Computer assistance for MR based diagnosis of breast cancer: present and future challenges.

MR based methods have gained an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. The application of MR based imaging methods depends on various automated image processing routines. The combination of techniques for preprocessing, quantification and visualization of datasets is necessary to achieve fast and solid assessment of valuable parameters for diagnosis. In this paper, different aspects such as registration methods for the reduction of motion artifacts, segmentation issues, as well as morphologic and dynamic lesion analysis will be reviewed with a focus on breast MRI, MR spectroscopy and MR guided biopsies of the breast, their implications and technical challenges from a computer assistance point of view.

Fast 1H spectroscopic imaging using steady state free precession and spectral-spatial RF pulses.

Recently, new methods for fast (1)H spectroscopic imaging based on the condition of steady state free precession (SSFP) were introduced to achieve a high signal-to-noise ratio at short minimum measurement times. In this work, a major improvement is presented to overcome a crucial drawback in some of the former sequences: the lack of spatial selectivity. Good spectral selectivity at very high sampling efficiency can be achieved by using spectral-spatial RF pulses, and combined with localised shimming. Results are shown from both phantom experiments and in vivo studies on the rat brain acquired at 4.7 T.

Fast 3D echo planar SSFP-based 1H spectroscopic imaging: demonstration on the rat brain in vivo.

A fast proton spectroscopic imaging pulse sequence based on the condition of steady-state free precession is presented. High 3D spatial and temporal resolution is achieved using simultaneous detection of both one spatial and one spectral dimension, with a time-dependent gradient cycle known from echo planar imaging. Additionally, in order to increase the spectral width of the measurement, an interleaved acquisition scheme is shown either for systems with limited gradient switching capabilities or applications with a wide chemical shift range. The pulse sequence is implemented on a standard 4.7-T nuclear magnetic resonance animal imaging system. Measurements with a total measurement time of less than 2.5 min and a nominal voxel size of 6.75 microl using a total of 64 x 32 x 16 voxels are performed on phantoms and healthy rat brain in vivo allowing the rapid detection of signals from both uncoupled and J-coupled spin systems with high signal-to-noise ratio.

Fast proton spectroscopic imaging using steady-state free precession methods.

Various pulse sequences for fast proton spectroscopic imaging (SI) using the steady-state free precession (SSFP) condition are proposed. The sequences use either only the FID-like signal S(1), only the echo-like signal S(2), or both signals in separate but adjacent acquisition windows. As in SSFP imaging, S(1) and S(2) are separated by spoiler gradients. RF excitation is performed by slice-selective or chemical shift-selective pulses. The signals are detected in absence of a B(0) gradient. Spatial localization is achieved by phase-encoding gradients which are applied prior to and rewound after each signal acquisition. Measurements with 2D or 3D spatial resolution were performed at 4.7 T on phantoms and healthy rat brain in vivo allowing the detection of uncoupled and J-coupled spins. The main advantages of SSFP based SI are the short minimum total measurement time (T(min)) and the high signal-to-noise ratio per unit measurement time (SNR(t)). The methods are of particular interest at higher magnetic field strength B(0), as TR can be reduced with increasing B(0) leading to a reduced T(min) and an increased SNR(t). Drawbacks consist of the limited spectral resolution, particularly at lower B(0), and the dependence of the signal intensities on T(1) and T(2). Further improvements are discussed including optimized data processing and signal detection under oscillating B(0) gradients leading to a further reduction in T(min).