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A method is presented that is capable of determining more than one fiber orientation within a single voxel in high angular resolution diffusion imaging (HARDI) data sets. This method is an extension of the Markov chain method recently introduced to diffusion tensor imaging (DTI) analysis, allowing the probability density function of up to 2 intra-voxel fiber orientations to be inferred. The multiple fiber architecture within a voxel is then assessed by calculating the relative probabilities of a 1 and 2 fiber model. It is demonstrated that for realistic signal to noise ratios, it is possible to accurately characterize the directions of 2 intersecting fibers using a 2 fiber model. Diffusion tensor imaging (DTI) has become a very important tool for investigating tissue microstructure (1-3), and is capable of providing information on the nerve fiber directions within imaged voxels. The principal axis of the diffusion tensor coincides with the nerve fiber direction because the anisotropic nature of nerve fibers preferentially allows diffusion in a direction parallel to the fiber. Despite the success of DTI, the standard second order tensor model copes poorly with partial volume effects (4) as it is unable to characterize the directions of crossing fibers (5,6). This has implications for tractography for 2 reasons. First, crossing fibers will lead to erroneous directions being inferred; this will add to other sources of noise in the data and potentially imply false connectivity. Secondly, voxels may appear isotropic with complete loss of directional information (7).High angular resolution diffusion imaging (HARDI) is an alternative to DTI (8). Methods to analyze such diffusion weighted data sets with many gradient encoding directions, which go beyond the traditional second order tensor approach, have been proposed and implemented in the literature. These schemes include the use of tensors above second order (5,9); spherical harmonic (10), harmonic decomposition (11), and spherical deconvolution (12) of the signal; as well as least-square fitting of a Gaussian mixture model of 2 intersecting fibers (13,14) and q-ball imaging (15). Determination of uncertainty of the diffusion tensor is a problem that has received much attention in the literature (16 -20), and this knowledge of the uncertainty in fiber orientation has been incorporated into fiber tracking methods (21).Behrens et al. (22) introduced Bayesian inference to the problem of determining fiber orientation. Using a single fiber model, their algorithm could produce a probability density function (PDF) for the fiber orientation within a given voxel (23). Their algorithm explicitly allows for partial volumes of isotropic material and, perhaps more importantly, allows uncertainty in the fiber orientation to be represented. The algorithm presented here is an extension of this analysis to address the problem of partial volumes in voxels containing 2 distinct fiber orientations, thereby allowing us to directly assess the directions and the separate PDFs of more than 1...

Purpose: To make the quantitative blood oxygenation level-dependent (qBOLD) method more suitable for clinical application by accounting for proton diffusion and reducing acquisition times. Materials and Methods:Monte Carlo methods are used to simulate the signal from diffusing protons in the presence of a blood vessel network. A diffusive qBOLD model was then constructed using a lookup table of the results. Acquisition times are reduced by parallel imaging and by employing an integrated fieldmapping method, rather than running an additional sequence.Results: The addition of diffusion to the model is shown to have a significant impact on predicted signal formation. This is found to affect all fitted parameters when the model is applied to real data. Parallel imaging and integrated fieldmapping allowed the GESSE (gradient echo sampling of a spin echo) acquisition to be made in less than 10 minutes while maintaining high signal-to-noise ratio (SNR). Conclusion:By incorporating integrated field mapping and parallel imaging techniques, GESSE data were acquired within clinically acceptable acquisition times. These data fit closely to the diffusive qBOLD model, providing more realistic and robust measurements of T 2 and blood oxygenation than the static model.

Modeled attenuation correction (AC) will be necessary for combined PET/MRI scanners not equipped with transmission scanning hardware. We compared 2 modeled AC approaches that use nonrigid registration with rotating 68 Ge rod-based measured AC for 10 subjects scanned with 18 F-FDG. Methods: Two MRI and attenuation map pairs were evaluated: tissue atlas-based and measured templates. The tissue atlas approach used a composite of the BrainWeb and Zubal digital phantoms, whereas the measured templates were produced by averaging spatially normalized measured MR image and coregistered attenuation maps. The composite digital phantom was manually edited to include 2 additional tissue classes (paranasal sinuses, and ethmoidal air cells or nasal cavity). In addition, 3 attenuation values for bone were compared. The MRI and attenuation map pairs were used to generate subject-specific attenuation maps via nonrigid registration of the MRI to the MR image of the subject. SPM2 and a B-spline free-form deformation algorithm were used for the nonrigid registration. To determine the accuracy of the modeled AC approaches, radioactivity concentration was assessed on a voxelwise and regional basis. Results: The template approach produced better spatial consistency than the phantom-based atlas, with an average percentage error in radioactivity concentration across the regions, compared with measured AC, of 21.2% 6 1.2% and 21.5% 6 1.9% for B-spline and SPM2 registration, respectively. In comparison, the tissue atlas method with B-spline registration produced average percentage errors of 0.0% 6 3.0%, 0.9% 6 2.9%, and 2.9% 6 2.8% for bone attenuation values of 0.143 cm 21 , 0.152 cm 21 , and 0.172 cm 21 , respectively. The largest errors for the template AC method were found in parts of the frontal cortex (23%) and the cerebellar vermis (25%). Intersubject variability was higher with SPM2 than with B-spline. Compared with measured AC, template AC with B-spline and SPM2 achieved a correlation coefficient (R 2 ) of 0.99 and 0.98, respectively, for regional radioactivity concentration. The corresponding R 2 for the tissue atlas approach with B-spline registration was 0.98, irrespective of the bone attenuation coefficient. Conclusion: Nonrigid registration of joint MRI and attenuation map templates can produce accurate AC for brain PET scans, particularly with measured templates and B-spline registration. Consequently, these methods are suitable for AC of brain scans acquired on combined PET/ MRI systems. At tenuation correction (AC) is a vital step in the determination of quantitatively accurate PET images (1). Furthermore, the most commonly used scatter-correction technique (2) also relies on accurate attenuation information. With the advent of scanners that combine PET with MRI (3) and incorporate neither rotating radioactive sources nor CT, a new solution must be found for determining photon attenuation.It is more challenging to estimate attenuation from MRI than CT because the contrast mechanism is unrelated to photon attenuation. The ...

As evidenced by the success of PET-CT, there are many benefits from combining imaging modalities into a single scanner. The combination of PET and MR offers potential advantages over PET-CT, including improved soft tissue contrast, access to the multiplicity of contrast mechanisms available to MR, simultaneous imaging and fast MR sequences for motion correction. In addition, PET-MR is more suitable than PET-CT for cancer screening due to the elimination of the radiation dose from CT.A key issue associated with combining PET and MR is the fact that the performance of the photomultiplier tubes (PMTs) used in conventional PET detectors is degraded in the magnetic field required for MR. Two approaches have been adopted to circumvent that issue: retention of conventional, magnetic field-sensitive PMT-based PET detectors by modification of other features of the MR or PET system, or the use of new, magnetic fieldinsensitive devices in the PET detectors including avalanche photo-diodes (APDs) and silicon photomultipliers (SiPMs).Taking the former approach, we are assembling a modified microPET ® Focus 120 within a gap in a novel, 1T superconducting magnet. The PMTs are located in a low magnetic field (~30mT) through a combination of magnet design and the use of fiber optic 'bundles'.Two main features of the modified PET system have been tested, namely the effect of using long fiber optic bundles in the PET detector, and the impact of magnetic field upon the performance of the position sensitive PMTs.The design of a modified microPET ® -MR system for small animal imaging is completed, and assembly and testing is underway.

Different theoretical models of the BOLD contrast mechanism are used for many applications including BOLD quantification (qBOLD) and vessel size imaging, both in health and disease. Each model simplifies the system under consideration, making approximations about the structure of the blood vessel network and diffusion of water molecules through inhomogeneities in the magnetic field created by deoxyhemoglobin-containing blood vessels. In this study, Monte-Carlo methods are used to simulate the BOLD MR signal generated by diffusing water molecules in the presence of long, cylindrical blood vessels. Using these simulations we introduce a new, phenomenological model that is far more accurate over a range of blood oxygenation levels and blood vessel radii than existing models. This model could be used to extract physiological parameters of the blood vessel network from experimental data in BOLD-based experiments. We use our model to establish ranges of validity for the existing analytical models of Yablonskiy and Haacke, Kiselev and Posse, Sukstanskii and Yablonskiy (extended to the case of arbitrary time in the spin echo sequence) and Bauer et al. (extended to the case of randomly oriented cylinders). Although these models are shown to be accurate in the limits of diffusion under which they were derived, none of them is accurate for the whole physiological range of blood vessels radii and blood oxygenation levels. We also show the extent of systematic errors that are introduced due to the approximations of these models when used for BOLD signal quantification.

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