Prospective Regularization Analysis and Design for Prior-Image-Based Reconstruction of X-ray CT

Prior-image-based reconstruction (PIBR) methods, which incorporate a high-quality patient-specific prior image into the reconstruction of subsequent low-dose CT acquisitions, have demonstrated great potential to dramatically reduce data fidelity requirements while maintaining or improving image quality. However, one challenge with the PIBR methods is in the selection of the prior image regularization parameter which controls the balance between information from current measurements and information from the prior image. Too little prior information yields few improvements for PIBR, and too much prior information can lead to PIBR results too similar to the prior image obscuring or misrepresenting features in the reconstruction. While exhaustive parameter searches can be used to establish prior image regularization strength, this process can be time consuming (involving a series of iterative reconstructions) and particular settings may not generalize for different acquisition protocols, anatomical sites, patient sizes, etc. Moreover, optimal regularization strategies can be dependent on the location within the object further complicating selection. In this work, we propose a novel approach for prospective analysis of PIBR. The methodology can be used to determine prior image regularization strength to admit specific anatomical changes without the need to perform iterative reconstructions in advance. The same basic methodology can also be used to prescribe uniform (shift-invariant) admission of change throughout the entire imaging field of view. The proposed predictive analytical approach was investigated in two phantom studies, and compared with the results from exhaustive search based on numerous iterative reconstructions. The experimental results show that the proposed analytical approach has high accuracy in predicting the admission of specific anatomical features, allowing for prospective determination of the prior image regularization parameter.

[1]  Lei Xing,et al.  Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints , 2012, Physics in medicine and biology.

[2]  Jeffrey H Siewerdsen,et al.  Prospective regularization design in prior-image-based reconstruction , 2015, Physics in medicine and biology.

[3]  E. Hoffman,et al.  Ultra-low dose lung CT perfusion regularized by a previous scan. , 2009, Academic radiology.

[4]  J H Siewerdsen,et al.  Reconstruction of difference in sequential CT studies using penalized likelihood estimation , 2016, Physics in medicine and biology.

[5]  Meng Wu,et al.  Approximate path seeking for statistical iterative reconstruction , 2015, Medical Imaging.

[6]  Jeffrey H Siewerdsen,et al.  PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction , 2013, Physics in medicine and biology.

[7]  J. Fessler,et al.  Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs , 1996, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[8]  Jeffrey A. Fessler,et al.  Resolution Properties of Regularized Image Reconstruction Methods , 2002 .

[9]  Jing Wang,et al.  Deriving adaptive MRF coefficients from previous normal-dose CT scan for low-dose image reconstruction via penalized weighted least-squares minimization. , 2014, Medical physics.

[10]  Qianjin Feng,et al.  Low-dose computed tomography image restoration using previous normal-dose scan. , 2011, Medical physics.

[11]  Jeffrey H Siewerdsen,et al.  Information Propagation in Prior-Image-Based Reconstruction. , 2012, Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography.

[12]  Jing Huang,et al.  Iterative Reconstruction for X-Ray Computed Tomography Using Prior-Image Induced Nonlocal Regularization , 2014, IEEE Transactions on Biomedical Engineering.

[13]  Jianhua Ma,et al.  Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images , 2016, IEEE Transactions on Medical Imaging.

[14]  Ella A Kazerooni,et al.  The solitary pulmonary nodule. , 2003, Chest.

[15]  J H Siewerdsen,et al.  dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images , 2014, Physics in medicine and biology.

[16]  Jie Tang,et al.  Low radiation dose C-arm cone-beam CT based on prior image constrained compressed sensing (PICCS): including compensation for image volume mismatch between multiple data acquisitions , 2009, Medical Imaging.

[17]  Wei Xu,et al.  Efficient low-dose CT artifact mitigation using an artifact-matched prior scan. , 2012, Medical physics.

[18]  Hakan Erdogan,et al.  Ordered subsets algorithms for transmission tomography. , 1999, Physics in medicine and biology.

[19]  Jerry L. Prince,et al.  Penalized-likelihood reconstruction for sparse data acquisitions with unregistered prior images and compressed sensing penalties , 2011, Medical Imaging.