 Original Article
 Open Access
 Published:
Noniterative image reconstruction from sparse magnetic resonance imaging radial data without priors
Visual Computing for Industry, Biomedicine, and Art volumeÂ 3, ArticleÂ number:Â 9 (2020)
Abstract
The stateoftheart approaches for image reconstruction using undersampled kspace data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a noniterative algorithm to estimate the unmeasured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the stateoftheart iterative compressedsensing image reconstruction method using the totalvariation optimization norm.
Introduction
This paper considers image reconstruction for undersampled magnetic resonance imaging (MRI) data, which is a typical case for fast imaging such as dynamic imaging and realtime imaging [1, 2]. Since the data is incomplete, direct image reconstruction contains severe artifacts. The stateoftheart approaches are compressed sensing based iterative reconstruction methods. The iterative methods optimize an objective function that contains spatial and/or temporal constraints. Some standard compressed sensing papers suggest objective functions with an L1 norm to encourage sparseness [3,4,5,6,7,8]. The compressed sensing approaches can be considered as Bayesian methods, in which the prior information is formulated as the constraints. It is a popular approach that the nonCartesian kspace measurements are interpolated into the Cartesian grid before reconstruction [9,10,11].
Recently machine learning is becoming a popular solution for applications in almost all areas. An important application of machine learning is image reconstruction with limited data [12,13,14,15]. On the surface, machine learning methods do not need any prior information about the image except for a large training set. In fact, the training data set is the prior information, and machine learning methods can also be considered as Bayesian methods.
One drawback of Bayesian methods is that if the object being imaged is quite different from the Bayesian assumptions, the reconstructed image from the Bayesian methods may not be trustworthy. The method proposed in this paper does not assume any prior information. Our method is noniterative and efficient to implement.
Regridding data points may introduce errors to the image. Due to the nature of the filtered backprojection (FBP), our proposed method assumes radial sampling in the kspace, and the measurements do not get interpolated into the Cartesian grid.
Parallel MRI uses multiple receiver coils. The spacedependent properties of receiver coils can be employed to reduce undersampling induced artifacts [16,17,18]. This paper considers only singlechannel MRI. Parallel MRI is beyond the scope of this paper.
Methods
Linear interpolation causes rotated shadow images
In this paper, we only consider radial kspace sampling. Undersampled kspace here implies that the number of views is not sufficient. In other words, the angular sampling is sparse. Typically streaking aliasing artifacts will appear in the reconstructed images if the angular sampling is not fine enough. It is noticed that the simple linear interpolation method to estimate the unmeasured measurements has never been used in undersampled MRI applications, and in the first section of this paper, we investigate the reasons why the naÃ¯ve linear interpolation approach does not work well.
Here we use a simple example in the spatial domain to illustrate our point. Let us refer to the onedimensional (1D) inverse Fourier transform in the radial direction of the kspace measurements as the sinogram. Let the sinogram be p(n, m), where n is the index along the radial direction and m is the view angle index. When m is odd, p(n, m) is assumed to be measured. When m is even, p(n, m) is not measured and needs to be estimated. A simple linear interpolation scheme to estimate p(n, 2â€‰m) from p(n, 2â€‰m  1) and p(n, 2â€‰mâ€‰+â€‰1) is
The ultimate effect of this interpolation scheme after image reconstruction is exaggeratingly illustrated as an outline drawing in Fig. 1, where the undersampling streaking artifacts are not shown. Figure 1a shows the main image reconstructed from the original sinogram, while Fig. 1b shows the image reconstructed from the linearly interpolated sinogram using Formula (1). It is interesting to observe from Fig. 1b that the reconstructed image from the linearly interpolated sinogram is a combination of three components: the main reconstruction using the original undersampled sinogram (with a weighting factor of 1), a rotated version of the main reconstruction by Î”Î³ (with a weighting factor of 0.5), and a rotated version of the main reconstruction by Î”Î³ (with a weighting factor of 0.5). Here 2Î”Î³ is the angular gap between two adjacent views in the original undersampled sinogram. In general, sinogram interpolation via linear convolution yields an image that is a combination of the main reconstruction and some rotated versions of the main reconstruction. Similar phenomena are expected for other convolution based sinogram estimation methods. The rotational artifacts are severer at locations farther away from the center of rotation.
Estimation of unmeasured data via displacement function interpolation
We believe that in order to significantly improve the sinogram estimation, we must use some sort of nonlinearity. The strategy of nonrigid deformation can be modified for our sinogram estimation task. There are many image deformation methods [19,20,21]. However, these methods cannot be directly applied to our sinogram estimation. One nonlinear deformation approach is sinewave fitting, which requires singular value decomposition and is rather complicated to implement [22].
The main idea of our algorithm is sketched below. A pair of measured sinogram views is provided: p(n, m_{1}) and p(n, m_{2}), where n is the index along the radial direction and m_{1} and m_{2} are two angular indices. The goal is to estimate p(n, m) with m between m_{1} and m_{2}.
The first step of our proposed method is to find a displacement function u(n) to connect p(n, m_{1}) and p(n, m_{2}) so that
We can find the function \( {u}_{m_1}^{m_2}(n) \) by minimizing an objective function F, for given m_{1}, m_{2}, and n,
with
and
where â„¤ is the set of all integers and N is a preselected small positive integer. For example, Nâ€‰=â€‰12. Here, Î» is a preset parameter to balance the weighting between constraints in the objective function F. We set Î»â€‰=â€‰0.001 in our implementation of Formula (3). In Formula (3), R is a regularization function. If we prefer that both sides of Formula (2) have the similar trends of slopes (i.e., upward trends or downward trends), the regularization function R can be defined as
The objective function of the optimization problem is given in Formula (4), which contains two terms. The first term enforces the function displacement, which is defined in Formula (2) and can be understood as follows. We have two functions: one is labeled as m_{1} and the other is labeled by m_{2}. We assume that the second function is the result of deformation from the first function. For any function value in the second function, we can find a corresponding function value in the first function. However, their associated variables differ by u(n). The second term in the objective function Formula (3) is the constraint term. The constraint is defined in Formula (4), which enforces that the slopes at the corresponding points of the two function have the same sign. In other words, if the second function at one point is increasing (or decreasing), then at the corresponding point of the first function is also increasing (or decreasing).
Normally, an objective function such as F in Formula (3) is minimized by an iterative gradient decent algorithm. However, if we restrict u(n) to be integers in [âˆ’N, N] with N being a preset positive integer, it is faster to evaluate the objection F with all possible u(n) values in [âˆ’N, N] and use a â€˜minâ€™ function to determine the optimal displacement function u(n). Here, â€˜minâ€™ is a builtin function in MatlabÂ® to find the minimum value in an array.
The motivation of using a limited range [âˆ’N, N] is to convert an iterative optimization procedure into a onestep procedure. The selection of the integer N is empirical. The computation complexity is directly proportional to 2â€‰Nâ€‰+â€‰1. A small N is desirable from computation cost point of view. However, if N is too small, the true displacement value may be outside the range of [âˆ’N, N]. The value of N can be selected according to the data missing gap. If N were chosen to be 1000, the computation complexity is approximate that of an iterative algorithm with 2001 iterations. In order to obtain an efficient algorithm, the value of N must be small enough. The selection of Nâ€‰=â€‰12 is empirical and data dependent. For different applications or different data sets this value may vary.
After the displacement function u(n) is found, in the second step, the unmeasured sinogram p(n, m) with index m between m_{1} and m_{2} can be readily obtained by linearly interpolating the displacement function u(n). For example, if m_{2} â€“ m_{1}â€‰=â€‰Mâ€‰+â€‰1, we can estimate M views between m_{1} and m_{2} as
for
We must point out in Formula (5) that nâ€‰+â€‰u(n)â€‰Ã—â€‰(mâ€‰âˆ’â€‰m_{1})/M is most likely not an integer. Let
and
where âŒŠxâŒ‹ is the largest integer that is not greater than x. Then Formula (5) can be implemented as the linear interpolation between two neighboring points as
for
Image reconstruction
The kspace data is complex in nature. Our proposed sinogram estimation method described in Section "Estimation of unmeasured data via displacement function interpolation" was developed for real functions. The 1D inverse Fourier transform for the radial kspace measurements is first performed viewbyview. The result is the spatialdomain sinogram. This spatialdomain sinogram has a real part and an imaginary part. The sinogram extension method described in Section "Estimation of unmeasured data via displacement function interpolation" is applied to the magnitude of the sinogram. The image reconstruction algorithm is chosen as the FBP algorithm. This FBP algorithm in MatlabÂ® is a builtin function â€˜iradonâ€™.
In computer simulations, we demonstrate our method with a realvalued (magnitude) sinogram. The original undersampled sinogram was generated analytically without noise. We performed three computer simulation studies and one patient study. In the first computer simulation study, the original measured number of views was 60 over 360Â°. After sinogram extension, the number of views was increased to 180 over 360Â°. In the second computer simulation study, the original measured number of views was 120 over 360Â°. After sinogram extension, the number of views was increased to 360 over 360Â°. The absolute error image between the estimated sinogram and the true sinogram was calculated and reported in the next section.
For the patient cardiac perfusion MRI study, a Siemens 3â€‰T Trio scanner was used for data acquisition [23]. We used a phased array of coils, one of which was chosen to demonstrate the proposed method. The scanner parameters for the radial acquisition were TR = 2.6â€‰ms, TE = 1.1â€‰ms, flip angle = 12Â°, Gd dose = 0.03â€‰mmol/kg, and slice thickness = 6â€‰mm. Reconstruction pixel size was 1.8â€‰Ã—â€‰1.8â€‰mm^{2}. Each image was acquired in a 62â€‰ms readout. The acquisition matrix size for an image frame was 256â€‰Ã—â€‰72, and 75 sequential images were obtained at 75 different times. At each time frame, the kspace is sampled with 72 uniformly spaced radial lines over an angular range of 180Â°.
To illustrate our proposed algorithm, at each time frame we undersampled the 72 views into 24 views for image reconstruction. The images with 72 views were treated as the gold standard. Only one timeframe was used at a time.
For the patient study, the stateoftheart iterative total variation (TV) algorithm was also used for image reconstruction with undersampled MRI data. The number of iterations was 1000. The reconstructions were compared with the gold standard images obtained with 72 views in terms of root mean square error (RMSE).
Results
Computer simulations
Figure 2 shows the results from the computer simulations with the FBP reconstruction algorithm. In this figure, measurements from 180 views over 360Â° are considered as a full sinogram, and measurements from 60 views over 360Â° are considered as an undersampled sinogram. Figure 2a and b show the FBP reconstruction results from the full and undersampled sinograms, respectively. Figure 2c and d show the results with linear convolution sinogram interpolation methods: linear interpolation and sinc function (convolution) interpolation. The linear interpolation method is equivalent to the triangle function (convolution) interpolation method. Figure 2e shows the result of the proposed nonlinear method.
The estimated sinograms and the true sinogram are compared in terms of the absolute value of the difference in Fig. 3 for the estimation methods used in Fig. 2. A summary of the absolute errors is listed in Table 1.
Patient study
A deidentified MRI data set was used for a comparison study. Both stateoftheart iterative TV algorithm and proposed noniterative algorithm were used to reconstruct the images. The results of the patient cardiac perfusion MRI study are shown in figures radial data. Figures 4 and 5â€™s second columns show the FBP reconstruction using the undersampled 24view data. Figures 4 and 5â€™s third columns show iterative TV reconstruction using the undersampled 24view data. Figures 4 and 5â€™s forth columns show the FBP reconstruction using the extended data by the proposed displacement function method from the 24view data. The values of u(n) was restricted to be integers in the range of [âˆ’â€‰12, 12].
The stateoftheart iterative TV algorithm provides the least noisy images. However, according to the RMSE analysis shown in Table 2, the proposed noniterative method has the smallest error compared to the gold standard, which uses 72 views.
Discussion and conclusions
This paper observes that linear convolution based sinogram interpolation method may produce rotation artifacts. To overcome this problem, we propose a nonlinear method that relates the adjacent measurements by a displacement function, and the linear interpolation of the displacement function yields the estimation of unmeasured data.
In this proposed method, two adjacent measured views in the original undersampled sinogram are used for missing data estimation. A displacement function, u(n), is estimated by minimizing an objective function, F. We restrict the values of the displacement function u(n) to be integers in a small range [âˆ’N, N], say, Nâ€‰=â€‰12. The minimization procedure can be noniterative. We use the â€˜minâ€™ function to find the optimal solution for each index n. Then linear interpolation of u(n) is performed for each unmeasured view between the two adjacent measured views. For example, if the unmeasured view is exactly at the middle between the two measured views, the interpolated displacement function for this unmeasured view is 0.5â€‰Ã—â€‰u(n). Most likely 0.5â€‰Ã—â€‰u(n) is not an integer. Linear interpolation is required to form the estimated sinogram p(nâ€‰+â€‰0.5u(n), m). Finally, the image is reconstructed by the FBP algorithm, in which the kspace regridding is not required.
One advantage of the proposed method is that the resultant FBP reconstruction using the estimated sinogram does not have the rotation artifacts. Our estimated sinogram is more accurate than the sinogram estimated by linearconvolutionbased methods. This point is demonstrated by the absolute errors as shown in Table 1. Thanks to the FBP algorithm, the proposed method does not suffer from the kspace regridding errors.
In our patient study, there are 24 views over 180Â°. This number of views is extremely small, much smaller than the recommended view numbers in clinical applications. The most significant feature of our algorithm is that no prior information is ever assumed in the proposed method. Our proposed algorithm is compared against the stateoftheart iterative TV algorithm using the patient dynamic MRI data set. The iterative TV algorithm provides lessnoisy images. However, the proposed noniterative algorithm produces the images that have less RMSE errors when compared with the 72view gold standard images. The unique characteristic of the proposed algorithm is its noniterative nature and efficient computation.
The main motivation for us to use the displacement function method is that the displacement function method is nonlinear, because we observe that linear methods cause rotational artifacts in the image. Other nonlinear methods may also work to estimate the missing data.
Availability of data and materials
Not applicable.
Abbreviations
 1D:

One dimensional
 FBP:

Filtered backprojection
 MRI:

Magnetic resonance imaging
 RMSE:

Root mean square error
 TV:

Total variation
References
Chu CY, Liu GS, Janowski M, Bulte JWM, Li S, Pearl M et al (2018) Realtime MRI guidance for reproducible hyperosmolar opening of the bloodbrain barrier in mice. Front Neurol 9:921. https://doi.org/10.3389/fneur.2018.00921
Mukherjee RK, Chubb H, Roujol S, Razavi R, O'Neill MD (2019) Advances in realtime MRIguided electrophysiology. Curr Cardiovasc Imaging Rep 12(2):6. https://doi.org/10.1007/s1241001994819
Donoho DL (2006) For most large underdetermined systems of linear equations the minimal 1norm solution is also the sparsest solution. Commun Pure Appl Math 59(6):797â€“829. https://doi.org/10.1002/cpa.20132
Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289â€“1306. https://doi.org/10.1109/TIT.2006.871582
CandÃ¨s EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489â€“509. https://doi.org/10.1109/TIT.2005.862083
Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182â€“1195. https://doi.org/10.1002/mrm.21391
Chen SW, Chao SC (2014) A reweighted â„“1minimization based compressed sensing for the spectral estimation of heart rate variability using the unevenly sampled data. PLoS One 9(6):e99098. https://doi.org/10.1371/journal.pone.0099098
Weizman L, Eldar YC, Bashat DB (2015) Compressed sensing for longitudinal MRI: an adaptiveweighted approach. Med Phys 42:5195â€“5208. https://doi.org/10.1118/1.4928148
Lingala SG, Toutios A, Toger J, Lim Y, Zhu YH, Kim YC et al (2016) Stateoftheart MRI protocol for comprehensive assessment of vocal tract structure and function, Proceedings of the annual conference of INTERSPEECH, ISCA, San Francisco, 812 September 2016, pp 475â€“479. https://doi.org/10.21437/Interspeech.2016559
Kholmovski EG, Coulombe N, Silvernagel J, Angel N, Parker D, Macleod R et al (2016) Realtime MRIguided cardiac cryoablation: a feasibility study. J Cardiovasc Electrophysiol 27(5):602â€“608. https://doi.org/10.1111/jce.12950
Tian Y, Mendes J, Pedgaonkar A, Ibrahim M, Jensen L, Schroeder JD et al (2019) Feasibility of multipleview myocardial perfusion MRI using radial simultaneous multislice acquisitions. PLoS One 14(2):e0211738. https://doi.org/10.1371/journal.pone.0211738
Gibbons EK, Hodgson KK, Chaudhari AS, Richards LG, Majersik JJ, Adluru G et al (2018) Simultaneous NODDI and GFA parameter map generation from subsampled qspace imaging using deep learning. Magn Reson Med 81(4):2399â€“2411. https://doi.org/10.1002/mrm.27568
Hyun CM, Kim HP, Lee SM, Lee S, Seo JK (2018) Deep learning for undersampled MRI reconstruction. Phys Med Biol 63(13):135007. https://doi.org/10.1088/13616560/aac71a
Jeelani H, Martin J, Vasquez F, Salerno M, Weller DS (2018) Image quality affects deep learning reconstruction of MRI, Proceedings of the IEEE 15th international symposium on biomedical imaging, IEEE, Washington, 24 May 2018, pp 357â€“360. https://doi.org/10.1109/ISBI.2018.8363592
Yang G, Yu SM, Dong H, Slabaugh G, Dragotti PL, Ye XJ et al (2018) DAGAN: deep dealiasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans Med Imaging 37(6):1310â€“1321. https://doi.org/10.1109/TMI.2017.2785879
Sodickson DK, Manning WJ (1997) Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 38(4):591â€“603. https://doi.org/10.1002/mrm.1910380414
Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42(5):952â€“962. https://doi.org/10.1002/(SICI)15222594(199911)42:5<952::AIDMRM16>3.0.CO;2S
Larkman DJ, Nunes RG (2007) Parallel magnetic resonance imaging. Phys Med Biol 52(7):R15â€“R55. https://doi.org/10.1088/00319155/52/7/R01
Rao A, Chandrashekara R, SanchezOrtiz GI, Mohiaddin R, Aljabar P, Hajnal JV et al (2004) Spatial transformation of motion and deformation fields using nonrigid registration. IEEE Trans Med Imaging 23(9):1065â€“1076. https://doi.org/10.1109/TMI.2004.828681
Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwellâ€™s demons. Med Image Anal 2(3):243â€“260. https://doi.org/10.1016/S13618415(98)800224
Wang H, Dong L, O'Daniel J, Mohan R, Garden AS, Ang KK et al (2005) Validation of an accelerated â€˜demonsâ€™ algorithm for deformable image registration in radiation therapy. Phys Med Biol 50(12):2887â€“2905. https://doi.org/10.1088/00319155/50/12/011
Kalke M, Siltanen S (2014) Sinogram interpolation method for sparseangle tomography. Appl Math 5(3):423â€“441. https://doi.org/10.4236/am.2014.53043
Adluru G, McGann C, Speier P, Kholmovski EG, Shaaban A, DiBella EVR (2009) Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging. J Magn Reson Imaging 29(2):466â€“473. https://doi.org/10.1002/jmri.21585
Acknowledgements
Not applicable.
Funding
This research is partially supported by American Heart Association, No. 18AJML34280074.
Author information
Authors and Affiliations
Contributions
All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisherâ€™s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Zeng, G.L., DiBella, E.V. Noniterative image reconstruction from sparse magnetic resonance imaging radial data without priors. Vis. Comput. Ind. Biomed. Art 3, 9 (2020). https://doi.org/10.1186/s4249202000044y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s4249202000044y
Keywords
 Tomographic image reconstruction
 Undersampled measurements
 Fast magnetic resonance imaging
 Analytics reconstruction