Improved Hardware for Higher Spatial Resolution Strain-encoded (SENC) Breast MRI for Strain Measurements

Rationale and Objectives

Early detection of breast lesions using mammography has resulted in lower mortality rates. However, some breast lesions are mammography occult, and magnetic resonance imaging (MRI) is recommended, but it has lower specificity. It is possible to achieve higher specificity by using strain-encoded (SENC) MRI and/or magnetic resonance elastography. SENC breast MRI can measure the strain properties of breast tissue. Similarly, magnetic resonance elastography is used to measure the elasticity (ie, shear stiffness) of different tissue compositions interrogating the tissue mechanical properties. Reports have shown that malignant tumors are three to 13 times stiffer than normal tissue and benign tumors.

Materials and Methods

The investigators have developed a SENC breast hardware device capable of periodically compressing the breast, thus allowing for longer scanning time and measuring the strain characteristics of breast tissue. This hardware enables the use of SENC MRI with high spatial resolution (1 × 1 × 5 mm 3 ) instead of fast SENC imaging. Simple controls and multiple safety measures were added to ensure accurate, repeatable, and safe in vivo experiments.

Results

Phantom experiments showed that SENC breast MRI has higher signal-to-noise ratio and contrast-to-noise ratio than fast SENC imaging under different scanning resolutions. Finally, the SENC breast device reproducibility measurements resulted in a difference of <1 mm with a 1% strain difference.

Conclusions

SENC breast magnetic resonance images have higher signal-to-noise ratio and contrast-to-noise ratios than fast SENC images. Thus, combining SENC breast strain measurements with diagnostic breast MRI to differentiate benign from malignant lesions could potentially increase the specificity of diagnosis in the clinical setting.

According to the American Cancer Society’s 2009 report, one in eight women will develop breast cancer in her lifetime. Early detection through periodic screening is the key to lower mortality rates, and the current methods used are mammography and ultrasound. However, some breast lesions are mammography and ultrasound occult (ie, those in dense breasts). Therefore, other methods, such as magnetic resonance imaging (MRI), are used especially in women who are at high risk to develop breast cancer.

Although MRI has high sensitivity (95%), its specificity remains moderate (83%) , and there is a need to increase specificity so that the number of unnecessary biopsy procedures can be reduced. Recent MRI methods have been proposed to further improve specificity, including diffusion-weighted imaging and apparent diffusion coefficient (DWI/ADC) mapping , magnetic resonance spectroscopy , and magnetic resonance elastography (MRE) , all of which were reviewed by El Khouli et al . DWI/ADC mapping can probe the movement of water and provide a measure of cellularity. Magnetic resonance spectroscopy can give metabolic information on the breast tissue.

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Methods

Hardware

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SENC breast hardware on the scanner side

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SENC breast hardware on the patient side

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SENC breast hardware modes of operation

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SENC MRI

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ϵ=ΔLL0=L−L0L0=LL0−1, ϵ

−

where Δ L is the change of the tissue’s length, and L and L 0 are the final and initial lengths of the tissue, respectively.

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ω(p)=ωLIL(p)+ωHIH(p)IL(p)+IH(p), ω

(

)

(

)

(

)

(

)

(

)

and local strain can then be quantified as

ϵ(p)=ω0ω(p)−1. ϵ

(

)

(

)

−

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Tagging Frequency

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ωH=ω0ϵmax stretch+1+B,ωL=ω0ϵmax compression+1−B,ω0=B(ϵmax compression+1)(ϵmax stretch+1)(ϵmax stretch−ϵmax compression), ω

max stretch

max compression

−

(

max compression

)

(

max stretch

)

(

max stretch

−

max compression

)

where B = (1/slice thickness), and ϵ max compression and ϵ max stretch are the expected strain values externally applied by our device. Note that ϵ max compression is always expressed in negative values, while ϵ max stretch is expressed in positive values. Because we are only interested in compression, we set ϵ max stretch to zero. Therefore, equation 3 is simplified to

ωL=ω0=−Bϵmax compression−B,ωH=ωL+B. ω

−

max compression

−

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Pulse Sequence and Flip Angle Optimization

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Ik∝MSSTAG(z)exp(−Δt/T1)∏k−1j=1cos(αj)sin(αk), I

∝

TAG

(

)

exp

(

−

)

∏

−

cos

(

)

sin

(

)

where M SS is the steady state magnetization, TAG( z ) is the tagging pattern created in the slice selection direction, Δ t is the time between each radiofrequency (RF) pulse, T1 is the relaxation time of the tissue, and α j is the flip angle of the j th RF pulse. To compensate for this signal loss, Stuber et al proposed increasing the excitation flip angle, α j , gradually through time to maintain uniform signal intensity through time using

αk−1=atan[sin(αk)exp(−Δt/T1)]. α

−

atan

[

sin

(

)

exp

(

−Δ

)

]

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Phantom design and experiments

Phantom Composition

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Table 1

Mixing Ratio for Different Masses with Corresponding Young’s Modulus Measured Using Dynamic Mechanical Analyzer

Group Material Mixing Ratio Young’s Modulus (KPa) Group/Background Ratio Classification A 3-4207 Dielectric Tough Gel ∗ 1:1 593 8.4 Malignant B 3-4133 Dielectric Gel ∗ 1.5:1 226 3.2 Benign C 3-4207 Dielectric Tough Gel ∗ 1:1.2 171 2.4 Benign D 3-4133 Dielectric Gel ∗ 1.2:1 100 1.4 Normal Background A-341 gel † 1:10 71 1 Normal E 3-4222 Dielectric Firm Gel ∗ 1:1.5 11–20 0.15–0.3 Normal

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MRI Scanning Protocol

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Table 2

Scanning Parameters for T1W, T2W, and Different Variations of SENC and FSENC Scans

Scan In-plane Resolution (mm) Slice Thickness (mm) ω_L = ω_0 (mm −1 ) ω H (mm −1 ) TFE Factor TSE Factor EPI Factor Compression Cycle/Slice Total Scan time (seconds) for Five Slices T1W 1 × 1 5 — — 3 — — 39 T2W 1 × 1 5 — — 12 — — 67 SENC 1 × 1 5 1.132 1.332 10 None 19 190 SENC1 2 × 2 5 1.132 1.332 10 None 9 90 SENC2 3 × 3 5 1.132 1.332 10 None 6 60 SENC3 3 × 3 10 0.567 0.667 10 None 6 60 FSENC1 2 × 2 5 1.132 1.332 17 3 1 10 FSENC2 3 × 3 5 1.132 1.332 11 3 1 10 FSENC3 3 × 3 10 0.567 0.667 11 3 1 10

EPI, echo-planar imaging; FSENC, fast strain-encoded; SENC, strain-encoded; TFE, turbo field-echo; T1W, T1-weighted; TSE, turbo spin-echo; T2W, T2-weighted.

T1W and T2W images are scanned while the device is in compress-and-hold mode.

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Quantification

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SNR=Sbackgroundσnoise, SNR

background

noise

where S is the mean signal intensity in the phantom background, and σ is the standard deviation of the noise calculated from a 30 × 30 pixel rectangular area outside the phantom. Because we were interested in detecting and measuring tumor size, we used the CNR elastography (CNR e ) given by Bilgen :

CNRe=2(Stumor−Sbackground)2σ2tumor+σ2background, CNR

(

tumor

−

background

)

tumor

background

where the background was calculated by including the pixels inside a circle surrounding the masses and then excluding pixels inside the masses.

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SENC Breast Hardware Accuracy

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Results

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Table 3

SNR and CNR e of Different Mass Groups for T1W, T2W, SENC, and FSENC Scans

Scan SNR CNR e Group A Group B Group C Group D Group E T1W 750 ± 20 19 ± 11 37 ± 22 49 ± 23 40 ± 22 21 ± 6 T2W 880 ± 10 35 ± 11 34 ± 32 50 ± 19 41 ± 28 23 ± 16 SENC 45 ± 5 80 ± 31 47 ± 18 40 ± 6 — — SENC1 60 ± 5 50 ± 29 33 ± 17 27 ± 10 — — SENC2 65 ± 9 38 ± 24 28 ± 10 25 ± 5 — — SENC3 150 ± 15 29 ± 19 31 ± 17 24 ± 5 — — FSENC1 22 ± 3 7.8 ± 5.5 2.2 ± 2.2 2.7 ± 3 — — FSENC2 54 ± 4 9.1 ± 5.6 1.8 ± 0.9 4.3 ± 3 — — FSENC3 110 ± 10 10 ± 7 1 ± 2 5 ± 7 — —

CNRe, contrast-to-noise ratio elastography; FSENC, fast strain-encoded; SENC, strain-encoded; SNR, signal-to-noise ratio; T1W, T1-weighted; T2W, T2-weighted.

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Discussion

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Conclusion

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References

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Improved Hardware for Higher Spatial Resolution Strain-encoded (SENC) Breast MRI for Strain Measurements

Rationale and Objectives

Materials and Methods

Results

Conclusions

Methods

Hardware

SENC breast hardware on the scanner side

SENC breast hardware on the patient side

SENC breast hardware modes of operation

SENC MRI

Tagging Frequency

Pulse Sequence and Flip Angle Optimization

Phantom design and experiments

Phantom Composition

MRI Scanning Protocol

Quantification

SENC Breast Hardware Accuracy

Results

Discussion

Conclusion

References

Further Reading

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