Dictionary-based through-plane interpolation of prostate cancer T2-weighted MR images

T2-weighted magnetic resonance images (T2W MRI) of prostate cancer are usually acquired with a large slice thickness compared to in-plane voxel dimensions and to the minimal significant malignant prostate tumour size. This causes a negative partial volume effect, decreasing the precision of tumour volumetry and complicating 3D texture analysis of the images. At the same time, three orthogonal, anisotropic acquisitions with overlapping fields of view are often acquired to allow insight into the prostate from different anatomical planes. It is desirable to reconstruct an isotropic prostate T2W image, using the 3 orthogonal volumes computationally, instead of directly acquiring a high-resolution MR image, which typically requires elongated scanning time, with higher cost, less patient comfort and lower signal-to-noise ratio. In our previous work, we followed the above rationale applying a Markov-Random-Field(MRF)-based combination of 3 orthogonal T2W images of the prostate. Our initial results were, however, biased by the quality of input orthogonal images. These were first preprocessed using spline interpolation to yield the same voxel dimensions and later registered. In this paper, we apply a dictionary learning approach to interpolation in order to increase the resolution of a coronal T2W MRI image. We compose a low-resolution dictionary from the original axial image, calculate its sparse representation by Orthogonal Matching Pursuit and finally derive the high-resolution dictionary to improve the original coronal image. We assess the improvement in visual image quality as satisfying and propose further studies.