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Purpose: Mean retinal sensitivity is the main output measure used in microperimetry. It is, however, of limited use in patients with poor vision because averaging is weighted toward zero in those with significant scotomas creating an artificial floor effect. In contrast, volumetric measures avoid these issues and are displayed graphically as a hill of vision. Methods: An open-source program was created to manipulate raw sensitivity threshold data files obtained from MAIA microperimetry. Thin plate spline interpolated heat maps and three-dimensional hill of vision plots with an associated volume were generated. Retrospective analyses of microperimetry volumes were undertaken in patients with a range of retinal diseases to assess the qualitative benefits of three-dimensional visualization and volumetric measures. Simulated pathology was applied to radial grid patterns to investigate the performance of volumetric sensitivity in nonuniform grids. Results: Volumetric analyses from microperimetry in RPGR-related retinitis pigmentosa, choroideremia, Stargardt disease, and age-related macular degeneration were analyzed. In simulated nonuniform testing grids, volumetric sensitivity was able to detect differences in retinal sensitivity where mean sensitivity could not. Conclusions: Volumetric measures do not suffer from averaging issues and demonstrate superior performance in nonuniform testing grids. Additionally, volume measures enable detection of localized retinal sensitivity changes that might otherwise be undetectable in a mean change. Translational Relevance: As microperimetry has become an outcome measure in several gene-therapy clinical trials, three-dimensional visualization and volumetric sensitivity enables a complementary analysis of baseline disease characteristics and subsequent response to treatment, both as a signal of safety and efficacy.

Original publication




Journal article


Transl Vis Sci Technol

Publication Date





Eye Proteins, Humans, Retina, Retrospective Studies, Visual Acuity, Visual Field Tests, Visual Fields