Diffusion-weighted single-shot EPI (sshEPI) is one of the most important tools for the diagnostic assessment of stroke patients, but it suffers from well known artifacts. Therefore, sshEPI was combined with SENSitivity Encoding (SENSE) to further increase EPI's potential for stroke imaging. Eight healthy volunteers and a consecutive series of patients (N ‫؍‬ 8) with suspected stroke were examined with diffusion-weighted SENSE-sshEPI using different reduction factors (1.0 ≤ R ≤ 3.0). Additionally, a high-resolution diffusion-weighted SENSEsshEPI scan was included. All examinations were diagnostic and of better quality than conventional sshEPI. No ghostings or aliasing artifacts were discernible, and EPI-related image distortions were markedly diminished. Chemical shift artifacts and eddy current-induced image warping were still present, although to a markedly smaller extent. Measured direction-dependent diffusion-coefficients and isotropic diffusion values were comparable to previous findings but showed less fluctuation. We have demonstrated the technical feasibility and clinical applicability of diffusion-weighted SENSE-sshEPI in patients with subacute stroke. Because of the faster k-space traversal, this novel technique is able to reduce typical EPI artifacts and increase spatial resolution while simultaneously remaining insensitive to bulk motion. Key words: magnetic resonance imaging; diffusion imaging; brain; stroke; parallel imaging Diffusion-weighted imaging (DWI) has become a major tool for the diagnostic assessment of stroke patients (1). In the acute stage, DWI is extremely valuable for treatment decisions because of its high sensitivity to early ischemic damage (2). Thereafter, contributions to the diagnostic work-up come from observing the characteristic course of diffusional abnormalities from infarction (3,4). Most often, single-shot echo-planar imaging (sshEPI) has been the preferred technique for performing DWI of stroke patients because it allows rapid image acquisition and is much less susceptible to ghosting artifacts than conventional multishot techniques. These ghosting artifacts are primarily the consequence of random phase errors, which are caused by bulk motion during the diffusion-preparation period. However, image quality of sshEPI can also suffer from problems such as image blurring due to the long EPI readout interval, and artifacts due to EPI's high susceptibility to resonance offsets (e.g., lipid chemical shift, magnetic susceptibility gradients, or B 0 inhomogeneities). In this context, image degradation is likely to occur, especially at the base of the skull and in infratentorial parts of the brain, and may therefore critically impair the detection and delineation of ischemic lesions in these regions.In this study, diffusion-weighted sshEPI was used together with SENSitivity Encoding (SENSE) (5) to overcome the aforementioned limitations of sshEPI. SENSE-sshEPI reduces the train of gradient echoes in combination with a faster k-space traversal per unit time. The resultant increased...

SENSitivity Encoding (SENSE) greatly enhances the quality of diffusion-weighted echo-planar imaging (EPI) by reducing blurring and off-resonance artifacts. Such improvement would also be desirable for diffusion tensor imaging (DTI), but measures derived from the diffusion tensor can be extremely sensitive to any kind of image distortion. Whether DTI is feasible in combination with SENSE has not yet been explored, and is the focus of this study. Using a SENSE-reduction factor of 2, DTI scans in eight healthy volunteers were carried out with regular-and high-resolution acquisition matrices. To further improve the stability of the SENSE reconstruction, a new coil-sensitivity estimation technique based on variational calculus and the principles of matrix regularization was applied. With SENSE, maps of the trace of the diffusion tensor and of fractional anisotropy (FA) had improved spatial resolution and less geometric distortion. Overall, the geometric distortions were substantially removed and a significant resolution enhancement was achieved with almost the same scan time as regular EPI. DTI was even possible without the use of quadrature body coil (QBC) reference scans. Geometry-factor-related noise enhancement was only discernible in maps generated with higher-resolu- Key words: magnetic resonance imaging; diffusion tensor imaging; brain; white matter; brain morphology SENSitivity Encoding (SENSE) is a valuable complement to regular gradient encoding in MRI (1). It uses some of the spatial information contained in the individual elements of an RF coil array to more efficiently traverse k-space. SENSE can therefore serve to accelerate virtually any conventional MRI technique without interfering with the numerous different contrast mechanisms used in MRI, such as diffusion weighting. It has been demonstrated that SENSE can help to improve single-shot EPI and FSE scans by reducing artifacts, as well as improving spatial resolution (2,3). In this context, it has been shown that singleshot SENSE EPI considerably increases lesion conspicuity in diffusion-weighted imaging (DWI) of ischemic stroke (4), which currently represents one of the most important fields of application for the DWI technique (5). One tradeoff of SENSE is a lower signal-to-noise ratio (SNR) due to the reduced number of phase-encoding steps (1). This penalty can be minimized, as discussed below.The ability to show the anisotropic diffusion of white matter is another powerful feature of DWI (6,7). White matter anisotropy reflects the anatomic organization of white matter tracts and is characterized best by the diffusion tensor D, which provides a mathematical-physical description of diffusion properties in three dimensions. Damage to the cerebral fiber system, and therefore a reduction of anisotropy, may occur with or be characteristic of various CNS disorders (8 -12). Similarly, measurements of diffusion anisotropy can serve to probe the degree of invasiveness of neoplasms to specific functional tracts (13), and to elucidate functional and anatomic ...

There is a need for, and utility in, the acquisition of data sets of cardiac histoanatomy, with the vision of reconstructing individual hearts on the basis of noninvasive imaging, such as MRI, enriched by reference to detailed atlases of serial histology obtained from representative samples. These data sets would be useful not only as a repository of knowledge regarding the specifics of cardiac histoanatomy, but could form the basis for generation of individualized high-resolution cardiac structure-function models. The current article presents a step in this general direction: it illustrates how whole-heart noninvasive imaging can be combined with whole-heart histology in an approach to achieve automated construction of histoanatomically detailed models of cardiac 3D structure and function at hitherto unprecedented resolution and accuracy (based on 26.4 × 26.4 × 24.4 μm MRI voxel size, and enriched by histological detail). It provides an overview of the tools used in this quest and outlines challenges posed by the approach in the light of applications that may benefit from the availability of such data and tools.

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