Murray H. Loew

Direct magnetic resonance detection of neuronal electrical activity

Present noninvasive neuroimaging methods measure neuronal activity indirectly, via either cerebrovascular changes or extracranial measurements of electrical/magnetic signals. Recent studies have shown evidence that MRI may be used to directly and noninvasively map electrical activity associated with human brain activation, but results are inconclusive. Here, we show that MRI can detect cortical electrical activity directly. We use organotypic rat-brain cultures in vitro that are spontaneously active in the absence of a cerebrovascular system. Single-voxel magnetic resonance (MR) measurements obtained at 7 T were highly correlated with multisite extracellular local field potential recordings of the same cultures before and after blockade of neuronal activity with tetrodotoxin. Similarly, for MR images obtained at 3 T, the MR signal changed solely in voxels containing the culture, thus allowing the spatial localization of the active neuronal tissue.

Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis

The vast majority of bladder cancers originate within 600 microm of the tissue surface, making optical coherence tomography (OCT) a potentially powerful tool for recognizing cancers that are not easily visible with current techniques. OCT is a new technology, however, and surgeons are not familiar with the resulting images. Technology able to analyze and provide diagnoses based on OCT images would improve the clinical utility of OCT systems. We present an automated algorithm that uses texture analysis to detect bladder cancer from OCT images. Our algorithm was applied to 182 OCT images of bladder tissue, taken from 68 distinct areas and 21 patients, to classify the images as noncancerous, dysplasia, carcinoma in situ (CIS), or papillary lesions, and to determine tumor invasion. The results, when compared with the corresponding pathology, indicate that the algorithm is effective at differentiating cancerous from noncancerous tissue with a sensitivity of 92% and a specificity of 62%. With further research to improve discrimination between cancer types and recognition of false positives, it may be possible to use OCT to guide endoscopic biopsies toward tissue likely to contain cancer and to avoid unnecessary biopsies of normal tissue.

Near Infrared Reflectance Imaging Spectroscopy to Map Paint Binders In Situ on Illuminated Manuscripts

In recent years, visible and near infrared (NIR) reflectance imaging spectroscopy, i.e., the collection of contiguous calibrated spectral images to provide the reflectance spectra for each pixel of the scene, has been applied to the study of old master paintings. Its application to the study of lightsensitive works of art, such as illuminated manuscripts, has been limited to partial pigment identification using visible electronic transitions. Here we show the potential of NIR imaging spectroscopy in the 1000 to 2500 nm (10 000– 4000 cm ) spectral region to map and identify paint binders by utilizing vibrational features associated with methylenic, CH and amide functional groups. This is demonstrated by using a novel hyperspectral NIR imaging spectrometer (1000– 2500 nm, 4.4 nm resolution) to map the use of a fat-containing paint binder (likely egg yolk) for certain compositional elements of a 15th century manuscript leaf. The use of a fatcontaining binder for manuscript illumination is surprising in itself since egg white and gum Arabic (protein and polysaccharides) are historically considered to be the binders preferred by illuminators. This study offers the opportunity to map paint binders in situ on works of art, at a macroscopic scale, for the first time. While analytical techniques using micro-samples (mg) taken from art objects can provide the most accurate identification of artists materials, there is a preference for in situ methods. Among them, site-specific tools such as X-ray fluorescence (XRF), Raman spectroscopy and fiber optics reflectance spectroscopy (FORS) can identify pigments. Production of material maps on the macroscopic scale (entire artwork) has been limited to date, while mapping on the microscopic scale has progressed rapidly and has proven to be useful. Techniques that have been used include scanning electron microscope–energy dispersive spectroscopy (SEMEDS), mid-IR (FT-IR), Raman, XRF, and luminescence spectroscopy using traditional as well as synchrotron sources. Given the 2-D nature of many works of art, the spatial information derived from macroscopic maps can provide important clues about an artist s working methods and help guide conservation choices. Hence, there is interest in the development of macroscopic mapping methods, which utilize existing analytical in situ methods such as XRF and X-ray diffraction, reflectance and luminescence spectroscopy. These methods not only provide the identification and mapping of artist s materials, but also other information such as compositional changes and layering of paint. Unlike XRF mapping, which provides elemental information and is thus limited to being used to infer inorganic pigments, X-ray diffraction and reflectance imaging spectroscopy provide information on the molecular structure of the pigment. Reflectance spectroscopy offers the capability to map also organic materials, such as dyes, and recently a plasticizer in a PVC object. To date most studies using reflectance imaging spectroscopy have relied on electronic transitions in the visible range alone for pigment identification and have been only partially successful. Improved results have been obtained by extending into the NIR range (750–1700 nm) in order to collect vibrational band overtones and combinations associated with hydroxy inorganic pigments. Extending the spectral range to 2500 nm to collect vibrational features associated with carbonate functional groups would be a further improvement. While progress has been made on mapping artists inorganic materials, the mapping of organic materials— paint binders in particular—has succeeded only on the microscopic scale utilizing mid-IR microscopes (650 to ca. 4000 cm ). While remote-sensing hyperspectral imaging cameras operating in the mid-IR exist, such instruments require exotic infrared focal planes and cooling to temper[*] Dr. P. Ricciardi, Dr. J. K. Delaney, M. Facini, Dr. S. Lomax National Gallery of Art, 6th St. and Constitution Ave. NW Washington, D.C. 20001 (USA) E-mail: j-delaney@nga.gov

A comparison of the wavelet and short-time fourier transforms for Doppler spectral analysis

Doppler spectrum analysis provides a non-invasive means to measure blood flow velocity and to diagnose arterial occlusive disease. The time-frequency representation of the Doppler blood flow signal is normally computed by using the short-time Fourier transform (STFT). This transform requires stationarity of the signal during a finite time interval, and thus imposes some constraints on the representation estimate. In addition, the STFT has a fixed time-frequency window, making it inaccurate to analyze signals having relatively wide bandwidths that change rapidly with time. In the present study, wavelet transform (WT), having a flexible time-frequency window, was used to investigate its advantages and limitations for the analysis of the Doppler blood flow signal. Representations computed using the WT with a modified Morlet wavelet were investigated and compared with the theoretical representation and those computed using the STFT with a Gaussian window. The time and frequency resolutions of these two approaches were compared. Three indices, the normalized root-mean-squared errors of the minimum, the maximum and the mean frequency waveforms, were used to evaluate the performance of the WT. Results showed that the WT can not only be used as an alternative signal processing tool to the STFT for Doppler blood flow signals, but can also generate a time-frequency representation with better resolution than the STFT. In addition, the WT method can provide both satisfactory mean frequencies and maximum frequencies. This technique is expected to be useful for the analysis of Doppler blood flow signals to quantify arterial stenoses.

The quadcode and its arithmetic

The quadcode is a hierarchical data structure for describing digital images. It has the following properties: (1) straightforward representation of dimension, size, and the relationship between an image and its subsets; (2) explicit description of geometric properties, such as location, distance, and adjacency; and (3) ease of conversion from and to raster representation. The quadcode has applications to computer graphics and image processing because of its ability to focus on selected subsets of the data and to allow utilization of multiple resolutions in different parts of the image. A related approach is the quadtree. Samet recently presented a thorough survey of the literature in that field [7]. Gargantini [2] and Abel and Smith [1] presented linear quadtrees and linear locational keys that are efficient labeling techniques for quadtrees. In those papers the geometric concepts of the image are discussed by using the tree as an interpretive medium, and the approaches and procedures are based on traversal of the nodes in the tree. In this paper we present the quadcode system, which is a direct description of the image, and discuss the geometric concepts in terms of the coded images themselves.

Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy

Effective radioimmunotherapy may depend on a priori knowledge of the radiation absorbed dose distribution obtained by trace imaging activities administered to a patient before treatment. A new, fast, and effective treatment planning approach is developed to deal with a heterogeneous activity distribution. Calculation of the three-dimensional absorbed dose distribution requires convolution of a cumulated activity distribution matrix with a point-source kernel; both are represented by large matrices (64 x 64 x 64). To reduce the computation time required for these calculations, an implementation of convolution using three-dimensional (3-D) fast Hartley transform (FHT) is realized. Using the 3-D FHT convolution, absorbed dose calculation time was reduced over 1000 times. With this system, fast and accurate absorbed dose calculations are possible in radioimmunotherapy. This approach was validated in simple geometries and then was used to calculate the absorbed dose distribution for a patients tumor and a bone marrow sample.

Estimating fractal dimension with fractal interpolation function models

Fractal dimension (FD) is a feature which is widely used to characterize medical images. Previously, researchers have shown that FD separates important classes of images and provides distinctive information about texture. The authors analyze limitations of two principal methods of estimating FD: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. The authors also present background information on the use of fractal interpolation function (FIF) models to estimate FD of data which can be represented in the form of a function. They present a new method of estimating FD in which multiple FIF models are constructed. The mean of the FDs of the FIF models is taken as the estimate of the FD of the original data. The standard deviation of the FDs of the FIF models is used as a confidence measure of the estimate. The authors demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, they generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods.

A new approach to reduce the 'blocking effect' of transform coding (image coding)

A combined-transform coding (CTC) scheme to reduce the blocking effect of conventional block transform coding and hence to improve the subjective performance is presented. The scheme is described, and its information-theoretic properties are discussed. Computer simulation results for a chest X-ray image are presented. The CTC scheme, the JPEG baseline scheme, and the conventional discrete Walsh-Hadamard transform (DWHT) are compared to demonstrate the performance improvement for the CTC scheme. The advantages of the CTC scheme include no ringing effect as there is no error propagation across the boundary, no additional computation, and distortion always held within a certain level. >

Analytical solutions for time-resolved fluorescence lifetime imaging in a turbid medium such as tissue.

An analytical solution is developed to quantify a site-specific fluorophore lifetime perturbation that occurs, for example, when the local metabolic status is different from that of surrounding tissue. This solution may be used when fluorophores are distributed throughout a highly turbid media and the site of interest is embedded many mean scattering distances from the source and the detector. The perturbation in lifetime is differentiated from photon transit delays by random walk theory. This analytical solution requires a priori knowledge of the tissue-scattering and absorption properties at the excitation and emission wavelengths that may be obtained from concurrent time-resolved reflection measurements. Additionally, the solution has been compared with the exact, numerically solved solution. Thus the presented solution forms the basis for practical lifetime imaging in turbid media such as tissue.

Discrimination of MR images of breast masses with fractal-interpolation function models*

RATIONALE AND OBJECTIVES The authors evaluated the feasibility of using statistical fractal-dimension features to improve discrimination between benign and malignant breast masses at magnetic resonance (MR) imaging. MATERIALS AND METHODS The study evaluated MR images of 32 malignant and 20 benign breast masses from archived data at the University of Pennsylvania Medical Center. The test set included four cases that were difficult to evaluate on the basis of border characteristics. All diagnoses had been confirmed at excisional biopsy. The fractal-dimension feature was computed as the mean of a sample space of fractal-dimension estimates derived from fractal interpolation function models. To evaluate the performance of the fractal-dimension feature, the classification effectiveness of five expert-observer architectural features was compared with that of the fractal dimension combined with four expert-observer features. Feature sets were evaluated with receiver operating characteristic analysis. Discrimination analysis used artificial neural networks and logistic regression. Robustness of the fractal-dimension feature was evaluated by determining changes in discrimination when the algorithm parameters were perturbed. RESULTS The combination of fractal-dimension and expert-observer features provided a statistically significant improvement in discrimination over that achieved with expert-observer features alone. Perturbing selected parameters in the fractal-dimension algorithm had little effect on discrimination. CONCLUSION A statistical fractal-dimension feature appears to be useful in distinguishing MR images of benign and malignant breast masses in cases where expert radiologists may have difficulty. The statistical approach to estimating the fractal dimension appears to be more robust than other fractal measurements on data-limited medical images.