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Representative Recent Research in Medical Imaging

Murray Loew

Biomedical Engineering Program

Department of Electrical and Computer Engineering

June 27, 2007

2With R. Brem, M.D., GWU, F. Razjouyan, B.S. student, and I. Kopriva, Ph.D., visiting scientist

Multispectral Infrared Imaging of the Breast

Thermal signatures of cancer can be visualized in the infrared (IR). Using several wavelengths (middle pair of images) provides additional information. Measures of asymmetry aid detection of anomalies.

Top: initial IR image (left) and after 10 minutes’ thermal equilibration. Middle: mid-wave (left) and long-wave IR images. Bottom: unsupervised classification of images; blue is low probability of cancer; red is high.

Each band of IR provides its own image; the resulting set is amenable to analysis by numerous methods

Independent component analysis allows the identification of “sources”within the breast that explain the thermal appearance and are likely to contribute to diagnosis. Studies will be conducted at GW’s Breast Clinic.

Multispectral Infrared Imaging of the Breast

Thermal signatures of cancer can be visualized in the infrared (IR). Using several wavelengths (middle pair of images) provides additional information. Measures of asymmetry aid detection of anomalies.

Top: initial IR image (left) and after 10 minutes’ thermal equilibration. Middle: mid-wave (left) and long-wave IR images. Bottom: unsupervised classification of images; blue is low probability of cancer; red is high.

Each band of IR provides its own image; the resulting set is amenable to analysis by numerous methods

Independent component analysis allows the identification of “sources”within the breast that explain the thermal appearance and are likely to contribute to diagnosis. Studies will be conducted at GW’s Breast Clinic.

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Thermo-elastic modeling of breast tissue with gravity-induced deformation

We can model the shape of the breast under realistic conditions of position and orientation

And can then model the thermal state of the tissue – in 3-d – under the influence of a source (e.g., a tumor) at an arbitrary position

Strain Temperature

This information is useful for diagnosis, prognosis, and therapy

With L. Jiang, doctoral student

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Applied Stereology: Tradeoffs in CT Lung Volume Measurement Using Subsets of Slices

When a patient is examined at different times or with different protocols, how can we know whether the observed differences in a volume estimate are due to the patient, the protocol, or both? Specifically, we would like to know what is the smallest difference in lung volume that can be computed reliably from two sets of CT data, acquired by varying the number and thicknesses of the slices.

A set of CT image slices taken to estimate lung

volume

First SectionRandomly

Placed

Tm

Object

Cavalieri method for estimating volume

Our results show that thick-slice CT images can be as effective as those with thin slicesin the estimation of lung volume. Patients in remote areas often can thus avoid a trip to a regional CT center by using images taken on a less-capable machine.

Applied Stereology: Tradeoffs in CT Lung Volume Measurement Using Subsets of Slices

When a patient is examined at different times or with different protocols, how can we know whether the observed differences in a volume estimate are due to the patient, the protocol, or both? Specifically, we would like to know what is the smallest difference in lung volume that can be computed reliably from two sets of CT data, acquired by varying the number and thicknesses of the slices.

A set of CT image slices taken to estimate lung

volume

First SectionRandomly

Placed

Tm

Object

Cavalieri method for estimating volume

Our results show that thick-slice CT images can be as effective as those with thin slicesin the estimation of lung volume. Patients in remote areas often can thus avoid a trip to a regional CT center by using images taken on a less-capable machine.

With J. Reinhardt, Ph.D., U. of Iowa, and Z. Markowitz, doctoral student

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Finding Salient Features in Mammograms

We have identified measures of salience that are effective at automatically identifying regions that draw the attention of mammographers. This has implications for computer-aided diagnosis and for image compression for transmission and storage. Ellipses on two subjects’ mammograms indicate fixations of human observers; dashed lines show ground truth location. Our technique produces salience maps (lower sets) for the CC and MLO views; the maps were successful in identifying the lesions and minimizing false positives.

Finding Salient Features in Mammograms

We have identified measures of salience that are effective at automatically identifying regions that draw the attention of mammographers. This has implications for computer-aided diagnosis and for image compression for transmission and storage. Ellipses on two subjects’ mammograms indicate fixations of human observers; dashed lines show ground truth location. Our technique produces salience maps (lower sets) for the CC and MLO views; the maps were successful in identifying the lesions and minimizing false positives.

With H. Kundel, M.D., U. of Pennsylvania, and P. Perconti, doctoral student

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Image Analysis: Insulin Granule ID and Tracking

A beta cell from the pancreatic islets; insulin granules are the black cores in the circles. How many are touching the boundary of the cell?

Completely automatic methods have been developed for identifying insulin granules in β- cell photomicrographs. This replaces the tedious and inaccurate manual methods and makes possible an estimation of the temporal response function – movement of granules toward the plasma membrane – and also their number, location, and morphology.

A sample of a β-cell after processing. Red: single granule core and halo; blue: compound granule; yellow: halo only.

Image Analysis: Insulin Granule ID and Tracking

A beta cell from the pancreatic islets; insulin granules are the black cores in the circles. How many are touching the boundary of the cell?

Completely automatic methods have been developed for identifying insulin granules in β- cell photomicrographs. This replaces the tedious and inaccurate manual methods and makes possible an estimation of the temporal response function – movement of granules toward the plasma membrane – and also their number, location, and morphology.

A sample of a β-cell after processing. Red: single granule core and halo; blue: compound granule; yellow: halo only.

With G. Sharp, Ph.D. and S. Straub, Ph.D., Cornell U., and T. McClanahan, doctoral student

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Other current work

• Optical coherence tomography (OCT) of the bladder (with J. Zara, Ph.D., ECE; M. Manyak, M.D., Urology; A. Lingley, doctoral student)

• Task-based measures of image quality for image compression (with D. Li, doctoral student)

• Diffusion-tensor imaging in MRI (with P. Basser, Ph.D., NIH; R. Freidlin, doctoral student)