Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning

Background:In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied to address the interior reconstruction problems for centered regions of interest (ROIs) with fixed sizes. Developing a method to enable interior tomography with arbitrarily located ROIs with nearly arbitrary ROI sizes inside a scanning field of view (FOV) remains an open question. Purpose:To develop a new pathway to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes using a single trained deep neural network model. Methods:The method consists of two steps. First, an analytical weighted backprojection reconstruction algorithm was developed to perform domain transform from divergent fan-beam projection data to an intermediate image feature space, B((x) over right arrow), for an arbitrary size ROI at an arbitrary location inside the FOV. Second, a supervised learning technique was developed to train a deep neural network architecture to perform deconvolution to obtain the true image f ((x) over right arrow) from the new feature space B((x) over right arrow). This two-step method is referred to as Deep-Interior for convenience. Both numerical simulations and experimental studies were performed to validate the proposed Deep-Interior method. Results:The results showed that ROIs as small as a diameter of 5 cm could be accurately reconstructed (similarity index 0.985 +/- 0.018 on internal testing data and 0.940 +/- 0.025 on external testing data) at arbitrary locations within an imaging object covering a wide variety of anatomical structures of different body parts. Besides, ROIs of arbitrary size can be reconstructed by stitching small ROIs without additional training. Conclusion:The developed Deep-Interior framework can enable interior tomographic reconstruction from divergent fan-beam projections for short-scan and super-short-scan acquisitions for small ROIs (with a diameter larger than 5 cm) at an arbitrary location inside the scanning FOV with high quantitative reconstruction accuracy.

Automated summary

This study developed a new method using a single trained deep neural network model to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes. The proposed method utilizes an analytical weighted backprojection reconstruction algorithm and a supervised learning technique to achieve accurate reconstruction of small ROIs within the scanning FOV with high quantitative reconstruction accuracy.