Three-dimensional (3D) imaging is extremely popular in medical imaging as it enables diagnosis and disease monitoring through complete anatomical coverage. Computed Tomography or Magnetic Resonance Imaging (MRI) techniques are commonly used, however, anisotropic volumes with thick slices are often acquired to reduce scan times. Deep learning (DL) can be used to recover high-resolution features in the low-resolution dimension through super-resolution reconstruction (SRR). However, this often relies on paired training data which is unavailable in many medical applications. We describe a novel approach that only requires native anisotropic 3D medical images for training. This method relies on the observation that small 2D patches extracted from a 3D volume contain similar visual features, irrespective of their orientation. Therefore, it is possible to leverage disjoint patches from the high-resolution plane, to learn SRR in the low-resolution plane. Our proposed unpaired approach uses a modified CycleGAN architecture with a cycle-consistent gradient mapping loss: Cycle Loss Augmented Degradation Enhancement (CLADE). We show the feasibility of CLADE in an exemplar application; anisotropic 3D abdominal MRI data. We demonstrate superior quantitative image quality with CLADE over supervised learning and conventional CycleGAN architectures. CLADE also shows improvements over anisotopic volumes in terms of qualitative image ranking and quantitative edge sharpness and signal-to-noise ratio. This paper demonstrates the potential of using CLADE for super-resolution reconstruction of anisotropic 3D medical imaging data without the need for paired training data.

To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets. This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n 22; mean age, 44 years 13 [standard deviation]; nine men) or shoulder (n 32; mean age, 56 years 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence.