Pure phase-encoded MRI and classification of solids

Solid-state 31P and 1H chemical MR micro-imaging of hard tissues and biomaterials with magic angle spinning at very high magnetic field

In this work, we show that it is possible to overcome the limitations of solid-state MRI for rigid tissues due to large line broadening and short dephasing times by combining Magic Angle Spinning (MAS) with rotating pulsed field gradients. This allows recording ex vivo 31P 3D and 2D slice-selected images of rigid tissues and related biomaterials at very high magnetic field, with greatly improved signal to noise ratio and spatial resolution when compared to static conditions. Cross-polarization is employed to enhance contrast and to further depict spatially localized chemical variations in reduced experimental time. In these materials, very high magnetic field and moderate MAS spinning rate directly provide high spectral resolution and enable the use of frequency selective excitation schemes for chemically selective imaging. These new possibilities are exemplified with experiments probing selectively the 3D spatial distribution of apatitic hydroxyl protons inside a mouse tooth with attached jaw bone with a nominal isotropic resolution nearing 100 µm.

Quantitative solid-state NMR imaging of synthetic calcium phosphate implants

It is shown that solid-state phosphorus-31 nuclear magnetic resonance imaging can be used to measure quantitatively the mass of hydroxyapatite (HA), a synthetic calcium phosphate used as an orthopedic implant material, in the presence of bone. A three-dimensional projection reconstruction technique was used to produce solid-state images from 998 free induction decays sampled in the presence of a fixed amplitude field gradient whose direction was varied uniformly over the unit sphere. Chemical selection is achieved using T1 contrast, as the synthetic calcium phosphate has a shorter T1 (1.8 sec at 4.7 T) compared with the bone (approximately 15 sec at 4.7 T in vivo, 42 sec ex vivo). Experimental results demonstrating the linear relationship between image intensity and HA density in phantoms containing HA and silicon (IV) oxide, and HA and bone are shown. Chemically pure images of bone mineral and synthetic HA have been computed from images of New Zealand White rabbits acquired in vivo at two different recycle times. The technique can be used to follow noninvasively the resorption and remodeling of calcium phosphate implants in vivo.

Multinuclear solid-state three-dimensional MRI of bone and synthetic calcium phosphates

Multinuclear three-dimensional solid-state MRI of bone, tooth, and synthetic calcium phosphates is demonstrated in vitro and in vivo with a projection reconstruction technique based on acquisition of free induction decays in the presence of fixed amplitude magnetic field gradients. Phosphorus-31 solid-state MRI provides direct images of the calcium phosphate constituents of bone substance and is a quantitative measurement of the true volumetric bone mineral density of the bone. Proton solid-state MRI shows the density of bone matrix including its organic constituents, which consist principally of collagen. These solid-state MRI methods promise to yield a biological picture of bone richer in information concerning the bone composition and short range-crystalline order than the fluid-state images provided by conventional proton MRI or the density images produced by radiologic imaging techniques. Three-dimensional solid-state projection reconstruction MRI should be readily adaptable to both human clinical use and nonmedical applications for a variety of solids in materials science.

Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because we allow for mixtures of materials and treat voxels as regions, our technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to our approach. First, we assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; we compute the relative proportion of each material in the voxels. Second, we incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, rho(x), from the samples and then looking at the distribution of values that rho(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that we classify is chosen to match the spacing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.