2008 SPIE Photonics West
Transcript of 2008 SPIE Photonics West
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