Improved hardware for higher spatial resolution strain-encoded (SENC) breast MRI for strain measurements

Strain-encoded magnetic resonance: a method for the assessment of myocardial deformation

This study aims to assess the usefulness of strain‐encoded magnetic resonance (SENC) for the quantification of myocardial deformation (‘strain’) in healthy volunteers and for the diagnostic workup of patients with different cardiovascular pathologies. SENC was initially described in the year 2001. Since then, the SENC sequence has undergone several technical developments, aiming at the detection of strain during single‐heartbeat acquisitions (fast‐SENC). Experimental and clinical studies that used SENC and fast‐SENC or compared SENC with conventional cine or tagged magnetic resonance in phantoms, animals, healthy volunteers, or patients were systematically searched for in PubMed. Using ‘strain‐encoded magnetic resonance and SENC’ as keywords, three phantom and three animal studies were identified, along with 27 further clinical studies, involving 185 healthy subjects and 904 patients. SENC (i) enabled reproducible assessment of myocardial deformation in vitro, in animals and in healthy volunteers, (ii) showed high reproducibility and substantially lower time spent compared with conventional tagging, (iii) exhibited incremental value to standard cine imaging for the detection of inducible ischaemia and for the risk stratification of patients with ischaemic heart disease, and (iv) enabled the diagnostic classification of patients with transplant vasculopathy, cardiomyopathies, pulmonary hypertension, and diabetic heart disease. SENC has the potential to detect a wide range of myocardial diseases early, accurately, and without the need of contrast agent injection, possibly enabling the initiation of specific cardiac therapies during earlier disease stages. Its one‐heartbeat acquisition mode during free breathing results in shorter cardiovascular magnetic resonance protocols, making its implementation in the clinical realm promising.

Assessment of liver fibrosis using fast strain-encoded MRI driven by inherent cardiac motion

PURPOSE

An external driver-free MRI method for assessment of liver fibrosis offers a promising noninvasive tool for diagnosis and monitoring of liver disease. Lately, the heart's intrinsic motion and MR tagging have been utilized for the quantification of liver strain. However, MR tagging requires multiple breath-hold acquisitions and substantial postprocessing. In this study, we propose the use of a fast strain-encoded (FSENC) MRI method to measure the peak strain (Sp ) in the liver's left lobe, which is in close proximity and caudal to the heart. Additionally, we introduce a new method of measuring heart-induced shear wave velocity (SWV) inside the liver.

METHODS

Phantom and in vivo experiments (11 healthy subjects and 11 patients with liver fibrosis) were conducted. Reproducibility experiments were performed in seven healthy subjects.

RESULTS

Peak liver strain, Sp , decreased significantly in fibrotic liver compared with healthy liver (6.46% ± 2.27% vs 12.49% ± 1.76%; P < 0.05). Heart-induced SWV increased significantly in patients compared with healthy subjects (0.15 ± 0.04 m/s vs 0.63 ± 0.32 m/s; P < 0.05). Reproducibility analysis yielded no significant difference in Sp (P = 0.47) or SWV (P = 0.56).

CONCLUSION

Accelerated external driver-free noninvasive assessment of left liver lobe strain and SWV is feasible using strain-encoded MRI. The two measures significantly separate healthy subjects from patients with fibrotic liver. Magn Reson Med 74:106-114, 2015. © 2014 Wiley Periodicals, Inc.

Strain-encoded breast MRI in phantom and ex vivo specimens with histological validation: preliminary results

PURPOSE

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.

Finding the optimal compression level for strain-encoded (SENC) breast MRI; simulations and phantom experiments

Breast cancer is the most common cancer among women and the second highest cause of cancer-related death. Diagnostic magnetic resonance imaging (MRI) is recommended to screen high-risk patients. Strain-Encoded (SENC) can improve MRI's specificity by detecting and differentiating masses according to their stiffness. Previous phantom and ex-vivo studies have utilized SENC to detect cancerous masses. However, SENC required a 30% compression of the tissue, which may not be feasible for in-vivo imaging. In this work, we use finite element method simulations and phantom experiments to determine the minimum compression required to detect and classify masses. Results show that SENC is capable of detecting stiff masses at compression level of 7%, though higher compression is needed in order to differentiate between normal tissue and benign or malignant masses. With on-line SENC calculations implemented on the scanner console, we propose to start with small compressions for maximum patient comfort, then progress to larger compressions if any masses are detected.