Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography最新文献

{"title":"Mixed Confidence Estimation for Iterative CT Reconstruction.","authors":"David S Perlmutter,&nbsp;Soo Mee Kim,&nbsp;Paul E Kinahan,&nbsp;Adam M Alessio","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present a statistical analysis of our previously proposed Constrain-Static Target-Kinetic algorithm for 4D CT reconstruction. This method, where framed iterative reconstruction is only performed on the dynamic regions of each frame, while static regions are fixed across frames to a composite image, was proposed to reduce computation time. In this work, we generalize the previous method to describe any application where a portion of the image is known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the image space into higher and lower confidence components, CSTK can lower the estimator variance in both regions compared to conventional reconstruction. We present a theoretical argument for this reduction in estimator variance and verify this argument with proof-of-principle simulations. This method allows for reduced computation time and improved image quality for imaging scenarios where portions of the image are known with more certainty than others.</p>","PeriodicalId":90477,"journal":{"name":"Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography","volume":"2014 ","pages":"29-32"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4307401/pdf/nihms655335.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33014802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iterative CT Reconstruction using Models of Source and Detector Blur and Correlated Noise.","authors":"Steven Tilley,&nbsp;Jeffrey H Siewerdsen,&nbsp;J Webster Stayman","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Statistical model-based reconstruction methods derive much of their advantage over traditional methods through more accurate forward models of the imaging system. Typical forward models fail to integrate two important aspects of real imaging systems: system blur and noise correlations in the measurements. This work develops an approach that models both aspects using a two-stage approach that includes a regularization deblurring operation followed by generalized penalized weighted least-squares reconstruction. Different reconstruction noise models including standard uncorrelated and correlated presumptions were explored. Moreover, different imaging systems were investigated in which blur was dominated by source effects, dominated by detector effects, or by a combination of source and detector blur. The proposed reconstruction approach that models the correlated noise demonstrated the best performance across all scenarios with the greatest benefits for increased source blur and for reconstructions with finer spatial resolution. This suggests potential application of the method for high resolution systems like dedicated flat-panel cone-beam CT (e.g., head, extremity, dental, mammography scanners) where system resolution is limited by both source and detector blur effects and noise correlations in measurement data are traditionally ignored.</p>","PeriodicalId":90477,"journal":{"name":"Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography","volume":"2014 ","pages":"363-367"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4207223/pdf/nihms612692.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32772941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Component Knowledge in Penalized-Likelihood Reconstruction with Morphological and Spectral Uncertainties.","authors":"J Webster Stayman,&nbsp;Steven Tilley,&nbsp;Jeffrey H Siewerdsen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Previous investigations [1-3] have demonstrated that integrating specific knowledge of the structure and composition of components like surgical implants, devices, and tools into a model-based reconstruction framework can improve image quality and allow for potential exposure reductions in CT. Using device knowledge in practice is complicated by uncertainties in the exact shape of components and their particular material composition. Such unknowns in the morphology and attenuation properties lead to errors in the forward model that limit the utility of component integration. In this work, a methodology is presented to accommodate both uncertainties in shape as well as unknown energy-dependent attenuation properties of the surgical devices. This work leverages the so-called known-component reconstruction (KCR) framework [1] with a generalized deformable registration operator and modifications to accommodate a spectral transfer function in the component model. Moreover, since this framework decomposes the object into separate background anatomy and \"known\" component factors, a mixed fidelity forward model can be adopted so that measurements associated with projections through the surgical devices can be modeled with much greater accuracy. A deformable KCR (dKCR) approach using the mixed fidelity model is introduced and applied to a flexible wire component with unknown structure and composition. Image quality advantages of dKCR over traditional reconstruction methods are illustrated in cone-beam CT (CBCT) data acquired on a testbench emulating a 3D-guided needle biopsy procedure - i.e., a deformable component (needle) with strong energy-dependent attenuation characteristics (steel) within a complex soft-tissue background.</p>","PeriodicalId":90477,"journal":{"name":"Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography","volume":"2014 ","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211110/pdf/nihms612683.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32783084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information Propagation in Prior-Image-Based Reconstruction.","authors":"J Webster Stayman,&nbsp;Jerry L Prince,&nbsp;Jeffrey H Siewerdsen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Advanced reconstruction methods for computed tomography include sophisticated forward models of the imaging system that capture the pertinent physical processes affecting the signal and noise in projection measurements. However, most do little to integrate prior knowledge of the subject - often relying only on very general notions of local smoothness or edges. In many cases, as in longitudinal surveillance or interventional imaging, a patient has undergone a sequence of studies prior to the current image acquisition that hold a wealth of prior information on patient-specific anatomy. While traditional techniques tend to treat each data acquisition as an isolated event and disregard such valuable patient-specific prior information, some reconstruction methods, such as PICCS[1] and PIR-PLE[2], can incorporate prior images into a reconstruction objective function. Inclusion of such information allows for dramatic reduction in the data fidelity requirements and more robustly accommodate substantial undersampling and exposure reduction with consequent benefits to imaging speed and reduced radiation dose. While such prior-image-based methods offer tremendous promise, the introduction of prior information in the reconstruction raises significant concern regarding the accurate representation of features in the image and whether those features arise from the current data acquisition or from the prior images. In this work we propose a novel framework to analyze the propagation of information in prior-image-based reconstruction by decomposing the estimation into distinct components supported by the current data acquisition and by the prior image. This decomposition quantifies the contributions from prior and current data as a spatial map and can trace specific features in the image to their source. Such \"information source maps\" can potentially be used as a check on confidence that a given image feature arises from the current data or from the prior and to more quantitatively guide the selection of parameter values affecting the strength of prior information in the resulting image.</p>","PeriodicalId":90477,"journal":{"name":"Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography","volume":"2012 ","pages":"334-338"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888030/pdf/nihms786580.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34448998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}