motioncorrection.bib

% bib

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% bib for state of the art 
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% USED
% Article about 4D CBCT (no algorithms, FDK and phase binning) in real patients
@article{thomas2006,
author = {Thomas G. Purdie and Douglas J. Moseley and Jean-Pierre Bissonnette and Michael B. Sharpe and Kevin Franks and Andrea Bezjak and David A. Jaffray},
title = {Respiration correlated cone-beam computed tomography and {4DCT} for evaluating target motion in Stereotactic Lung Radiation Therapy},
journal = {Acta Oncologica},
volume = {45},
number = {7},
pages = {915-922},
year = {2006},
doi = {10.1080/02841860600907345},

URL = { 
        http://dx.doi.org/10.1080/02841860600907345
    
},
eprint = { 
        http://dx.doi.org/10.1080/02841860600907345
    
}
}


% USED
% 4D CBCT by enforcing Non-Local means temporally (N images reconstructed)
% BY DIONG FDK
@article{jia2012,
  title={Four-dimensional cone beam {CT} reconstruction and enhancement using a temporal nonlocal means method},
  author={Jia, Xun and Tian, Zhen and Lou, Yifei and Sonke, Jan-Jakob and Jiang, Steve B},
  journal={Medical physics},
  volume={39},
  number={9},
  pages={5592--5602},
  year={2012},
  publisher={American Association of Physicists in Medicine}
}

%USED
% 27: Solving a parametric DVF using NCG by comparing results to projections, reducing the CBCT projections to 64
@article{brock2010,
  title={Reconstruction of a cone-beam {CT} image via forward iterative projection matching},
  author={Brock, R Scott and Docef, Alen and Murphy, Martin J},
  journal={Medical physics},
  volume={37},
  number={12},
  pages={6212--6220},
  year={2010},
  publisher={American Association of Physicists in Medicine}
}
%USED
% Fit CBCT to PCA'd DVFs. "patient specific" FDK
@article{zhang2010correction,
  title={Correction of motion artifacts in cone-beam {CT} using a patient-specific respiratory motion model},
  author={Zhang, Qinghui and Hu, Yu-Chi and Liu, Fenghong and Goodman, Karyn and Rosenzweig, Kenneth E and Mageras, Gig S},
  journal={Medical physics},
  volume={37},
  number={6},
  pages={2901--2909},
  year={2010},
  publisher={American Association of Physicists in Medicine}
}

%USED

% 36: Solving a parametric {{DVF} using {NCG} by comparing results to projections, reducing the {CBCT} projections to 57, clinical study   FDK
@article{Ren20121584,
title = "Development and Clinical Evaluation of a Three-Dimensional Cone-Beam Computed Tomography Estimation Method Using a Deformation Field Map ",
journal = "International Journal of Radiation Oncology*Biology*Physics ",
volume = "82",
number = "5",
pages = "1584 - 1593",
year = "2012",
note = "",
issn = "0360-3016",
doi = "http://dx.doi.org/10.1016/j.ijrobp.2011.02.002",
url = "http://www.sciencedirect.com/science/article/pii/S0360301611002094",
author = "Lei Ren and Indrin J. Chetty and Junan Zhang and Jian-Yue Jin and Q. Jackie Wu and Hui Yan and David M. Brizel and W. Robert Lee and Benjamin Movsas and Fang-Fang Yin",
keywords = "Imaging dose reduction",
keywords = "Image reconstruction",
keywords = "Deformable registration",
keywords = "Image-guided radiation therapy",
keywords = "Cone-beam \{CT\} (CBCT) ",
 "
}


% USED
% DVF , PCA, projection matching..... Just 1 projection in this case
@article{:/content/aapm/journal/medphys/37/6/10.1118/1.3426002,
   author = "Li, Ruijiang and Jia, Xun and Lewis, John H. and Gu, Xuejun and Folkerts, Michael and Men, Chunhua and Jiang, Steve B.",
   title = "Real-time volumetric image reconstruction and {3D} tumor localization based on a single {X}-ray projection image for lung cancer radiotherapy",
   journal = "Medical Physics",
   year = "2010",
   volume = "37",
   number = "6", 
   eid = ,
   pages = "2822-2826",
   url = "http://scitation.aip.org/content/aapm/journal/medphys/37/6/10.1118/1.3426002",
   doi = "http://dx.doi.org/10.1118/1.3426002" 
}% USED
% continuation. Some improvements and real data
@article{:/content/aapm/journal/medphys/38/5/10.1118/1.3582693,
   author = "Li, Ruijiang and Lewis, John H. and Jia, Xun and Gu, Xuejun and Folkerts, Michael and Men, Chunhua and Song, William Y. and Jiang, Steve B.",
   title = "{3D} tumor localization through real-time volumetric {X}-ray imaging for lung cancer radiotherapy",
   journal = "Medical Physics",
   year = "2011",
   volume = "38",
   number = "5", 
   eid = ,
   pages = "2783-2794",
   url = "http://scitation.aip.org/content/aapm/journal/medphys/38/5/10.1118/1.3582693",
   doi = "http://dx.doi.org/10.1118/1.3582693" 
}


%USED
% Same thing, Solve DVF with NCG, but instead of FDK, ASD-POCS is used for 4D-CBCT (no temporal constrain)
@article{wang2012high,
  title={High-quality four-dimensional cone-beam {CT} by deforming prior images},
  author={Wang, Jing and Gu, Xuejun},
  journal={Physics in medicine and biology},
  volume={58},
  number={2},
  pages={231},
  year={2012},
  publisher={IOP Publishing}
}

%USED
% 1) CBCT 2)4dCBCT (TV-spatial+temporal) 3)optical flow 4)better 4D-CBCT
%
% complicated multi step algorithm
@article{christoffersen2013registration,
  title={Registration-based reconstruction of four-dimensional cone beam computed tomography},
  author={Christoffersen, Christian PV and Hansen, David and Poulsen, Per and S{\o}rensen, Thomas Sangild},
  journal={IEEE Transactions on Medical Imaging},
  volume={32},
  number={11},
  pages={2064--2077},
  year={2013},
  publisher={IEEE}
}


%USED
% THE PICCS algorithm, here the desccription to dynamic CT is explained
@article{chen2008prior,
  title={Prior image constrained compressed sensing {(PICCS)}: a method to accurately reconstruct dynamic {CT} images from highly undersampled projection data sets},
  author={Chen, Guang-Hong and Tang, Jie and Leng, Shuai},
  journal={Medical physics},
  volume={35},
  number={2},
  pages={660--663},
  year={2008},
  publisher={American Association of Physicists in Medicine}
}
%USED

% PICCS (reg:  alpha*TV(x-xprior)+ (a-alpha)*TV(x)) in  4DCBCT
@article{chen2012time,
  title={Time-resolved interventional cardiac {C}-arm cone-beam {CT}: an application of the {PICCS} algorithm},
  author={Chen, Guang-Hong and Th{\'e}riault-Lauzier, Pascal and Tang, Jie and Nett, Brian and Leng, Shuai and Zambelli, Joseph and Qi, Zhihua and Bevins, Nicholas and Raval, Amish and Reeder, Scott and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={31},
  number={4},
  pages={907--923},
  year={2012},
  publisher={IEEE}
}

%USED

% PICCS again
@article{0031-9155-53-20-006,
  author={Shuai Leng and Jie Tang and Joseph Zambelli and Brian Nett and Ranjini Tolakanahalli and Guang-Hong Chen},
  title={High temporal resolution and streak-free four-dimensional cone-beam computed tomography},
  journal={Physics in Medicine and Biology},
  volume={53},
  number={20},
  pages={5653},
  url={http://stacks.iop.org/0031-9155/53/i=20/a=006},
  year={2008},
  abstract={Cone-beam computed tomography (CBCT) has been clinically used to verify patient position and to localize the target of treatment in image-guided radiation therapy (IGRT). However, when the chest and the upper abdomen are scanned, respiratory-induced motion blurring limits the utility of CBCT. In order to mitigate this blurring, respiratory-gated CBCT, i.e. 4D CBCT, was introduced. In 4D CBCT, the cone-beam projection data sets acquired during a gantry rotation are sorted into several respiratory phases. In these gated reconstructions, the number of projections for each respiratory phase is significantly reduced. Consequently, undersampling streaking artifacts are present in the reconstructed images, and the image contrast resolution is also significantly compromised. In this paper, we present a new method to simultaneously achieve both high temporal resolution (~100 ms) and streaking artifact-free image volumes in 4D CBCT. The enabling technique is a newly proposed image reconstruction method, i.e. prior image constrained compressed sensing (PICCS), which enables accurate image reconstruction using vastly undersampled cone-beam projections and a fully sampled prior image. Using PICCS, a streak-free image can be reconstructed from 10?20 cone-beam projections while the signal-to-noise ratio is determined by a denoising feature of the selected objective function and by the prior image, which is reconstructed using all of the acquired cone-beam projections. This feature of PICCS breaks the connection between the temporal resolution and streaking artifacts' level in 4D CBCT. Numerical simulations and experimental phantom studies have been conducted to validate the method.}
}


% 5D papaer. No-binning! quite complex algorithm

@article{0266-5611-31-11-115007,
  author={Jiulong Liu and Xue Zhang and Xiaoqun Zhang and Hongkai Zhao and Yu Gao and David Thomas and Daniel A Low and Hao Gao},
  title={{5D} respiratory motion model based image reconstruction algorithm for {4D} cone-beam computed tomography},
  journal={Inverse Problems},
  volume={31},
  number={11},
  pages={115007},
  url={http://stacks.iop.org/0266-5611/31/i=11/a=115007},
  year={2015},
  abstract={4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.}
}
	

%Used
% 4DCBCT, DVF, TV temporal,spatial.Tigth frame,.... some complex stuff about reconstructing the static and moving image separetedly, and so on.
  	
@article{0031-9155-56-11-002,
  author={Hao Gao and Jian-Feng Cai and Zuowei Shen and Hongkai Zhao},
  title={Robust principal component analysis-based four-dimensional computed tomography},
  journal={Physics in Medicine and Biology},
  volume={56},
  number={11},
  pages={3181},
  url={http://stacks.iop.org/0031-9155/56/i=11/a=002},
  year={2011},
  abstract={The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames.}
}

%USED
% 4D ROOSTER
@article{:/content/aapm/journal/medphys/41/2/10.1118/1.4860215,
   author = "Mory, Cyril and Auvray, Vincent and Zhang, Bo and Grass, Michael and Schäfer, Dirk and Chen, S. James and Carroll, John D. and Rit, Simon and Peyrin, Françoise and Douek, Philippe and Boussel, Loïc",
   title = "Cardiac {C}-arm computed tomography using a {3D} + time {ROI} reconstruction method with spatial and temporal regularization",
   journal = "Medical Physics",
   year = "2014",
   volume = "41",
   number = "2", 
   eid = 021903,
   pages = "",
   url = "http://scitation.aip.org/content/aapm/journal/medphys/41/2/10.1118/1.4860215",
   doi = "http://dx.doi.org/10.1118/1.4860215" 
}
%USED
% Improvement of ROOSTER with DVF

@article{mory2016motion,
  title={Motion-aware temporal regularization for improved {4D} cone-beam computed tomography},
  author={Mory, Cyril and Janssens, Guillaume and Rit, Simon},
  journal={Physics in Medicine and Biology},
  volume={61},
  number={18},
  pages={6856},
  year={2016},
  publisher={IOP Publishing}
}

% USED
% AwTV 4DCBCT

	  	
@article{0031-9155-57-6-1517,
  author={Ludwig Ritschl and Stefan Sawall and Michael Knaup and Andreas Hess and Marc Kachelrie},
  title={Iterative {4D} cardiac micro-{CT} image reconstruction using an adaptive spatio-temporal sparsity prior},
  journal={Physics in Medicine and Biology},
  volume={57},
  number={6},
  pages={1517},
  url={http://stacks.iop.org/0031-9155/57/i=6/a=1517},
  year={2012},
  abstract={Temporal-correlated image reconstruction, also known as 4D CT image reconstruction, is a big challenge in computed tomography. The reasons for incorporating the temporal domain into the reconstruction are motions of the scanned object, which would otherwise lead to motion artifacts. The standard method for 4D CT image reconstruction is extracting single motion phases and reconstructing them separately. These reconstructions can suffer from undersampling artifacts due to the low number of used projections in each phase. There are different iterative methods which try to incorporate some a priori knowledge to compensate for these artifacts. In this paper we want to follow this strategy. The cost function we use is a higher dimensional cost function which accounts for the sparseness of the measured signal in the spatial and temporal directions. This leads to the definition of a higher dimensional total variation. The method is validated using in vivo cardiac micro-CT mouse data. Additionally, we compare the results to phase-correlated reconstructions using the FDK algorithm and a total variation constrained reconstruction, where the total variation term is only defined in the spatial domain. The reconstructed datasets show strong improvements in terms of artifact reduction and low-contrast resolution compared to other methods. Thereby the temporal resolution of the reconstructed signal is not affected.}
}
	

%USED
% $DCBCT triggered to minimize projections 	
	@article{t2016first,
  title={The first implementation of respiratory triggered {4DCBCT} on a linear accelerator},
  author={T O\'Brien, Ricky and Cooper, Benjamin J and Shieh, Chun-Chien and Stankovic, Uros and Keall, Paul J and Sonke, Jan-Jakob},
  journal={Physics in medicine and biology},
  volume={61},
  number={9},
  pages={3488},
  year={2016},
  publisher={IOP Publishing}
}






%USED
% Esoteric mesh-based reconstruction for 4DCBCT

  	
@article{0031-9155-61-3-996,
  author={Zichun Zhong and Xuejun Gu and Weihua Mao and Jing Wang},
  title={{4D} cone-beam {CT} reconstruction using multi-organ meshes for sliding motion modeling},
  journal={Physics in Medicine and Biology},
  volume={61},
  number={3},
  pages={996},
  url={http://stacks.iop.org/0031-9155/61/i=3/a=996},
  year={2016},
  abstract={A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp�Davis�Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.}
} The
	
	
	% Cine-CBCT some complex maths with low rank-matrices.....
	@ARTICLE{6803058, 
author={J. F. Cai and X. Jia and H. Gao and S. B. Jiang and Z. Shen and H. Zhao}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Cine Cone Beam {CT} Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study}, 
year={2014}, 
volume={33}, 
number={8}, 
pages={1581-1591}, 
keywords={computerised tomography;image reconstruction;matrix decomposition;medical image processing;pneumodynamics;4DCBCT;cine cone beam CT reconstruction;cone beam computed tomography;low-rank matrix factorization;proof-of-principle study;respiration-correlated CBCT;respiratory phase-resolved CBCT images;Approximation methods;Coherence;Image reconstruction;Matrix decomposition;Sparse matrices;Transforms;X-ray imaging;Cine cone beam computed tomography (CBCT);low-rank matrix;reconstruction;0}, 
doi={10.1109/TMI.2014.2319055}, 
ISSN={0278-0062}, 
month={Aug},}



%USED
% title
@article{sonke2005respiratory,
  title={Respiratory correlated cone beam {CT}},
  author={Sonke, Jan-Jakob and Zijp, Lambert and Remeijer, Peter and van Herk, Marcel},
  journal={Medical physics},
  volume={32},
  number={4},
  pages={1176--1186},
  year={2005},
  publisher={American Association of Physicists in Medicine}
}
% USED
% just binned 4DCBCT
@article{li2006four,
  title={Four-dimensional cone-beam computed tomography using an on-board imager},
  author={Li, Tianfang and Xing, Lei and Munro, Peter and McGuinness, Christopher and Chao, Ming and Yang, Yong and Loo, Bill and Koong, Albert},
  journal={Medical physics},
  volume={33},
  number={10},
  pages={3825--3833},
  year={2006},
  publisher={American Association of Physicists in Medicine}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% END STATE OF THE ART
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% FDK in medicine
@article{Pan2009,
  author={ Pan, X. and  Sidky, E.Y. and  Vannier, M.},
  title={Why do commercial {CT} scanners still employ traditional, filtered back-projection for image reconstruction?},
  journal={Inverse Problems},
  volume={25},
  number={12},
  pages={123009},
  url={http://stacks.iop.org/0266-5611/25/i=12/a=123009},
  year={2009},
  abstract={Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years�the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.}
}

%FDK
@article{Feldkamp1984, 
author = {L.A. Feldkamp and L.C. Davis and J.W. Kress}, 
journal = {J. Opt. Soc. Am. A}, 
keywords = {},
number = {6}, 
pages = {612--619}, 
publisher = {OSA},
title = {Practical cone-beam algorithm}, 
volume = {1}, 
month = {Jun},
year = {1984},
url = {http://josaa.osa.org/abstract.cfm?URI=josaa-1-6-612},
doi = {10.1364/JOSAA.1.000612},
abstract = {A convolution-backprojection formula is deduced for direct reconstruction of a three-dimensional density function from a set of two-dimensional projections. The formula is approximate but has useful properties, including errors that are relatively small in many practical instances and a form that leads to convenient computation. It reduces to the standard fan-beam formula in the plane that is perpendicular to the axis of rotation and contains the point source. The algorithm is applied to a mathematical phantom as an example of its performance.},
}

% Iterateive>FDK 1
@article{Beister2012,
title = "Iterative reconstruction methods in {X}-ray {CT} ",
journal = "Physica Medica ",
volume = "28",
number = "2",
pages = "94 - 108",
year = "2012",
note = "",
issn = "1120-1797",
doi = "http://dx.doi.org/10.1016/j.ejmp.2012.01.003",
url = "http://www.sciencedirect.com/science/article/pii/S112017971200004X",
author = " Beister, M. and  Kolditz, D. and  Kalender, W.A.",
keywords = "CT",
keywords = "Image reconstruction",
keywords = "Iterative reconstruction",
keywords = "Statistical reconstruction",
keywords = "Model-based reconstruction",
keywords = "Dose",
keywords = "Image quality ",
abstract = "Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of \{IR\} compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in \{CT\} have changed the situation; \{IR\} has become a hot topic for all major vendors of clinical \{CT\} systems in the past 5 years. This review strives to provide information on \{IR\} methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated \{CT\} scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of \{IR\} methods as well as open points for research in this field are discussed. "
}


@article{biguri2017general,
  title={A general method for motion compensation in {X}-ray computed tomography},
  author={Biguri, Ander and Dosanjh, Manjit and Hancock, Steven and Soleimani, Manuchehr},
  journal={Physics in Medicine and Biology},
  year={2017},
  publisher={IOP Publishing}
}


@Article{Pontana20102,
author="Pontana, F.
and Pagniez, J.
and Flohr, T.
and Faivre, J.
and Duhamel, A.
and Remy, J.
and Remy-Jardin, M.",
title="Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose {CT} examinations in 80 patients",
journal="European Radiology",
year="2010",
volume="21",
number="3",
pages="636--643",
abstract="To evaluate the image quality of an iterative reconstruction algorithm (IRIS) in low-dose chest CT in comparison with standard-dose filtered back projection (FBP) CT.",
issn="1432-1084",
doi="10.1007/s00330-010-1991-4",
url="http://dx.doi.org/10.1007/s00330-010-1991-4"
}
@Article{Pontana2010,
author="Pontana, F.
and Pagniez, J.
and Flohr, T.
and Faivre, J.
and Duhamel, A.
and Remy, J.
and Remy-Jardin, M.",
title="Chest computed tomography using iterative reconstruction vs filtered back projection (Part 1): evaluation of image noise reduction in 32 patients",
journal="European Radiology",
year="2010",
volume="21",
number="3",
pages="627--635",
abstract="To assess noise reduction achievable with an iterative reconstruction algorithm.",
issn="1432-1084",
doi="10.1007/s00330-010-1990-5",
url="http://dx.doi.org/10.1007/s00330-010-1990-5"
}

@article{coban2015,
  title={When do the iterative reconstruction methods become worth the effort?},
  author={Coban, S.B. and Withers, P.J. and Lionheart, W.R.B. and McDonald, S.A.},
  year={2015}
}

  	
@article{TIGRE,
  author={Ander Biguri and Manjit Dosanjh and Steven Hancock and Manuchehr Soleimani},
  title={{TIGRE}: a {MATLAB-GPU} toolbox for {CBCT} image reconstruction},
  journal={Biomedical Physics \& Engineering Express},
  volume={2},
  number={5},
  pages={055010},
  url={http://stacks.iop.org/2057-1976/2/i=5/a=055010},
  year={2016},
  abstract={In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of iterative algorithms as well as FDK, including a range of algorithms in the SART family, the Krylov subspace family and a range of methods using total variation regularization. Additionally, the toolbox has GPU-accelerated projection and back projection using the latest techniques and it has a modular design that facilitates the implementation of new algorithms. We present an overview of the structure and techniques used in the creation of the toolbox, together with two usage examples. The TIGRE Toolbox is released under an open source licence, encouraging people to contribute.}
}
	
@article{siddon1985fast,
  title={Fast calculation of the exact radiological path for a three-dimensional {CT} array},
  author={Siddon, R.L.},
  journal={Medical physics},
  volume={12},
  number={2},
  pages={252--255},
  year={1985},
  publisher={American Association of Physicists in Medicine}
}
@INPROCEEDINGS{jacobs1998fast, 
author={G. Han and Z. Liang and J. You}, 
booktitle={Nuclear Science Symposium, 1999. Conference Record. 1999 IEEE}, 
title={A fast ray-tracing technique for {TCT} and {ECT} studies}, 
year={1999}, 
volume={3}, 
pages={1515-1518 vol.3}, 
keywords={computerised tomography;emission tomography;Siddon's algorithm;decrement operation;emission computed tomography studies;fast ray-tracing technique;increment operation;medical diagnostic imaging;nuclear medicine;randomly generated projection rays;total computing time;transmission computed tomography;voxel indices;voxels tracing;Astronomy;Attenuation;Computational modeling;Computed tomography;Computer science;Distributed computing;Electrical capacitance tomography;Physics;Radiology;Ray tracing}, 
doi={10.1109/NSSMIC.1999.842846}, 
ISSN={1082-3654}, 
month={},}


@article{jia2012gpu,
  title={A {GPU} tool for efficient, accurate, and realistic simulation of cone beam {CT} projections},
  author={Jia, X. and Yan, H. and Cervi{\~n}o, L. and Folkerts, M. and Jiang, S.B}.,
  journal={Medical physics},
  volume={39},
  number={12},
  pages={7368--7378},
  year={2012},
  publisher={American Association of Physicists in Medicine}
}


@article{Chou2011,
   author = "Chou, Cheng-Ying and Chuo, Yi-Yen and Hung, Yukai and Wang, Weichung",
   title = "A fast forward projection using multithreads for multirays on {GPU}s in medical image reconstruction",
   journal = "Medical Physics",
   year = "2011",
   volume = "38",
   number = "7", 
   eid = ,
   pages = "4052-4065",
   url = "http://scitation.aip.org/content/aapm/journal/medphys/38/7/10.1118/1.3591994",
   doi = "http://dx.doi.org/10.1118/1.3591994" 
}
@article{Liu2007531,
title = "Assessing Respiration-Induced Tumor Motion and Internal Target Volume Using Four-Dimensional Computed Tomography for Radiotherapy of Lung Cancer ",
journal = "International Journal of Radiation Oncology*Biology*Physics ",
volume = "68",
number = "2",
pages = "531 - 540",
year = "2007",
note = "",
issn = "0360-3016",
doi = "http://dx.doi.org/10.1016/j.ijrobp.2006.12.066",
url = "http://www.sciencedirect.com/science/article/pii/S0360301607000922",
author = "H. Helen Liu and Peter Balter and Teresa Tutt and Bum Choi and Joy Zhang and Catherine Wang and Melinda Chi and Dershan Luo and Tinsu Pan and Sandeep Hunjan and George Starkschall and Isaac Rosen and Karl Prado and Zhongxing Liao and Joe Chang and Ritsuko Komaki and James D. Cox and Radhe Mohan and Lei Dong",
keywords = "4DCT",
keywords = "Tumor motion",
keywords = "Respiration",
keywords = "Internal target volume",
keywords = "Lung cancer "
}

@inproceedings{vandemeulebroucke2007popi,
  title={The {POPI}-model, a point-validated pixel-based breathing thorax model},
  author={Vandemeulebroucke, Jef and Sarrut, David and Clarysse, Patrick and others},
  year={2007}
}

@article{ASD_POCS,

  author={Sidky, E.Y. and Pan, X.},

  title={Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization},

  journal={Physics in Medicine and Biology},

  volume={53},

  number={17},

  pages={4777},

  url={http://stacks.iop.org/0031-9155/53/i=17/a=021},

  year={2008},

  abstract={An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.}

}


@article{forwardproj,
   author = "Chou, Cheng-Ying and Chuo, Yi-Yen and Hung, Yukai and Wang, Weichung",
   title = "A fast forward projection using multithreads for multirays on {GPU}s in medical image reconstruction",
   journal = "Medical Physics",
   year = "2011",
   volume = "38",
   number = "7", 
   eid = ,
   pages = "4052-4065",
   url = "http://scitation.aip.org/content/aapm/journal/medphys/38/7/10.1118/1.3591994",
   doi = "http://dx.doi.org/10.1118/1.3591994" 
}

@article{zinsser2013systematic,
  title={Systematic performance optimization of cone-beam back-projection on the Kepler architecture},
  author={Zinsser, Timo and Keck, Benjamin},
  journal={Proceedings of the 12th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
  pages={225--228},
  year={2013},
  publisher={Citeseer}
}
@INPROCEEDINGS{Papenhausen11gpu-acceleratedback-projecting,
    author = {Eric Papenhausen and Ziyi Zheng and Klaus Mueller},
    title = {GPU-Accelerated Back-Projecting Revisited: Squeezing Performance by Careful Tuning},
    booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
    year = {2011}
}
@inproceedings{kaseberg2013opencl,
  title={{OpenCL} accelerated multi-{GPU} cone-beam reconstruction},
  author={K{\"a}seberg, Marc and Melnik, Steffen and Keeve, Erwin},
  booktitle={Proceedings of The 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},
  pages={477--480},
  year={2013}
}

@article{SART,

  title={Simultaneous algebraic reconstruction technique ({SART}): a superior implementation of the {ART} algorithm},

  author={Andersen, A.H. and Kak, A.C.},

  journal={Ultrasonic imaging},

  volume={6},

  number={1},

  pages={81--94},

  year={1984},

  publisher={SAGE Publications}

}
@article{wang2002universal,

  title={A universal image quality index},

  author={Wang, Zhou and Bovik, Alan C},

  journal={IEEE signal processing letters},

  volume={9},

  number={3},

  pages={81--84},

  year={2002},

  publisher={IEEE}

}
@article{otsu1975threshold,
  title={A threshold selection method from gray-level histograms},
  author={Otsu, Nobuyuki},
  journal={Automatica},
  volume={11},
  number={285-296},
  pages={23--27},
  year={1975}
}



@article{liu2013nonparametric,
  title={Nonparametric optimization of constrained total variation for tomography reconstruction},
  author={Liu, Li and Yin, Zhaofang and Ma, Xueyun},
  journal={Computers in biology and medicine},
  volume={43},
  number={12},
  pages={2163--2176},
  year={2013},
  publisher={Elsevier}
}
@article{tian2011low,
  title={Low-dose {CT} reconstruction via edge-preserving total variation regularization},
  author={Tian, Zhen and Jia, Xun and Yuan, Kehong and Pan, Tinsu and Jiang, Steve B},
  journal={Physics in medicine and biology},
  volume={56},
  number={18},
  pages={5949},
  year={2011},
  publisher={IOP Publishing}
}
@article{liu2015reconstruction,
  title={Reconstruction of sparse-view {X}-ray computed tomography using adaptive iterative algorithms},
  author={Liu, Li and Lin, Weikai and Jin, Mingwu},
  journal={Computers in biology and medicine},
  volume={56},
  pages={97--106},
  year={2015},
  publisher={Elsevier}
}

@article{xcatweb,
  title={{4D XCAT} phantom webpage: \url{https://olv.duke.edu/industry-investors/available-technologies/xcat}},
  howpublished = {\url{https://olv.duke.edu/industry-investors/available-technologies/xcat}},
  note = {Accessed: 2017-10-19}
}

@misc{popi-modelweb,
  title = {{POPI} model webpage and data},
  howpublished = {\url{https://www.creatis.insa-lyon.fr/rio/popi-model_original_page}},
  note = {Accessed: 2016-0912-19}
}
@misc{pstweb,
  title = {{CERN} phase space tomography},
  howpublished = {\url{http://cern.ch/tomography}},
  note = {Accessed: 2017-10-19}
}
@article{pst1,
  title={Tomographic measurements of longitudinal phase space density},
  author={Hancock, S and Lindroos, M and McIntosh, E and Metcalf, Mike},
  journal={Computer Physics Communications},
  volume={118},
  number={1},
  pages={61--70},
  year={1999},
  publisher={Elsevier}
}

@article{pst2,
  title={Longitudinal phase space tomography with space charge},
  author={Hancock, S and Lindroos, M and Koscielniak, S},
  journal={Physical Review Special Topics-Accelerators and Beams},
  volume={3},
  number={12},
  pages={124202},
  year={2000},
  publisher={APS}
}
@misc{TIGREweb,
  title = {{TIGRE} {Github} repository: \url{https://github.com/CERN/TIGRE}},
  howpublished = {\url{https://github.com/CERN/TIGRE}},
  note = {Accessed: 2016-0912-19}
}

@article{Pengpan2012246,
title = "Cone Beam {CT} using motion-compensated algebraic reconstruction methods with limited data ",
journal = "Computer Methods and Programs in Biomedicine ",
volume = "105",
number = "3",
pages = "246 - 256",
year = "2012",
note = "",
issn = "0169-2607",
doi = "http://dx.doi.org/10.1016/j.cmpb.2011.09.007",
url = "http://www.sciencedirect.com/science/article/pii/S0169260711002446",
author = "T. Pengpan and W. Qiu and N.D. Smith and M. Soleimani",
keywords = "Motion-compensated CBCT",
keywords = "Limited data reconstruction",
keywords = "ART",
keywords = "SART",
keywords = "OS-SART ",
abstract = "Cone Beam Computed Tomography (CBCT) is widely used in radiation therapy for verifying treatment areas, since it provides three-dimensional image reconstruction of those tumour regions under inspection. However, organ motion is problematic during the scanning process, it causes motion artefacts on the \{CBCT\} image and can lead to mispositioning for the subsequent treatment. Moreover, patient dose is also considerable and there is a need for methods which yield acceptable image quality with as few X-ray images as possible. Although methods have been developed to handle limited projection data, such as the Algebraic Reconstruction Technique (ART); Simultaneous \{ART\} (SART); and Ordered-Subset \{SART\} (OS-SART), this study applied motion compensation to these reconstruction techniques. Root Mean Square Error (RMSE) of image is calculated to study the convergence of reconstructed images compared with the truth image. When motion was applied to a phantom and the motion compensation was used to account for the motion, the results showed that motion compensation improved the quality of \{CBCT\} image, when compared to uncompensated images. Furthermore, the experiments suggested that minimising phase error, for breathing models, was more important than minimising amplitude error. "
}
@article {Rit1,
author = {Rit, Simon and Wolthaus, Jochem W. H. and van Herk, Marcel and Sonke, Jan-Jakob},
title = {On-the-fly motion-compensated cone-beam {CT} using an a priori model of the respiratory motion},
journal = {Medical Physics},
volume = {36},
number = {6},
publisher = {American Association of Physicists in Medicine},
issn = {2473-4209},
url = {http://dx.doi.org/10.1118/1.3115691},
doi = {10.1118/1.3115691},
pages = {2283--2296},
keywords = {Reconstruction, Computed tomography, Artifacts and distortion, Noise, computerised tomography, image denoising, image reconstruction, medical image processing, motion compensation, motion estimation, motion compensation, image guided radiation therapy, cone-beam CT, organ motion, reconstruction, Medical imaging, Cone beam computed tomography, Medical image reconstruction, Computed tomography, Medical image quality, Cancer, Medical image noise, Medical image artifacts, Lungs, Motion estimation},
year = {2009},
}
@ARTICLE{Rit2, 
author={S. Rit and D. Sarrut and L. Desbat}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam {CT} Reconstruction of the Thorax}, 
year={2009}, 
volume={28}, 
number={10}, 
pages={1513-1525}, 
keywords={algebra;computerised tomography;image reconstruction;lung;medical image processing;motion compensation;phantoms;CBCT reconstruction;Feldkamp-Davis-Kress method;computed tomography;cone-beam CT;motion artifacts;motion compensation;phantoms;respiratory motion;simultaneous algebraic reconstruction technique;thorax;Computed tomography;Equations;Image motion analysis;Image reconstruction;Imaging phantoms;Linear accelerators;Motion analysis;Motion estimation;Testing;Thorax;Image reconstruction;X-ray tomography;motion compensation;respiratory system;Algorithms;Computer Simulation;Cone-Beam Computed Tomography;Humans;Image Processing, Computer-Assisted;Phantoms, Imaging;Radiography, Thoracic;Respiratory Mechanics}, 
doi={10.1109/TMI.2008.2008962}, 
ISSN={0278-0062}, 
month={Oct},}

@INPROCEEDINGS{fwdproj, 
author={Fang Xu and K. Mueller}, 
booktitle={3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.}, 
title={A comparative study of popular interpolation and integration methods for use in computed tomography}, 
year={2006}, 
pages={1252-1255}, 
keywords={computer graphics;computerised tomography;image reconstruction;integration;interpolation;medical image processing;CT backprojection;CT projection;Marschner-Lobb dataset;area-based methods;computed tomography;hexagonal subsampling;image reconstruction;integration methods;interpolation methods;line-based methods;parallel-beam projection geometry;programmable commodity graphics hardware;Computed tomography;Computer science;Equations;Filters;Frequency;Interpolation;Kernel;Taylor series;Testing;Visualization}, 
doi={10.1109/ISBI.2006.1625152}, 
ISSN={1945-7928}, 
month={April},}

@article{Sonke2008590,
title = "Variability of Four-Dimensional Computed Tomography Patient Models ",
journal = "International Journal of Radiation Oncology*Biology*Physics ",
volume = "70",
number = "2",
pages = "590 - 598",
year = "2008",
note = "",
issn = "0360-3016",
doi = "http://dx.doi.org/10.1016/j.ijrobp.2007.08.067",
url = "http://www.sciencedirect.com/science/article/pii/S0360301607040746",
author = "Jan-Jakob Sonke and Joos Lebesque and Marcel van Herk",
keywords = "Respiratory motion",
keywords = "Geometric uncertainties",
keywords = "Four-dimensional imaging",
keywords = "Cone-beam CT",
keywords = "Image-guided \{RT\} ",
abstract = "Purpose To quantify the interfractional variability in lung tumor trajectory and mean position during the course of radiation therapy. Methods and Materials Repeat four-dimensional (4D) cone-beam computed tomography (CBCT) scans (median, nine scans/patient) routinely acquired during the course of treatment were analyzed for 56 patients with lung cancer. Tumor motion was assessed by using local rigid registration of a region of interest in the 3D planning \{CT\} to each phase in the 4D CBCT. Displacements of the mean tumor position relative to the planned position (baseline variations) were obtained by using time-weighted averaging of the motion curve. Results The tumor trajectory shape was found to be stable interfractionally, with mean variability not exceeding 1 mm (1 SD) in each direction for the inhale and exhale phases. Interfractional baseline variations, however, were large, with 1.6- (left-right), 3.9- (cranial-caudal), and 2.8-mm (anterior-posterior) systematic variations (1 SD) and 1.2- (left-right), 2.4- (cranial-caudal) and 2.2-mm (anterior-posterior) random variations. Eliminating baseline variations by using soft-tissue guidance decreases planning target volume margins by approximately 50% compared with bony anatomy–driven protocols for conventional fractionation schemes. Conclusions Systematic and random baseline variations constitute a substantial portion of the geometric variability present in the treatment of patients with lung cancer and require generous safety margins when relying on accurate setup/immobilization or bony anatomy–driven correction strategies. The 4D-CBCT has the ability to accurately monitor tumor trajectory shape and baseline variations and drive image-guided correction strategies that allows safe margin reduction. "
}

@article{0031-9155-51-17-003,
  author={J M Blackall and S Ahmad and M E Miquel and J R McClelland and D B Landau and D J Hawkes},
  title={{MRI}-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning},
  journal={Physics in Medicine and Biology},
  volume={51},
  number={17},
  pages={4147},
  url={http://stacks.iop.org/0031-9155/51/i=17/a=003},
  year={2006},
  abstract={Respiratory organ motion has a significant impact on the planning and delivery of radiotherapy (RT) treatment for lung cancer. Currently widespread techniques, such as 4D-computed tomography (4DCT), cannot be used to measure variability of this motion from one cycle to the next. In this paper, we describe the use of fast magnetic resonance imaging (MRI) techniques to investigate the intra- and inter-cycle reproducibility of respiratory motion and also to estimate the level of errors that may be introduced into treatment delivery by using various breath-hold imaging strategies during lung RT planning. A reference model of respiratory motion is formed to enable comparison of different breathing cycles at any arbitrary position in the respiratory cycle. This is constructed by using free-breathing images from the inhale phase of a single breathing cycle, then co-registering the images, and thereby tracking landmarks. This reference model is then compared to alternative models constructed from images acquired during the exhale phase of the same cycle and the inhale phase of a subsequent cycle, to assess intra- and inter-cycle variability ('hysteresis' and 'reproducibility') of organ motion. The reference model is also compared to a series of models formed from breath-hold data at exhale and inhale. Evaluation of these models is carried out on data from ten healthy volunteers and five lung cancer patients. Free-breathing models show good levels of intra- and inter-cycle reproducibility across the tidal breathing range. Mean intra-cycle errors in the position of organ surface landmarks of 1.5(1.4)–3.5(3.3) mm for volunteers and 2.8(1.8)–5.2(5.2) mm for patients. Equivalent measures of inter-cycle variability across this range are 1.7(1.0)–3.9(3.3) mm for volunteers and 2.8(1.8)–3.3(2.2) mm for patients. As expected, models based on breath-hold sequences do not represent normal tidal motion as well as those based on free-breathing data, with mean errors of 4.4(2.2)–7.7(3.9) mm for volunteers and 10.1(6.1)–12.5(6.3) mm for patients. Errors are generally larger still when using a single breath-hold image at either exhale or inhale to represent the lung. This indicates that account should be taken of intra- and inter-cycle respiratory motion variability and that breath-hold-based methods of obtaining data for RT planning may potentially introduce large errors. This approach to analysis of motion and variability has potential to inform decisions about treatment margins and optimize RT planning.}
}
	

  	
@article{0031-9155-58-5-1447,
  author={Hao Yan and Xiaoyu Wang and Wotao Yin and Tinsu Pan and Moiz Ahmad and Xuanqin Mou and Laura Cerviño and Xun Jia and Steve B Jiang},
  title={Extracting respiratory signals from thoracic cone beam {CT} projections},
  journal={Physics in Medicine and Biology},
  volume={58},
  number={5},
  pages={1447},
  url={http://stacks.iop.org/0031-9155/58/i=5/a=1447},
  year={2013},
  abstract={The patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such a signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely the Amsterdam Shroud method, the intensity analysis method and the Fourier-transform-based phase analysis method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting a respiratory signal. We also identified the applicability of each existing method.}
}
	@article{apostol2003cell,
  title={A cell-based assay for aggregation inhibitors as therapeutics of polyglutamine-repeat disease and validation in {D}rosophila},
  author={Apostol, Barbara L and Kazantsev, Alexsey and Raffioni, Simona and Illes, Katalin and Pallos, Judit and Bodai, Laszlo and Slepko, Natalia and Bear, James E and Gertler, Frank B and Hersch, Steven and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={100},
  number={10},
  pages={5950--5955},
  year={2003},
  publisher={National Acad Sciences}
}

@article{LUENGO201743,
title = "{SuRVoS}: Super-Region Volume Segmentation workbench",
journal = "Journal of Structural Biology",
volume = "198",
number = "1",
pages = "43 - 53",
year = "2017",
issn = "1047-8477",
doi = "https://doi.org/10.1016/j.jsb.2017.02.007",
url = "http://www.sciencedirect.com/science/article/pii/S1047847717300308",
author = "Imanol Luengo and Michele C. Darrow and Matthew C. Spink and Ying Sun and Wei Dai and Cynthia Y. He and Wah Chiu and Tony Pridmore and Alun W. Ashton and Elizabeth M.H. Duke and Mark Basham and Andrew P. French",
keywords = "Interactive segmentation, Hierarchical segmentation, Super-Regions, Semi-supervised learning, Cryo soft X-ray tomography, Cryo electron tomography"
}