Document 14875566
Mammographically Configured,
Automated Ultrasound: Methods
and Poten;al Applica;ons
Paul L. Carson, Ph.D.
Marilyn A. Roubidoux, M.D., Mitchel A. GoodsiC, Ph.D.,
Oliver Kripfgans, Ph.D., Won-‐Mean Lee, M.S., Rungroj
Jintamethasawat, M.S., Brian Fowlkes, Ph.D., Chris
Lashbrook, RT, Fong Ming Hooi, Ph.D., Xueding Wang,
Ph.D., et al.
University of Michigan
Peng Gu, B.E., Jie Yuan, Ph.D.
Nanjing University
AAPM, July 15, 2015
T
he most promising imaging modality for improved breast screening -‐ automated
breast US
• False posi;ves are a problem
• 3 geometries
• Supine for US plus op;cal imaging
-‐ Not collocated with dependent or compressed breast
• Conven;onal mammographic geometry
-‐ US with Breast Tomography &
Op;cal/Photoacous;c
• Dependent breast in air or water
-‐ US with MRI/X-‐ray CT &
Op;cal/Microwave SOS MRI
Karmanos Ca Inst., N Duric
1
3
Breast Light and Ultrasound
Combined Imaging (BLUCI)
Two of the 3 systems discussed
2
Mul;modality breast imaging in same and different systems
3
Acquired scan
C-‐Scan MLO
View from front-‐Eleva;onal scan
DBT density
Looks like glandular
;ssue on US in this case
Dual Sided MLO
Orthogonal Views
• Acous;c coupling in the mammographic geometry -‐ nearly solved
• Good 3D, aligned with
Asymptoma;c Volunteer DBT, within ~1 cm.
•Alignment is beCer when combined with the DBT system, facilita;ng mutually synergis;c reconstruc;ons.
4
DBT and Single and Dual Sided Breast Images with 38 mm wide transducer
Combined DBT/single-‐ sided US scanner
Dual-‐sided US scanner
DBT
CC MLO
6_10_014
5
Contrast to noise increased 67% in dual sided images
7 cyst cases
Single sided Dualsided
Case 1
Case 2
Case 3 single sided TOP image (red downwards-‐poin;ng triangle), the single sided BOT image (green upwards-‐poin;ng triangle), and the dual sided TOPBOT image (blue dot)
Eric Larson, et al.
Frac;onal coverage of the breast
59% for imaging from the top or boCom only
89% for both)
Lobular CA In Situ
Long Array Fusion System
LCC DBT
Clincal US, Logiq E9
LMLO DBT
Zoomed
LCC LMLO
Sclerosing
Fibroadenoma
not visible on DBT, or mammo even with BX clips
Long Array Fusion System
Clincal US
Exis;ng Systems for SOS Imaging
Techniscan
C. Li, N. Duric, P. LiCrup, and L. Huang, "In vivo breast sound-‐speed imaging with ultrasound tomography." Ultrasound in Medicine & Biology , vol.
35, no. 10, pp. 1615-‐1628, Oct. 2009.
M. P. André, C. H. Barker, N. Sekhon, J. Wiskin, D. Borup, and K. Callahan, "Pre-‐Clinical experience with Full-‐Wave Inverse-‐ScaCering for breast imaging acous;cal imaging," ser. Acous;cal Imaging, I. Akiyama, Ed. Dordrecht: Springer Netherlands, 2009, vol. 29, ch. 10, pp. 73-‐80.
•
Malignant lesions typically have an elevated sound speed and aCenua;on in comparison to breast fat
;ssue.
Background
M. P. André, C. H. Barker, N. Sekhon, J. Wiskin, D. Borup, and K. Callahan, "Pre-‐Clinical experience with Full-‐Wave Inverse-‐ScaCering for breast imaging acous;cal imaging," ser. Acous;cal Imaging, I. Akiyama, Ed. Dordrecht: Springer Netherlands, 2009, vol. 29, ch. 10, pp.
73-‐80.
The Dual Sided System, BLUCI
• Breast Light and Ultrasound
Combined Imaging (BLUCI) employs opposed arrays opera;ng in the compressed breast geometry
• Can do transmission tomography and PAT
• Co-‐registered to two-‐sided B-‐mode ultrasound
• Ready for coregistra;on to:
– mammography
– X-‐ray tomosynthesis
– photoacous;cs
Sound Speed Image Reconstruc=on Algorithm for
Ultrasound Limited Angle Tomography For
Mammographic Geometry
Posi;on of R rela;ve to T x x
(x, y, z)
Time-‐of-‐flight matrix of size nxn
Object’s region of interest
Object’ region of interest (ROI)
n …
T x
3 2 1
2
1
Object
1406 m/s
Background
1492 m/s
1 2 3 n
R x
…
Sound speed image reconstruc;on algorithm
Start
Obtain Prior_slowness_map
(homogeneous),
Experimental_TOF_map,
R x
posi;on
Slowness_map = Prior_slowness_map
Generate TOF_map using Slowness_map, R x posi;on
Update Slowness_map using conjugate gradient update method
Compute misfit func;on S from
Slowness_map, Prior_slowness_map,
TOF_map, Experimental_TOF_map,
C
D
,
C
M
S <=
Thresh old
Y
N
Return
Final_SOS = 1/
Slowness_map
Ray Tracing Reconstruc=on
• Given:
– Time-‐of-‐flight of all pairs of transmit and receive elements
– Receive transducer’ posi;on
• Calculate
– The slowness map
2S
C
D
: Data covariance matrix
TOF_map
Prior_slowness_map
Experimental_TOF_ map
Slowness_map
C
M
: Model covariance matrix
(ROI_map)
Original Reconstruction EXPERIMENT
2.5
3
3.5
4
0.5
1
1.5
2
4.5
5
− 2 − 1 0
Lateral [cm]
1 2
Precise forward model necessary for convergence
Adequately fine grid spacing
Knowledge of transducer loca;ons
Incorporate lens informa;on in the forward model
A priori informa;on from pulse echo
Can make limited angle SOS
quan;ta;ve
3.5
4
4.5
5
− 2
0.5
1
1.5
2
2.5
3
Reconstruction with A Priori Information
− 1 0
Lateral [cm]
1 2
Cross − section Through Inclusion
1500
1480
1460
1440
1420
1400
1380
0 1 2 3
Axial [cm]
4
Cross − section Through Inclusion
5
1500
1480
1460
1440
1420
1400
1380
0 1 2 3
Axial [cm]
4 5
•
Madsinh phantom
Breast Phantom
1
2
3
4
5
6
7
8
− 2 − 1 0
Lateral [cm]
1
1500
1480
1460
1440
1420
1400
2
1380
Limited angle aCenua;on images, with L4-‐7 transducer, of an aCenua;ng, 12 mm dia., cylinder in water using a weighted least squares model with a priori informa;on
Uncorrected
Hooi FM, "Op;mized beamforming and limited angle tomography of the compressed breast," disserta;on, Univ. of
Michigan, 2012, Ann
Arbor, pp. 114, MYNCBI
99826424.
Corrected
Simula;on Experiment
17
Segmenta;on in Real Breasts
!
acquired view of the data with the coordinate system. (a) Skin, (b) subcutaneous fat, (c) glandular ;ssue and (d) fat.
d a
(b) Opening-‐by-‐reconstruc;on and closing-‐by-‐ reconstruc;on in 3D space
(c) Applica;on of 3D sobel operator
(d) Watershed boundaries in image space
(e) Detail of d
(e) Tissue-‐specific region classifica;on
segmenta;on approach
(1) morphological processing, to eliminate the speckle noise
(2) edge informa;on extrac;on;
(3) watershed segmenta;on;
(4) region classifica;on
Region classifica;on based on features of mean intensity histogram
!
Manually supervised and corrected and automated segmenta;ons of fibroglandular ;ssues
Automated segmenta;on agreed with
BIRADS classifica;on by radiologist in
86% of 21 cases.
Manually supervised and corrected classifica;on on 8 cases showed good
Pixel by pixel agreement.
SE -‐-‐ misclassified pixels as
% of all breast pixels
TC – Intersec;on of sets of manual and automated segmented pixels/ union of those sets
Peng Gu, Won-‐Mean Lee, Marilyn A. Roubidoux, Jie Yuan, Paul L. Carson
Limited Angle Ultrasound
Tomography
• Less than perfectly rigid scanning system
• Applica;on: combina;on with X-‐ray
Breast specimen
Ultrasound linear transducers
Jintamethasawat,
Kripfgans, et al.
• Errors from a priori : segmenta=on misalignments
• Errors from poor system calibra;on : transducer misalignments
• If cannot eliminate errors, how much error the imaging system can tolerate?
Limita=ons
• Breast specimen reconstruc;on example
Conclusions
• Es;mated maximum misplacement allowed in each imaging scenario (9 mm object diameter)
Misplacement type/
Imaging scenario
Segmenta;on size
Segmenta;on loca;on in lateral direc;on
Segmenta;on loca;on in axial direc;on
Transducer transla;onal misplacement in axial direc;on
Transducer transla;onal misplacement in lateral direc;on
Rota;onal misalignment about eleva;onal axis
Transducer transla;onal misplacement in eleva;onal direc;on
Rota;onal misalignment about axial axis
20 m/s Fat-‐ water discrimina=on
6 mm
> 5 mm, < -‐5 mm
> 5 mm, < -‐5 mm
0.2 mm
4 mm
> 10 deg, < -‐10 deg
5 mm
> 10 deg, < -‐10 deg
5 m/s Fine discrimina=on
1 mm
2 mm
5 mm
35 µm
1 mm
2 deg
2 mm
> 10 deg, < -‐10 deg
0.5ºC
Hyperthermia monitoring
0.2 mm
0.4 mm
1 mm
7 µm
0.2 mm
0.4 deg
0.4 mm
> 10 deg, < -‐10 deg
Photoacous;c Image of breast vascularity
Earlier in vitro work: Xie Z, Hooi F, Fowlkes
B, Pinsky R, Wang X, Carson P," Ult Med
Biol 39 (11), 2176-‐2184, (2013.
Color Doppler image of breast vascularity
Acknowledgements
This work was supported in part by:
• NIH BRP 1 R01 CA115267, NIH BRP 2 R01 CA91713, and NSF CBET
0756338.
Sushma Alvar
Sacha Verweij
Mark Haynes
Kai Thomenius
Cynthia Davis
Larry Mo et al.
