DW MR imaging allows monitoring of antiandrogen therapy in bone metastases. PSA level decrease corresponded well with an increase in mean tumor ADC. Heterogeneity of tumor response to therapy was demonstrated by functional DM analysis.

Purpose: To apply and to evaluate the newly developed advanced fast marching algorithm (aFM) in vivo by reconstructing the human visual pathway, which is characterized by areas of extensive fiber crossing and branching, i.e., the optic chiasm and the lateral geniculate nucleus (LGN). Materials and Methods:Diffusion tensor images were acquired in 10 healthy volunteers. Due to the proximity to bony structures and air-filled spaces of the optic chiasm, a high sensitivity encoding (SENSE) reduction factor was applied to reduce image distortions in this area. To reconstruct the visual system, three different seed areas were chosen separately. The results obtained by the aFM tracking algorithm were compared and validated with known anatomy. Results:The visual system could be reconstructed reproducibly in all subjects and the reconstructed fiber pathways are in good agreement with known anatomy. Conclusion:The present work shows that the advanced aFM, which is especially designed for overcoming tracking limitations within areas of extensive fiber crossing, handles the fiber crossing and branching within the optic chiasm and the LGN correctly, thus allowing the reconstruction of the entire human visual fiber pathway, from the intraorbital segment of the optic nerves to the visual cortex.

Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography resultsStaempfli, P; Reischauer, C; Jaermann, T; Valavanis, A; Kollias, S; Boesiger, P Staempfli, P; Reischauer, C; Jaermann, T; Valavanis, A; Kollias, S; Boesiger, P (2008 Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results AbstractA powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somatotopic map of the striatum was reconstructed, based on fMRI activations that were provoked by unilateral motor tasks. Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers. A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somat...

Objectives To assess the influence of age and sex on 10 cerebrospinal fluid (CSF) flow dynamics parameters measured with an MR phase contrast (PC) sequence within the cerebral aqueduct at the level of the intercollicular sulcus. Materials and Methods 128 healthy subjects (66 female subjects with a mean age of 52.9 years and 62 male subjects with a mean age of 51.8 years) with a normal Evans index, normal medial temporal atrophy (MTA) score, and without known disorders of the CSF circulation were included in the study. A PC MR sequence on a 3T MR scanner was used. Ten different flow parameters were analyzed using postprocessing software. Ordinal and linear regression models were calculated. Results The parameters stroke volume (sex: p < 0.001, age: p = 0.003), forward flow volume (sex: p < 0.001, age: p = 0.002), backward flow volume (sex: p < 0.001, age: p = 0.018), absolute stroke volume (sex: p < 0.001, age: p = 0.005), mean flux (sex: p < 0.001, age: p = 0.001), peak velocity (sex: p = 0.009, age: p = 0.0016), and peak pressure gradient (sex: p = 0.029, age: p = 0.028) are significantly influenced by sex and age. The parameters regurgitant fraction, stroke distance, and mean velocity are not significantly influenced by sex and age. Conclusion CSF flow dynamics parameters measured in the cerebral aqueduct are partly age and sex dependent. For establishment of reliable reference values for clinical use in future studies, the impact of sex and age should be considered and incorporated.

Various techniques have been proposed which aim at scan time reduction and/or at improved image quality by increasing the spatial resolution. Compressed sensing (CS) takes advantage of the fact that MR images are usually sparse in some transform domains and recovers this sparse representation from undersampled data. CS may be combined with parallel imaging such as sensitivity encoding (SENSE), hereafter referred to as Compressed SENSE, to further accelerate image acquisition since both techniques rely on different ancillary information. In practice, Compressed SENSE may reduce scan times of two-dimensional (2D) and three-dimensional (3D) scans by up to 50% depending on the sequence acquired and it works on 1.5-T or 3-T scanners. Compressed SENSE may be applied to 2D and 3D sequences in various anatomies and image contrasts. Image artefacts (i.e. motion, metal and flow artefacts, susceptibility artefacts) frequently appear on magnetic resonance images. The Compressed SENSE technique may cause special artefacts, which might influence image assessment if they go undetected by imaging readers. Our institution has been using Compressed SENSE for over half a year, both in a neuroradiological setting and for musculoskeletal examinations. So far, three special image artefacts—called the wax-layer artefact, the streaky-linear artefact and the starry-sky artefact—have been encountered and we aim to review these main artefacts appearing in sequences acquired with Compressed SENSE.Teaching Points • Compressed SENSE combines compressed sensing and SENSE technique. • Compressed SENSE permits scan time reduction and increases spatial image resolution. • Images acquired with Compressed SENSE may present with special artefacts. • Knowledge of artefacts is necessary for reliable image assessment.

Purpose: To construct a temperature-controlled diffusion phantom with known diffusion properties and geometry in order to facilitate the comparison and optimization of diffusion sequences with the objective of increasing the precision of experimentally derived diffusion parameters. Materials and Methods:A temperature-stabilized diffusion phantom made up of two crossing strands of hydrophobic polyethylene fibers was constructed. Reproducibility and temperature dependence of several diffusion parameters was investigated and compared with computer simulations. Furthermore, in order to stimulate actual use, the precision of measurement of different diffusion-encoding schemes was compared using bootstrap analysis. Results:The measured values of the diffusion parameters are highly reproducible and feature strong temperature dependence which is reproduced in simulations, underlining the necessity of a temperature-stabilized environment for quality control. The exemplary application presented here demonstrates that the phantom allows comparing and optimizing different diffusion sequences with regard to their measurement precision. Conclusion:The present work demonstrates that the diffusion phantom facilitates and improves the comparison and quality control of diffusion sequences and the ensuing parameters. The results show that an accurate temperature control is a vital prerequisite for highly reproducible calibration measurements. As such, the phantom might provide a valuable calibration tool for clinical studies. Regardless of the applied diffusion MRI technique, diffusion parameters are defined independently of acquisition hardware and sequence characteristics. In practice, this requirement is rarely met, since the choice of imaging sequence, imaging hardware, and main magnetic field strength affect the accuracy and the precision of the measurement, for example, by altering the sensitivity to artifacts or by evoking different achievable signal-to-noise ratios (SNRs). Because diffusion-weighted MRI is associated with inherently low SNR, scalar diffusion parameters are especially susceptible to these factors. Subsequently, image quality and the ensuing quantitative diffusion measures are often compromised. In addition, for a given diffusion imaging technique, measurement precision depends on a variety of sequence parameters. For example, measurement precision of diffusion parameters in anisotropic systems depends on the number and orientation of the diffusion-encoding directions (5,6).Restricted comparability and ongoing quantification efforts in clinical diagnostics and neuroscience underline the importance of a phantom that provides calibration means and allows the optimization of diffusion sequences with regard to the precision of the derived scalar diffusion parameters. Several recent projects have addressed the reliability of diffusion measurements and analysis using phantom studies (7-10). The results show that diffusion measures such as the ADC and the FA value are sensitive to the diameter of the artificial pha...

Applicability of intravoxel incoherent motion (IVIM) imaging in the clinical setting is hampered by the limited reliability in particular of the perfusion-related parameter estimates. To alleviate this problem, various advanced postprocessing methods have been introduced. However, the underlying algorithms are not readily available and generally suffer from an increased computational burden. Contrary, several computationally fast image denoising methods have recently been proposed which are accessible online and may improve reliability of IVIM parameter estimates. The objective of the present work is to investigate the impact of image denoising on accuracy and precision of IVIM parameter estimates using comprehensive in-silico and in-vivo experiments. Image denoising is performed with four different algorithms that work on magnitude data: two algorithms which are based on nonlocal means (NLM) filtering, one algorithm that relies on local principal component analysis (LPCA) of the diffusion-weighted images, and another algorithms that exploits joint rank and edge constraints (JREC). Accuracy and precision of IVIM parameter estimates is investigated in an in-silico brain phantom and an in-vivo ground truth as a function of the signal-to-noise ratio for spatially homogenous and inhomogenous levels of Rician noise. Moreover, precision is evaluated using bootstrap analysis of in-vivo measurements. In the experiments, IVIM parameters are computed a) by using a segmented fit method and b) by performing a biexponential fit of the entire attenuation curve based on nonlinear least squares estimates. Irrespective of the fit method, the results demonstrate that reliability of IVIM parameter estimates is substantially improved by image denoising. The experiments show that the LPCA and the JREC algorithms perform in a similar manner and outperform the NLM-related methods. Relative to noisy data, accuracy of the IVIM parameters in the in-silico phantom improves after image denoising by 76–79%, 79–81%, 84–99% and precision by 74–80%, 80–83%, 84–95% for the perfusion fraction, the diffusion coefficient, and the pseudodiffusion coefficient, respectively, when the segmented fit method is used. Beyond that, the simulations reveal that denoising performance is not impeded by spatially inhomogeneous levels of Rician noise in the image. Since all investigated algorithms are freely available and work on magnitude data they can be readily applied in the clinical setting which may foster transition of IVIM imaging into clinical practice.

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