BIOS 2008CONFERENCE: ADVANCED BIOMEDICAL AND CLINICAL

DIAGNOSTIC SYSTEMS IV

D. VU, M. MUJAT, T. USTUN, D. HAMMER, D. FERGUSON, and N. IFTIMIA

PHYSICAL SCIENCES, INC., ANDOVER, MA

B. GOLDBERG, P. JILLELLA, and G. TEARNEYMASSACHUSETTS GENERAL HOSPITAL, BOSTON, MA

Spectral-domain low coherence interferometry

system for fine/core needle biopsy guidance

Content

Background and MotivationIntroduction of SD-LCI/OCT methodInstrumentation

Optical probes Electronic System

In Vitro Clinical StudyIn Vivo Study Tissue Differentiation AlgorithmResultsConclusions

About Fine/Core Needle Aspiration Biopsy

FNA biopsy: Percutaneous ("through the skin") procedure to sample fluid from a cyst or remove clusters of cells from a solid mass;

Core needle biopsy: Percutaneous ("through the skin") procedure to remove a small amount of tissue from a solid mass;

Fastest and easiest method for superficial biopsy, relatively inexpensive, and the results are rapidly available.

Motivation

Current FNAB Method20% non-diagnostic

palpation in 5-10% patients

Dependent on skills & experience of cytopathologist

60-93% sensitivity77-96% specificityUltra-sound, CT-scan ->

time consuming, expensive, additional trained staff.

Optical Guidance Inexpensive and

compact Integrated into existing

biopsy probe.No need for additional

trained staff – tissue differentiation is done automatically by the software – in real time.

Introduction to LCI

Coherent Source Low-Coherence Source

Mirror Displacement

Det

ecto

d S

ingl

a

/2

Mirror Displacement

Detector

lc~1/

Reference

Sample

Reference

Sample

Image courtesy of - J. De Boer, Wellman Center for Photomedicine

SDLCI Principle and its application in FNAB

InstrumentationOptical probes

SD-LCI integrated into FNAB probe

SD-OCT probe

InstrumentationOpto-electronic System

-512 ellements sensor (25 x 250 mm pixel size)

- Over 1500 A-lines speed.

-Custom optic design minimized aberrations and improved MTF.

System Specs:

Light Source: 1300 nmDepth Resolution: 15 micronsImaging Range: 3 mmCamera Line Rate: 2k lines/sec

Scope: - develop tissue differentiation algorithm.

Methods:- Over 70 breast tissue samples from 7 patients- Training set (35 samples) and model development- Validation set (40 samples)

Model: decision-tree classification algorithm

In Vitro Clinical Study

Data Processing AlgorithmIn

ten

sity

(a

.u.)

Depth (pixels)

Clustering of tissue types

Criteria for tissue differentiation

Results

Intensity Map Tissue Differentiation Map

Adipose Tissue

A = 87%, F = 11.3%, T = 1.7%Full depth scale ~ 1mm

Results

Intensity Map Tissue Differentiation Map

Adipose Tissue

A = 7.7%, F = 0%, T = 92.3%Full depth scale ~ 1mm

Results

Intensity Map Tissue Differentiation Map

Adipose Tissue

A = 67.1%, F = 32.9%, T = 0%Full depth scale ~ 1mm

Animal study

Goal: Test the correlation between the ex vivo-in vivo LCI measurements

Methods: Perform in vivo measurements on tumor and normal tissue locations Mark the in vivo locations Sacrifice the animal and perform ex vivo measurements on the marked sites Perform histology on the measurement locations to confirm tissue-type

Next steps

Analyze animal data to test the in vivo/ex vivo correlation

Improve the data analysis algorithmDesign and test various probes for both fine

needle and core needlePlan a clinical trial study in humans

Conclusions

An apparatus utilizing LCI for guiding breast FNA has been developed

Our preliminary results demonstrate that relatively simple parameters computed from LCI axial reflectivity profiles may be used for accurate differentiation of various types of breast tissue

This technology holds promise for improving the diagnostic outcome of breast biopsy

Acknowledgement

NIH/NCI – STTR phase I – 1R41CA114896-01A1

MGH, Wellman Center