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In ultrasound imaging the appearance of homogeneous regions of tissue issubject to speckle, which for certain applications can make the detection oftissue irregularities difficult. To cope with this, it is common practice toapply speckle reduction filters to the images. Most conventional filteringtechniques are fairly hand-crafted and often need to be finely tuned to thepresent hardware, imaging scheme and application. Learning based techniques onthe other hand suffer from the need for a target image for training (in case offully supervised techniques) or require narrow, complex physics-based models ofthe speckle appearance that might not apply in all cases. With this work wepropose a deep-learning based method for speckle removal without theselimitations. To enable this, we make use of realistic ultrasound simulationtechniques that allow for instantiation of several independent specklerealizations that represent the exact same tissue, thus allowing for theapplication of image reconstruction techniques that work with pairs ofdifferently corrupted data. Compared to two other state-of-the-art approaches(non-local means and the Optimized Bayesian non-local means filter) our methodperforms favorably in qualitative comparisons and quantitative evaluation,despite being trained on simulations alone, and is several orders of magnitudefaster.

Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data