Painless Indirect Argon Laser in High Risk Proliferative Diabetic Retinopathy.

Background: Proliferative retinopathy is an important cause of vision loss in diabetic patients. Incomplete panretinal photocoagulation (PRP) can lead to recurrent proliferation of new vessels. Patients and Methods: We retrospectively analysed the outcome of patients with high risk proliferative diabetic retinopathy (PDR) previously treated with slit lamp PRP who underwent indirect fill in argon laser treatment with scleral indentation under anesthesia for persistent neovascular proliferation. Results: Seventeen eyes of ten patients were included. The mean age at diabetes onset was 17.3 years SD 16.2 (range 2-44). All patients reported long standing poor glycemic control (mean HbA1c: 8.5 % SD 1.3 range 5.9-10.2). The area of retinal ischemia decreased significantly from 15 ± 7.5 disk areas (DA) before fill-in laser to 3.2 ± 4.2 DA after fill-in laser (p = 0.001). The new vessels also regressed significantly after laser treatment 8.6 ± 6.1 DA before treatment versus 6.5 ± 6.4 DA after laser treatment, (p = 0.044). Quiescent PDR was reached in 10 eyes (58.8 %) at the last visit. Conclusions: Fill-in indirect argon laser under general anesthesia should be considered to achieve further new vessels regression in high risk PDR patients. Scleral indentation and absence of pain may allow for more extensive laser application.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.