MR Image Reconstruction Using a Combination of Compressed Sensing and Partial Fourier Acquisition: ESPReSSo.

A Cartesian subsampling scheme is proposed incorporating the idea of PF acquisition and variable-density Poisson Disc (vdPD) subsampling by redistributing the sampling space onto a smaller region aiming to increase k-space sampling density for a given acceleration factor. Especially the normally sparse sampled high-frequency components benefit from this sampling redistribution, leading to improved edge delineation. The prospective subsampled and compacted k-space can be reconstructed by a seamless combination of a CS-algorithm with a Hermitian symmetry constraint accounting for the missing part of the k-space. This subsampling and reconstruction scheme is called Compressed Sensing Partial Subsampling (ESPReSSo) and was tested on in-vivo abdominal MRI datasets. Different reconstruction methods and regularizations are investigated and analyzed via global (intensity-based) and local (region-of-interest and line evaluation) image metrics, to conclude a clinical feasible setup. Results substantiate that ESPReSSo can provide improved edge delineation and regional homogeneity for multidimensional and multi-coil MRI datasets and is therefore useful in applications depending on well-defined tissue boundaries, such as image registration and segmentation or detection of small lesions in clinical diagnostics.

Automated volumetric assessment of the Achilles tendon (AVAT) using a 3D T2 weighted SPACE sequence at 3T in healthy and pathologic cases.

PURPOSE: Achilles tendinopathy has been reported to be frequently associated with increasing volume of the tendon. This work aims at reliable and accurate volumetric quantification of the Achilles tendon using a newly developed contour detection algorithm applied on high resolution MRI data sets recorded at 3T. MATERIALS AND METHODS: A total of 26 healthy tendons and 4 degenerated tendons were examined for this study. Automated identification (AI) of tendon boundaries was performed in transverse slices with isotropic resolution (0.8mm) gained with a T2-weighted SPACE sequence at 3T. For AI a snake algorithm was applied and compared to manual tracing (MT). RESULTS: AI was feasible in all examined tendons without further correction. AI of both tendons was performed in each participant within 2 min (2 × 37 slices) compared to MT lasting 20 min. MT and AI showed excellent agreement and correlation (R(2) = 0.99, p<0.0001). AI provided a reduction of measurement error (0.4 cm(3) vs. 0.5 cm(3)) and coefficient of variation (1% vs. 2%). DISCUSSION: Compared to MT the AI allows assessment of tendon volumes in highly resolved MRI data in a more accurate and reliable time-saving way. Therefore automated volume detection is seen as a helpful clinical tool for evaluation of small volumetric changes of the Achilles tendon.