title: Strain-encoded breast MRI in phantom and ex vivo specimens with histological validation: preliminary results.
abstract: OBJECTIVE
  To evaluate the feasibility of using strain-encoded (SENC) breast magnetic resonance images (MRI) for breast cancer detection by examining the compression and relaxation response properties in phantoms and ex vivo breast samples.
  
  
  METHODS
  A tissue phantom was constructed to mimic different sizes of breast masses and tissue stiffness. In addition, five human ex vivo whole breast specimens with and without masses were studied. MR data was acquired on a 3T scanner consisting of T(1)-weighted, fat suppressed spin echo T(2)-weighted, and SENC breast images. Mechanical tissue characteristics (strain) of the phantoms and breast tissue samples were measured using SENC imaging in both compression and relaxation modes. The breast tissue specimens were sectioned and stained in the same plane as the MRI for histological evaluation.
  
  
  RESULTS
  For the phantom, SENC images showed soft masses with quantitative strain values between 35% and 50%, while harder masses had strain values between 0% and 20%. Combined compression (CMP) and relaxation (REX) breast SENC images separately categorized all masses into three different groups. For breast SENC, the signal intensities between ex vivo breast mass and breast glandular tissue were significantly different (-7.6 ± 2.6 verses -20.6 ± 5.4 for SENC-CMP, and 4.2 ± 1.5 verses 22.6 ± 5 for SENC-REX, p < 0.05).
  
  
  CONCLUSIONS
  We have demonstrated that SENC breast MRI can be used to obtain mechanical tissue properties and give quantitative estimates of strain in tumors. This feasibility study provides the basis for future clinical studies.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Ahmed A
authors.initials: AA
authors.lastname: Haruoni
authors.forename: Jakir
authors.initials: J
authors.lastname: Hossain
authors.forename: Riham
authors.initials: R
authors.lastname: El Khouli
authors.forename: Kant M
authors.initials: KM
authors.lastname: Matsuda
authors.forename: David A
authors.initials: DA
authors.lastname: Bluemke
authors.forename: Nael F
authors.initials: NF
authors.lastname: Osman
authors.forename: Michael A
authors.initials: MA
authors.lastname: Jacobs
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000503:physiopathology*; D054459:Elasticity Imaging Techniques / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity