Investigation of maximum a posteriori probability expectation-maximization for image-based weighting spectral X-ray CT image reconstruction.

Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.

Investigation of contrast-enhanced subtracted breast CT images with MAP-EM based on projection-based weighting imaging.

Contrast-enhanced subtracted breast computer tomography (CESBCT) images acquired using energy-resolved photon counting detector can be helpful to enhance the visibility of breast tumors. In such technology, one challenge is the limited number of photons in each energy bin, thereby possibly leading to high noise in separate images from each energy bin, the projection-based weighted image, and the subtracted image. In conventional low-dose CT imaging, iterative image reconstruction provides a superior signal-to-noise compared with the filtered back projection (FBP) algorithm. In this paper, maximum a posteriori expectation maximization (MAP-EM) based on projection-based weighting imaging for reconstruction of CESBCT images acquired using an energy-resolving photon counting detector is proposed, and its performance was investigated in terms of contrast-to-noise ratio (CNR). The simulation study shows that MAP-EM based on projection-based weighting imaging can improve the CNR in CESBCT images by 117.7%-121.2% compared with FBP based on projection-based weighting imaging method. When compared with the energy-integrating imaging that uses the MAP-EM algorithm, projection-based weighting imaging that uses the MAP-EM algorithm can improve the CNR of CESBCT images by 10.5%-13.3%. In conclusion, MAP-EM based on projection-based weighting imaging shows significant improvement the CNR of the CESBCT image compared with FBP based on projection-based weighting imaging, and MAP-EM based on projection-based weighting imaging outperforms MAP-EM based on energy-integrating imaging for CESBCT imaging.

An effective calculation method for an overlap volume histogram descriptor and its application in IMRT plan retrieval.

To effectively calculate an overlap volume histogram (OVH) descriptor and improve intensity modulated radiation treatment (IMRT) planning by basing it on previous plans with similar features, a method based on morphology for OVH calculation was proposed and a novel similarity measurement was employed for retrieval of a suitable IMRT plan. First, the minimum and maximum distances between the tumor and organs at risk (OARs) were calculated as the start and end points for contraction or expansion, and a suitable step size for contraction or expansion was determined according to these distances. Then, a dilation or erosion morphology operator was employed to compute the OVH descriptor. Finally, the performance of IMRT plan retrieval was evaluated, where the area between OVH descriptors was taken as the similarity measurement, and a 3D reconstruction for each case was also performed for visual comparison. Twenty-eight nasopharyngeal carcinoma (NPC) cases were evaluated. The results show that OVH descriptors can be calculated effectively with the proposed method, and match well to the 3D geometrical features of the tumor and OARs. Further, the IMRT plan retrieval results match well based on a visual inspection of their 3D geometrical features, and an increase of the area between OVH descriptors leads to a decrease of visual similarity. Therefore, the proposed method can be used effectively for the calculation of an OVH descriptor as well as the retrieval of similar IMRT cases.