically intact tissue. ISODATA areas were congruent and significantly correlated (r = 0.99, P < 0.05) with histologically defined lesions. In contrast, DWI, ADC, and T2 lesion areas showed no significant correlation with histologically evaluated lesions until subacute time points. These data indicate that multiparameter ISODATA methodology can accurately detect and identify ischemic cell damage early and late after ischemia, with ISODATA outperforming ADC, DWI, and T2-weighted images in identification of ischemic lesions from 4 to 168 hours after stroke.
Algorithms↗
Identification of cerebral ischemic lesions in rat using Eigenimage filtered magnetic resonance imaging.
An accurate noninvasive time-independent identification of an ischemic cerebral lesion is an important objective of magnetic resonance imaging (MRI). This study describes a novel application of a multiparameter MRI analysis algorithm, the Eigenimage (EI) filter, to experimental stroke. The EI is a linear filter that maximizes the projection of a desired tissue (ischemic tissue) while it minimizes the projection of undesired tissues (nonischemic tissue) onto a composite image called an eigenimage. Rats (n=26) were subjected to permanent middle cerebral artery occlusion. T2- and T1-weighted coronal MRI were acquired on separate groups of animals. The animals were immediately sacrificed after each imaging session for histopathological analysis of tissue at 4-8 h, 16-24 h, and 48-168 h after stroke onset. Lesion areas from MRI were defined using EI. The EI defined lesion areas were coregistered and warped to the corresponding histopathological sections. The ischemic lesion as defined by EI exhibited ischemic cell damage ranging from scattered acute cell damage to pan necrosis. Ischemic cellular damage was not detected in homologous contralateral hemisphere regions. EI lesion areas overlaid on histopathological sections were significantly correlated (r=0.92, p<0.05) acutely, (r=0.98, p<0.05) subacutely, and (r=0.99, p<0.05) chronically. These data indicate that EI methodology can accurately segment ischemic damage after MCA occlusion from 4-168 h after stroke.
Algorithms↗
Registration and warping of magnetic resonance images to histological sections.
We present a method for coregistration and warping of magnetic resonance images (MRI) to histological sections for comparison purposes. This methodology consists of a modified head and hat surface-based registration algorithm followed by a new automated warping approach using nonlinear thin plate splines to compensate for distortions between the data sets. To test the methodology, 15 male Wistar rats were subjected to focal cerebral ischemia via permanent occlusion of the middle cerebral artery. The MRI images were acquired in separate groups of animals at 16-24 h (n = 9) and 48-168 h (n = 6) postocclusion. After imaging, animals were immediately sacrificed and hematoxylin- and eosin-stained brain sections were obtained for histological analysis. The MRI was coregistered and warped to histological sections. The MRI lesion areas were defined using the Eigenimage (EI) filter technique. The EI is a linear filter that maximizes the projection of a desired tissue (ischemic tissue) while it minimizes the projection of undesired tissues (nonischemic tissue) onto a composite image called an EI. When using coregistration without warping the MRI lesion area demonstrated poor correlation (r = 0.55, p > 0.01) with a percent difference between the two lesion areas of 22.5% +/- 10.8%. After warping, the MRI and histology had significant correlation (r = 0.97, p < 0.01) and a decreased percent difference of 5.56% +/- 4.31%. This methodology is simple and robust for coregistration and warping of MRI to histological sections and can be utilized in many applications for comparison of MRI to histological data.
Algorithms↗
A wandering nasal prong-a thing of risks and problems.
We describe an unusual complication of nasal continuous positive airway pressure (nCPAP) ventilation in a preterm low birth weight neonate being weaned from respiratory support. The tube used to administer nasal CPAP became dislodged from its metal connector whilst in the nasopharynx and slipped into the stomach. After waiting eight days the tube showed no signs of passing spontaneously through the gastrointestinal tract and retrieval was then successfully achieved by means of a 3.5 mm paediatric fibreoptic bronchoscope without complication.
Bronchoscopes↗
Feature space analysis of MRI.
This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre- and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.
Artifacts↗
Cerebral tumor volume calculations using planimetric and eigenimage analysis.
Volume determination in cerebral tumors requires accurate and reproducible segmentation. This task has been traditionally accomplished using planimetric methods which define the boundary of the lesion using thresholding and edge detection schemes. These methods lack accuracy and reproducibility when the contrast between the lesion and surrounding tissue is not maximized. Because of this limitation contrast agents are used providing reproducible results for the enhancing portion of the lesion. A novel approach for volume determination has been developed (eigenimage filter) which segments a desired feature (tissue type) from surrounding undesired features in a sequence of images. This method corrects for partial volume effects and has been shown to provide accurate and reproducible volume determinations. In addition, the eigenimage filter does not require the use of contrast and has the capability to segment a lesion into multiple regions. This allows different components of the lesion to be included and monitored in treatment. In this study planimetric methods and the eigenimage filter were compared for segmenting cerebral tumors and determining their volumes. The planimetric methods were reproducible in determining volumes for the enhancing portion of the lesion with interobserver percent differences < 8% and intraobserver percent differences < 4%. The eigenimage filter had interobserver percent differences < 7% and intraobserver percent differences < 3%. In the eigenimage procedure both the enhancing portion of the lesion as well as additional regions within the lesion were identified. Comparing the results obtained from the two methods demonstrated good agreement for presurgical studies (percent differences < 9%). When comparing postsurgical studies large differences were seen. In the postsurgical studies the eigenimage method allowed multiple regions to be followed in subsequent MRI and in two patients showed a volume change that suggested tumor recurrence more clearly. Since the amount of information obtained using the eigenimage filter may allow a more complete assessment of the lesion, it is suggested that it could improve the clinical evaluation of cerebral tumors.
Biophysical Phenomena↗
Anaesthetists, errors in drug administration and the law.
AIM: To document the problem of drug administration error in anaesthesia in New Zealand, with regard to: prevalence (in context of the total number of drugs that might be administered during an anaesthetist's career); preventative strategies; and anaesthetists' perceptions concerning the medicolegal environment prevailing in New Zealand. METHODS: A questionnaire was posted to a random sample of 75 New Zealand anaesthetists. Drug administrations per anaesthetic were counted on a random sample of anaesthetic records at Green Lane Hospital. Ten anaesthetists were asked the number of anaesthetics administered per year. RESULTS: Eighty-nine percent of 66 respondents reported at least one error of drug administration, and 12.5% had actually harmed patients. There was no relationship between any preventative strategy and frequency of error. All respondents were concerned about the possibility of manslaughter charges arising from a drug error; 57% thought the medicolegal environment in New Zealand impacted adversely on their practice; 83% thought it might impede the reporting of errors. There seems to be no definitive strategy for the elimination of drug error. CONCLUSION: Error is inherent in drug administration in anaesthesia, as it is in any complex human endeavour; therefore it is illogical to treat it as necessarily criminal. Instead, effort and resources should be aimed at rational initiatives to reduce this worrying problem.
Anesthesiology↗
Analysis of the evolution of focal cerebral ischemia in the rat using the eigenimage filter.
The eigenimage filter was used to evaluate the results of a MRI study of cerebral ischemia in a rat model. This linear filter segments a desired feature in an image sequence from other features which may interfere with its observation. The animals were imaged temporally, after occlusion of the middle cerebral artery, to investigate the evolution of the ischemic process. The temporal evolution of ischemia was evaluated by analysis of the "eigenimages," calculated T2 and T1 map images, and images for the angles between signature vectors defined in the eigenimage technique. The eigenimages and angle map images demonstrated an improved visibility of the lesion at all time points, as compared to the original images and T2 and T1 map images. The eigenimages also demonstrated signal intensity changes within the area of ischemia. These changes are speculated to be related to variations in local cerebral blood flow resulting in varying degrees of tissue damage. The eigenimage intensities and the angles between signature vectors demonstrated time-related changes similar to the T2 and T1 values. Since the eigenimage filter and angle calculations are not dependent upon physical models (like T2 and T1), and the errors associated with these models, they may be preferable as methods for tissue characterization.
Animals↗
Brain tumor segmentation and characterization by pattern analysis of multispectral NMR images.
A major problem in tumor treatment planning and evaluation is determination of the tumor extent. This paper presents a pattern analysis methodology for segmentation and characterization of brain tumors from multispectral NMR images. The proposed approach has been used in 15 clinical studies of cerebral tumor patients who have been scheduled for surgical biopsy and resection. The tissue biopsy results, obtained at specific spatial coordinates determined in the analysis, have been utilized to validate the methodology. It was found that in all cases the lesion had extended into normal tissue, at least to the location where the sample was taken. In most cases, the proposed method suggested that the lesion had extended several millimetres beyond the point from where the biopsy sample was taken. In some cases, the extent of the lesion into normal tissue was well beyond the boundary seen on T1- or T2-weighted images. It is concluded that the proposed approach indicates brain tumor infiltration more precisely than what is visualized in the original NMR images and therefore its utilization facilitates proper treatment planning for the cerebral tumor patients.
Biopsy↗
Percutaneous embolotherapy of lower gastrointestinal hemorrhage.
PURPOSE: To evaluate percutaneous embolotherapy in the treatment of lower gastrointestinal hemorrhage. MATERIALS AND METHODS: Twenty-one patients who underwent attempted percutaneous embolization for acute lower gastrointestinal bleeding between 1982 and 1997 were retrospectively studied. Hemorrhagic sites included jejunum (n = 4), ileum (n = 4), cecum (n = 4), and the remaining colon (n = 9). RESULTS: Embolization was not technically possible in four patients (19%). Hemostasis was achieved in 15 patients (71%) with prolonged hemostasis in 10 (48%). All embolizations distal to the cecum resulted in prolonged hemostasis. Three of four patients with jejunal bleeding had recurrent bleeding after apparent successful embolization. Only one of four cecal embolizations achieved prolonged cessation of bleeding. No ischemic complications were identified. CONCLUSION: Based on these data, it would appear that the risk of bowel ischemia/infarction in the lower gastrointestinal tract may not be as high as has been suggested. Two regions (cecum and proximal jejunum) were associated with poor results, suggesting these areas may not be as responsive to embolotherapy as other sites in the lower gastrointestinal tract.
Aged↗
A fast and accurate algorithm for volume determination in MRI.
The determination of volumes in clinical MRI studies are prohibitive because of the time required to compute an accurate volume. Techniques that speed up the calculation are prone to large errors which make most impractical for an accurate diagnosis. A linear filter, called the eigenimage filter, has been developed that separates a desired feature from other features which interfere with its observation in an image. Using the images produced by this technique (eigenimages), the amount of operator interaction required to calculate volumes are significantly reduced. The technique also has the ability to correct for partial volume averaging effects and as a result a more accurate volume can be determined. The technique was applied to a computer simulation and two phantom studies. The time required to calculate the volume was less than 1 min per slice and the errors in accuracy and reproducibility were less than 2% for all studies.
Algorithms↗