Trans-rectal near-infrared optical tomography reconstruction of a regressing experimental

tumor in a canine prostate by using the prostate shape profile synthesized from sparse 2-dimentional trans-rectal ultrasound images

Dhanashree Palande, Daqing PiaoSchool of Electrical and Computer Engineering,

Oklahoma State University, Stillwater, OK 74078, USA

Outline Objective• Methods• Results• Conclusion and future work

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Objective• Near infrared(NIR) optical imaging:

– well suited for non-invasive quantification of hemoglobin oxygen saturation(StO2)

– provides unique information regarding optical properties

• Limitation of NIR: – low spatial resolution due to high scattering in

tissue• Solution:

– compensate optical imaging with spatial prior information extracted from high resolution trans-rectal ultrasound (TRUS) images to improve the reconstruction outcome of trans-rectal DOT

– obtain a 3D prostate profile from 2D TRUS images using segmentation which is used as a structural spatial prior in optical tomography reconstruction

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Outline• Objective Methods• Results• Conclusion and future work

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NIR Optical Tomography(DOT) • Non-invasive imaging technique: aims to

reconstruct images of tissue function and physiology

• Biological tissue is highly scattering at NIR wavelengths (650-900 nm)

• Also known as diffuse optical tomography(DOT)• NIR light is applied through optical fibers

positioned to surface of the tissue• Emergent light is measured at other locations on

the same tissue surface • NIR optical tomography along with reconstruction

algorithm, produces images of tissue physiology for detection and characterization of malignancy

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HbT and StO2 measurement• In the range 700-900nm, absorption of water is

much lower than that of oxygenated hemoglobin and deoxygenated hemoglobin

• Multi-wavelength data:– Rendered extracting oxygen saturation and hemoglobin concentration.– 705 nm, 785 nm, 808 nm

• Absorption coefficients recovered at 3 specific bands are:

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HbT and StO2 measurement• They are used to calculate HbO and Hb as

Where, was matrix of molar extinction coefficients

• Total hemoglobin: HbT= HbO+Hb (in milliMole)

• Oxygen saturation: StO2=HbO/HbT x 100 (in %)

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NIR Reconstruction Geometry• Outer rectangular mesh:

– equivalent to tissue surrounding the prostate– Required to match NIR reconstruction geometry

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The Forward Model• The technique to determine what a given sensor

would measure in given environment by using theoretical equations for sensor response

• Diffusion approximation in frequency domain

Where : absorption coefficient: reduced scattering coefficient : isotropic source term : photon fluence rate at position r and modulation frequency : diffusion coefficient : speed of light in medium

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The Inverse Model• Using the results of actual observations to infer

the values of the parameters characterizing the system under investigation.

• Goal: recovery of optical properties at each spatial element

• Tikhonov minimization: : measured fluence at tissue surface : calculated data using forward solver

Where, NM: number of measurements from imaging device

NN: number of spatial elements : regularization parameter : initial estimate of optical properties

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The Inverse Model• The minimization with respect to μ in equation

: Jacobian matrix, JUsing linear approximation and solving it as iterative scheme,

Where, : update of optical properties: data-model misfit at current iteration I I: identity matrix

Slight modification gives,

Where, and 11

The Inverse Model• NIRFAST is used for inverse problem solving

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TRUS Images of a Canine Prostate• A canine prostate was used for study

• Transmissible Venereal Tumor(TVT) cells was injected in right lobe of a prostate

• Dog was monitored over the 63-days period, at weekly intervals

• TRUS images were taken at:– Right edge plane– Right middle plane– Middle sagittal plane– Left middle plane– Left edge plane 13

TRUS Images of a Canine Prostate

Axial view Sagittal view

rectumrectum

Left lobeRight lobeCaudal side

Cranial side

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TRUS Images of a Canine Prostate

Axial view Sagittal view

rectumrectum

Left lobeRight lobeCaudal side

Cranial side

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TRUS Image Segmentation• TRUS image segmentation is challenging due to– Complexity in contrast – Image artifacts– Morphological features– Variation in prostate shape and size

• Manual contour tracking– Interactive program takes input

from user– Sagittal images segmented manually– Used as reference for 3D profile

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Approximating Axial Plane Positions

• We have set of sparsely acquired axial images• We use 3 images at cranial side, middle and

caudal side of the prostate• A program is written

to find approximate positions of these axialplanes

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3D Profiling of a Prostate• Interpolation

– Spline type of interpolation for smooth profile along the curve

– Using the points on axial contours– New data points are interpolated depending on

required mesh density

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Mesh Generation• Generation of a 3D mesh prostate profile using

Delaunay triangulation– Input: interpolated data points from 3D profile– Output: elements of all the tetrahedrons

• This mesh is now used as a spatial prior for NIR image reconstruction

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Prostate Mesh within Rectangular Mesh

• Mesh used for reconstruction

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Outline• Objective• Methods Results• Conclusion and future work

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Manually Segmented ImagesFor axial images

For sagittal images

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3D Prostate Profile3D profile of prostate

3D mesh profile of a prostate

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Rectangular Mesh With spatial prior Without spatial prior

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Reconstruction: Right LobeBaseline

With spatial prior Without spatial prior

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90

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90

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60 mm 60 mm

40 m

m

40 m

m

Ultrasound image

HbT

StO2

Reconstruction: Right LobeDay 49

With spatial prior Without spatial prior

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90

40

90

4060 mm 60 mm

40 m

m

40 m

m

Ultrasound image

HbT

StO2

Reconstruction: Right LobeDay 56

With spatial prior without spatial prior

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90

40

90

40

60 mm 60 mm

40 m

m

40 m

m

Ultrasound image

HbT

StO2

Right Lobe

40

40

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Right Lobe

(weeks) 7 8 9 7 8 9 (weeks)

Reconstruction: Left LobeDay 49

With spatial prior without spatial prior

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90

40

90

4060 mm60 mm

40 m

m

40 m

mUltrasound image

HbT

StO2

Left LobeDay 63

With spatial prior without spatial prior

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90

40

90

40

60 mm60 mm

40 m

m

40 m

m

Ultrasound image

HbT

StO2

Left Lobe

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90

10

90

10

40

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Outline• Objective• Methods• Results Conclusion and future work

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Conclusion and future work- Contribution of this work• This work explores combination of structural and

functional imaging for the study of prostate cancer• 3D prostate profile was generated from sparse 2D axial

TRUS images of a canine prostate• A prostate mesh developed was used a spatial prior to

NIR optical tomography for image reconstruction• Reconstructed images with and without prior were

compared qualitatively• This approach helps to interpret results for good

understanding of position of tumor lesion developed in prostate.

• To our knowledge, this is the first attempt to use TRUS guided structural spatial prior for image reconstruction of a prostate using NIR optical tomography

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Conclusion and future work-Future directions• Extending this study to other animals and

eventually to human prostates• Applying spectral prior information along with

spatial prior• To make this system work real-time, so as to be

used during clinical exams

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Thank youQuestions/suggestions

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