IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Mar 2011
Simultaneous reconstruction of activity and attenuation for PET/MR.
Medical investigations targeting a quantitative analysis of the position emission tomography (PET) images require the incorporation of additional knowledge about the photon attenuation distribution in the patient. Today, energy range adapted attenuation maps derived from computer tomography (CT) scans are used to effectively compensate for image quality degrading effects, such as attenuation and scatter. Replacing CT by magnetic resonance (MR) is considered as the next evolutionary step in the field of hybrid imaging systems. ⋯ The adverse effects of scattered and accidental gamma coincidences on the quantitative accuracy of PET, as well as artifacts caused by the inherent crosstalk between activity and attenuation estimation are efficiently reduced using enhanced decay event localization provided by time-of-flight PET, accurate correction for accidental coincidences, and a reduced number of unknown attenuation coefficients. First results achieved with measured whole body PET data and reference segmentation from CT showed an absolute mean difference of 0.005 cm⁻¹ (< 20%) in the lungs, 0.0009 cm⁻¹ (< 2%) in case of fat, and 0.0015 cm⁻¹ (< 2%) for muscles and blood. The proposed method indicates a robust and reliable alternative to other MR-AC approaches targeting patient specific quantitative analysis in time-of-flight PET/MR.
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IEEE Trans Med Imaging · Mar 2011
Fast MR image reconstruction for partially parallel imaging with arbitrary k-space trajectories.
Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. ⋯ Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality.
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Spiral projection imaging (SPI) is a 3D, spiral based magnetic resonance imaging (MRI) acquisition scheme that allows for self-navigated motion estimation of all six degrees-of-freedom. The trajectory, a set of spiral planes, is enhanced to accommodate motion tracking by adding orthogonal planes. Rigid-body motion tracking is accomplished by comparing the overlapping data and deducing the motion that is consistent with the comparisons. ⋯ The artifacts of off-resonance, coils sensitivity, and gradient warping impose an unnotable effect on the accuracy of motion estimation. The worst mean accuracy is 0.15° and 0.20 mm for the phantom while the worst mean accuracy is 0.48° and 0.34 mm when imaging a brain, indicating that the nonrigid component in human subjects slightly degrades accuracy. When applied to in vivo motion, the proposed technique considerably reduces motion artifact.
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IEEE Trans Med Imaging · Feb 2011
Enhanced assessment of the wound-healing process by accurate multiview tissue classification.
With the widespread use of digital cameras, freehand wound imaging has become common practice in clinical settings. There is however still a demand for a practical tool for accurate wound healing assessment, combining dimensional measurements and tissue classification in a single user-friendly system. We achieved the first part of this objective by computing a 3-D model for wound measurements using uncalibrated vision techniques. ⋯ The main contribution of this paper is to overcome this drawback with a multiview strategy for tissue classification, relying on a 3-D model onto which tissue labels are mapped and classification results merged. The experimental classification tests demonstrate that enhanced repeatability and robustness are obtained and that metric assessment is achieved through real area and volume measurements and wound outline extraction. This innovative tool is intended for use not only in therapeutic follow-up in hospitals but also for telemedicine purposes and clinical research, where repeatability and accuracy of wound assessment are critical.
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IEEE Trans Med Imaging · Feb 2011
SNR dependence of optimal parameters for apparent diffusion coefficient measurements.
Optimizing the diffusion-weighted imaging (DWI) parameters (i.e., the b-value and the number of image averages) to the tissue of interest is essential for producing high-quality apparent diffusion coefficient (ADC) maps. Previous investigation of this optimization was performed assuming Gaussian noise statistics for the ADC map, which is only valid for high signal-to-noise ratio (SNR) imaging. In this work, the true statistics of the noise in ADC maps are derived, followed by an optimization of the DWI parameters as a function of the imaging SNR. ⋯ Incorporating the effects of T(2) weighting further increases the SNR dependence of the optimal parameters. The proposed optimization scheme is particularly important for high-resolution DWI, which intrinsically suffers from low SNR and therefore cannot afford the use of the conventional high b-values. Comparison scans were performed for high-resolution DWI of the spinal cord, demonstrating the improvements in the resulting images and the ADC maps achieved by this method.