Donald J. Peck

Time course of ADCW changes in ischemic stroke : Beyond the human eye!

BACKGROUND AND PURPOSE Using newly developed computerized image analysis, we studied the heterogeneity of apparent diffusion coefficient of water (ADCw) values in human ischemic stroke within 10 hours of onset. METHODS Echo-planar trace diffusion-weighted images from 9 patients with focal cortical ischemic stroke were obtained within 10 hours of symptom onset. An Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm was implemented to segment different tissue types with a series of DW images. ADCw maps were calculated from 4 DW images on a pixel-by-pixel basis. The segmented zones within the lesion were characterized as low, pseudonormal, or high, expressed as a ratio of the mean+/-SD of ADCw of contralateral noninvolved tissue. RESULTS The average ADCW in the ischemic stroke region within 10 hours of onset was significantly depressed compared with homologous contralateral tissue (626.6+/-76.8 versus 842.9+/-60.4x10(-6) mm2/s; P<0.0001). Nevertheless, ISODATA segmentation yielded multiple zones within the stroke region that were characterized as low, pseudonormal, and high. The mean proportion of low:pseudonormal:high was 72%:20%:8%. CONCLUSIONS Despite low average ADCW, computer-assisted segmentation of DW MRI detected heterogeneous zones within ischemic lesions corresponding to low, pseudonormal, and high ADCw not visible to the human eye. This supports acute elevation of ADCw in human ischemic stroke and, accordingly, different temporal rates of tissue evolution toward infarction.

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.

The temporal evolution of MRI tissue signatures after transient middle cerebral artery occlusion in rat

We have developed a multiparameter magnetic resonance imaging (MRI) cluster analysis model of acute ischemic stroke using T2 relaxation times and the diffusion coefficient of water (ADCw). To test the ability of this model to predict cerebral infarction, male Wistar rats (n = 7) were subjected to 2 h of transient middle cerebral artery (MCA) occlusion, and diffusion and T2 weighted MRI were performed on these rats before, during and up to 7 days after MCA occlusion. MRI tissue signatures, specified by values of ADCw and T2 were assigned to tissue histopathology. Significant correlations were obtained between MRI signatures at different time points and histopathologic measurements of lesion area obtained at 1 week. In addition, we compared the temporal evolution of MRI tissue signatures to a separate population of animals at which histological data were obtained at select times of reperfusion. A significant shift (p < or = 0.05) within signatures reflecting tissue histopathology was demonstrated as the ischemic lesion evolved over time. Our data suggest, that the MRI signatures are associated with the degree of ischemic cell damage. Thus, the tissue signature model may provide a noninvasive means to monitor the evolution of ischemic cell damage and to predict final outcome of ischemic cell damage.

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. J. Magn. Reson. Imaging 2000;11:425–437.

A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences

The performance of the eigenimage filter is compared with those of several other filters as applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. The conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.

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 Analsis 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.

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.

An exposure indicator for digital radiography

Digital radiographic imaging systems, such as those using photostimulable storage phosphor, amorphous selenium, amorphous silicon, CCD, and MOSFET technology, can produce adequate image quality over a much broader range of exposure levels than that of screen/film imaging systems. In screen/film imaging, the final image brightness and contrast are indicative of over- and underexposure. In digital imaging, brightness and contrast are often determined entirely by digital postprocessing of the acquired image data. Overexposure and underexposures are not readily recognizable. As a result, patient dose has a tendency to gradually increase over time after a department converts from screen/film-based imaging to digital radiographic imaging. The purpose of this report is to recommend a standard indicator which reflects the radiation exposure that is incident on a detector after every exposure event and that reflects the noise levels present in the image data. The intent is to facilitate the production of consistent, high quality digital radiographic images at acceptable patient doses. This should be based not on image optical density or brightness but on feedback regarding the detector exposure provided and actively monitored by the imaging system. A standard beam calibration condition is recommended that is based on RQA5 but uses filtration materials that are commonly available and simple to use. Recommendations on clinical implementation of the indices to control image quality and patient dose are derived from historical tolerance limits and presented as guidelines.

An exposure indicator for digital radiography: AAPM Task Group 116 (Executive Summary)

Digital radiographic imaging systems, such as those using photostimulable storage phosphor, amorphous selenium, amorphous silicon, CCD, and MOSFET technology, can produce adequate image quality over a much broader range of exposure levels than that of screen/film imaging systems. In screen/film imaging, the final image brightness and contrast are indicative of over- and underexposure. In digital imaging, brightness and contrast are often determined entirely by digital postprocessing of the acquired image data. Overexposure and underexposures are not readily recognizable. As a result, patient dose has a tendency to gradually increase over time after a department converts from screen/film-based imaging to digital radiographic imaging. The purpose of this report is to recommend a standard indicator which reflects the radiation exposure that is incident on a detector after every exposure event and that reflects the noise levels present in the image data. The intent is to facilitate the production of consistent, high quality digital radiographic images at acceptable patient doses. This should be based not on image optical density or brightness but on feedback regarding the detector exposure provided and actively monitored by the imaging system. A standard beam calibration condition is recommended that is based on RQA5 but uses filtration materials that are commonly available and simple to use. Recommendations on clinical implementation of the indices to control image quality and patient dose are derived from historical tolerance limits and presented as guidelines.

ACR–AAPM–SIIM Technical Standard for Electronic Practice of Medical Imaging

This technical standard has been revised by the American College of Radiology (ACR), the American Association of Physicists in Medicine (AAPM), and the Society for Imaging Informatics in Medicine (SIIM). For the purpose of this technical standard, the images referred to are those that diagnostic radiologists would normally interpret, including transmission projection and cross-sectional X-ray images, ionizing radiation emission images, and images from ultrasound and magnetic resonance modalities. Research, nonhuman and visible light images (such as dermatologic, histopathologic, or endoscopic images) are out of scope, though many of the same principles are applicable. Increasingly, medical imaging and patient information are being managed using digital data during acquisition, transmission, storage, display, interpretation, and consultation. The management of these data during each of these operations may have an impact on the quality of patient care. This technical standard is applicable to any system of digital image data management, from a single-modality or single-use system to a complete picture archiving and communication system (PACS) to the electronic transmission of patient medical images from one location to another for the purposes of interpretation and/or consultation. It defines goals, qualifications of personnel, equipment guidelines, specifications of data manipulation and management, and quality control and quality improvement procedures for the use of digital image data that should result in high-quality radiological care. A glossary of commonly used terminology (Appendix A) and a reference list are included. In all cases for which an ACR practice guideline or technical standard exists for the modality being used or the specific examination being performed, that practice guideline or technical standard will continue to apply when digital image data management systems are used. Digital mammography is outside the scope of this document. For further information, see the ACR–AAPM–SIIM Practice Guideline for Determinants of Image Quality in Digital Mammography. The goals of the electronic practice of medical imaging include, but are not limited to: Initial acquisition or generation and recording of accurately labeled and identified image data. Transmission of data to an appropriate storage medium from which it can be retrieved for display for formal interpretation, review, and consultation. Retrieval of data from available prior imaging studies to be displayed for comparison with a current study. Transmission of data to remote sites for consultation, review, or formal interpretation. Appropriate compression of image data to facilitate transmission or storage, without loss of clinically significant information. Archiving of data to maintain accurate patient medical records in a form that: May be retrieved in a timely fashion Meets applicable facility, state, and federal regulations Maintains patient confidentiality Promoting efficiency and quality improvement. Providing interpreted images to referring providers. Supporting telemedicine by making medical image consultations available in medical facilities without on-site medical imaging support. Providing supervision of off-site imaging studies. Providing timely availability of medical images, image consultation, and image interpretation by: Facilitating medical image interpretations in on-call situations Providing subspecialty support as needed. Enhancing educational opportunities for practicing radiologists. Minimizing the occurrence of poor image quality. Appropriate database management procedures applicable to all of the above should be in place.