Minimization Reconstruction Research Articles

Moment preserving tomographic image reconstruction model

Shape descriptors provide valuable prior information in many tomographic image reconstruction methods. Such descriptors include, among others, centroid, circularity, orientation, and elongation. Shape descriptor measures are often analytically expressed as a composition of certain geometric moments. Building upon this fact, this paper suggests preserving the values of a specific geometric moment in the reconstruction process, instead of preserving entire descriptors, as it has been suggested so far. Reconstructions from two natural projection directions (vertical and horizontal) are considered with special attention. The provided theoretical analysis demonstrates that preserving the value of a specific geometric moment, provided as prior information for the reconstruction process, simultaneously ensures the preservation of the true measures of all four abovementioned descriptors. Based on this result, a novel regularized energy minimization reconstruction model is proposed. The minimization task of the new model is solved using gradient-based optimization algorithm. Performance evaluation of the proposed method is supported by experimental results obtained through comparisons with other well-known reconstruction methods.

Evaluation of standardized uptake value on 131I-6β-iodomethyl-19-norcholesterol scintigraphy for diagnosis of primary aldosteronism and correspondence with adrenal venous sampling.

Adrenal venous sampling (AVS) is a reliable method for lateralization of adrenal hormone secretion, which is important for discriminating between aldosterone-producing adenoma and bilateral adrenal hyperplasia, both of which cause primary aldosteronism (PA). The aim of this study was to evaluate the diagnostic accuracy of the maximum and mean standardized uptake values (SUVmax and SUVmean, respectively) of 131I-6β-iodomethyl-19-norcholesterol (NP-59) single-photon emission computed tomography (SPECT) for PA and its correspondence with AVS. Adrenal NP-59 scintigraphy was performed in 14 patients with suspected PA, and AVS was also performed in 7 of them. SUVmax and SUVmean of the adrenal lesions on the dominant side and their ratios to the values on the non-dominant side (SUVRmax and SUVRmean, respectively) were calculated on SPECT images using ordered-subset conjugate gradient minimization (OSCGM) and three-dimensional ordered-subset expectation maximization (3D-OSEM) reconstruction algorithms. SUVmax and SUVmean on NP-59 SPECT images were significantly higher for aldosterone-producing adenoma than for bilateral adrenal hyperplasia or non-functioning adenoma and slightly superior to SUVRmax and SUVRmean (P = 0.0475 and P = 0.0447 vs. P = 0.124 and P = 0.132, respectively, with OSCGM). The respective areas under the receiver-operating characteristic curve for SUV and SUVR were 0.933 and 0.725 with OSCGM and 0.844 and 0.750 with 3D-OSEM, while SUVmax and SUVRmax had exactly the same diagnostic accuracy as SUVmean and SUVRmean. SUV and SUVR were associated with the diagnostic features on AVS and consistent with lateralization by AVS in most patients. In this study, SUV on NP-59 SPECT helped in the diagnosis of PA and was consistent with the results of AVS in nearly all cases.

Quantitative analysis of mesoporous structures by electron tomography: A phantom study

Electron tomography (ET) has attracted significant attention for a quantitative analysis of mesoporous materials, especially for complex disordered pore structures, as no priori assumption on the pore shape is needed, which is normally inevitable when using traditional bulk characterization techniques. However, a reliable quantification of such pore structures from ET critically depends on the fidelity of the segmented reconstruction, which can be significantly affected, e.g. by the raw data quality, the limited tilting range, artifacts introduced during alignment and further depends on the reconstruction algorithm. Therefore, we systematically investigated the reconstruction reliability of three main-stream algorithms including simultaneous iterative reconstruction technique (SIRT), total variation minimization (TVM) and discrete algebraic reconstruction technique (DART) for mesoporous materials using different imperfect (realistic) tilt-series based on a set of phantom simulations. We found that DART outperforms the other two methods in reliably revealing small pores and narrow channels, especially when the number of projections is strongly constrained. The accurately segmented reconstruction from DART makes it possible to achieve reliable quantification of the pores structure, which in turn leads to reliable evaluation of effective diffusion coefficients. We discuss the influence of different acquisition and reconstruction parameters on the reconstructed 3D volume and the quantitative analysis of pore features. We aim to provide a practical guideline for optimizing acquisition and reconstruction parameters and how to evaluate the accuracy when describing the mesoporous structure.

Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval.

Learning to hash has been widely applied for image retrieval due to the low storage and high retrieval efficiency. Existing hashing methods assume that the distributions of the retrieval pool (i.e., the data sets being retrieved) and the query data are similar, which, however, cannot truly reflect the real-world condition due to the unconstrained visual cues, such as illumination, pose, background, and so on. Due to the large distribution gap between the retrieval pool and the query set, the performances of traditional hashing methods are seriously degraded. Therefore, we first propose a new efficient but transferable hashing model for unconstrained cross-domain visual retrieval, in which the retrieval pool and the query sample are drawn from different but semantic relevant domains. Specifically, we propose a simple yet effective unsupervised hashing method, domain adaptation preconceived hashing (DAPH), toward learning domain-invariant hashing representation. Three merits of DAPH are observed: 1) to the best of our knowledge, we first propose unconstrained visual retrieval by introducing DA into hashing for learning transferable hashing codes; 2) a domain-invariant feature transformation with marginal discrepancy distance minimization and feature reconstruction constraint is learned, such that the hashing code is not only domain adaptive but content preserved; and 3) a DA preconceived quantization loss is proposed, which further guarantees the discrimination of the learned hashing code for sample retrieval. Extensive experiments on various benchmark data sets verify that our DAPH outperforms many state-of-the-art hashing methods toward unconstrained (unrestricted) instance retrieval in both single- and cross-domain scenarios.

3D Dynamic MRI with Homotopic l0 Minimization Reconstruction

3D Dynamic MRI with Homotopic l0 Minimization Reconstruction

Interplay of Sensor Quantity, Placement and System Dimension in POD-Based Sparse Reconstruction of Fluid Flows

Sparse linear estimation of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale partial differential equations (PDE) dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying low-dimensional space spanning the manifold in which the system resides. In this paper, we adopt an approach that learns basis from singular value decomposition (SVD) of training data to recover sparse information. This results in a set of four design parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of design parameters implicitly determines the choice of algorithm as either l 2 minimization reconstruction or sparsity promoting l 1 minimization reconstruction. In this work, we systematically explore the implications of these design parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement, particularly interpolation points from the discrete empirical interpolation method (DEIM), provide the best balance of computational complexity and accurate reconstruction.

Impact of Novel Incorporation of CT-based Segment Mapping into a Conjugated Gradient Algorithm on Bone SPECT Imaging: Fundamental Characteristics of a Context-specific Reconstruction Method.

Objective(s):The latest single-photon emission computed tomography (SPECT)/computed tomography (CT) reconstruction system, referred to as xSPECT Bone™, is a context-specific reconstruction system utilizing tissue segmentation information from CT data, which is called a zone map. The aim of this study was to evaluate the effects of zone-map enhancement incorporated into the ordered-subset conjugated gradient minimization (OSCGM) reconstruction method on SPECT images.Methods: Image quality with zone-map enhanced OSCGM (OSCGMz) and non-enhanced OSCGM methods was compared using various reconstruction parameters. The compartment phantom had 3 radioactive sections with CT values of about 1000, 250, and 0 HU. SPECT data were acquired using a low-energy high-resolution (LEHR) collimator, with a 256×256 matrix and 2.4-mm pixel size. The performances of the 2 reconstruction methods (OSCGM vs. OSCGMz) were evaluated on the basis of %error, coefficient of variation (%CV), and normalized mean squared error (NMSE), and adequate iterative update numbers were determined. The relative CV representing the ratio of smoothed images to non-smoothed images was calculated to evaluate the effects of the Gaussian filter on each section set with different CT values.Results:On comparing the OSCGM and OSCGMz methods, it was found that the %error of the OSCGMz method tended to show convergence with fewer updates, especially in the high CT value section mimicking bone absorption. In the water section, the %CV of the OSCGMz method was lower than that of the OSCGM method. The NMSE minimum values for the OSCGM and OSCGMz methods were obtained at 30 and 20 updates, respectively. The relative CV for the OSCGMz method in the water section decreased remarkably according to the size of the full width at half maximum (FWHM) of the Gaussian filter.Conclusion:The zone-map enhancement contributed to SPECT reconstruction for the reproduction of radioactive concentrations in bone tissues, using a low number of OSCGM updates. Our findings indicated that the incorporation of zone maps into SPECT reconstruction might improve image quality.

The attenuated spline reconstruction technique for single photon emission computed tomography.

We present the attenuated spline reconstruction technique (aSRT) which provides an innovative algorithm for single photon emission computed tomography (SPECT) image reconstruction. aSRT is based on an analytic formula of the inverse attenuated Radon transform. It involves the computation of the Hilbert transforms of the linear attenuation function and of two sinusoidal functions of the so-called attenuated sinogram These computations are achieved by employing the attenuation information provided by computed tomography (CT) scans and by utilizing custom-made cubic spline interpolation. The purpose of this work is: (i) to present the mathematics of aSRT, (ii) to reconstruct simulated and real SPECT/CT data using aSRT and (iii) to evaluate aSRT by comparing it to filtered backprojection (FBP) and to ordered subsets expectation minimization (OSEM) reconstruction algorithms. Simulation studies were performed by using an image quality phantom and an appropriate attenuation map. Reconstructed images were generated for 45, 90 and 180 views over 360 degrees with 20 realizations and involved Poisson noise of three different levels (NL), namely 100% (NL1), 50% (NL2) and 10% (NL3) of the total counts, respectively. Moreover, real attenuated SPECT sinograms were reconstructed from a real study of a Jaszczak phantom, as well as from a real clinical myocardial SPECT/CT study. Comparisons between aSRT, FBP and OSEM reconstructions were performed using contrast, bias and image roughness. The results suggest that aSRT can efficiently produce accurate attenuation-corrected reconstructions for simulated and real phantoms, as well as for clinical data. In particular, in the case of the clinical myocardial study, aSRT produced reconstructions with higher cold contrast than both FBP and OSEM. aSRT, by incorporating the attenuation correction within itself, may provide an improved alternative to FBP. This is particularly promising for 'cold' regions as those occurring in myocardial ischaemia.

Assessing the Impact of Building Volume on Land Subsidence in the Central Business District of Beijing with SAR Tomography

ABSTRACTHigh-rise buildings in the modern urban setting may have a considerable impact on land subsidence. The process of urbanization in Beijing has been found to be in direct relationship with the land subsidence. The permanent scatterers interferometric aperture radar (PSInSAR) technology is becoming a conventional tool for land subsidence monitoring. Synthetic aperture radar (SAR) tomography, as an extension of PSInSAR technology, allows a full 3D imaging, and is useful to reconstruct 3D urban building maps. In this study, a SAR tomography method “scale-down by L1 norm minimization, model selection, and estimation reconstruction” (SL1MMER) was applied to 33 TerraSAR-X images to retrieve building height. The object-oriented classification method based on the support vector machine was applied on the World View 2 image to extract geometry of building footprints and to aggregate SL1MMER derived building heights for each building. The building footprints and building heights were used to calculate the volume of buildings. The Stanford method for persistent scatterers, a PSInSAR methodology, was used to obtain land subsidence rates. The correlation coefficient between the building volumes and land subsidence showed no positive relationship for buildings that were below a certain volume. However, at local scale, with the increase of the building volumes, the correlation increased. The larger the volume of buildings, the stronger the relationship became. Once the volume of buildings became larger than 3.00 × 105 m3, the impact of building volume on land subsidence remained stable.

A feature refinement approach for statistical interior CT reconstruction

Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)—minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

Fast and exact unidimensional L2–L1 optimization as an accelerator for iterative reconstruction algorithms

This paper studies the use of fast and exact unidimensional L2–L1 minimization as a line search for accelerating iterative reconstruction algorithms. In L2–L1 minimization reconstruction problems, the squared Euclidean, or L2 norm, measures signal-data discrepancy and the L1 norm stands for a sparsity preserving regularization term. Functionals as these arise in important applications such as compressed sensing and deconvolution. Optimal unidimensional L2–L1 minimization has only recently been studied by Li and Osher for denoising problems and by Wen et al. for line search. A fast L2–L1 optimization procedure can be adapted for line search and used in iterative algorithms, improving convergence speed with little increase in computational cost. This paper proposes a new method for exact L2–L1 line search and compares it with the Li and Osher's, Wen et al.'s, as well as with a standard line search algorithm, the method of false position. The use of the proposed line search improves convergence speed of different iterative algorithms for L2–L1 reconstruction such as iterative shrinkage, iteratively reweighted least squares, and nonlinear conjugate gradient. This assertion is validated experimentally in applications to signal reconstruction in compressed sensing and sparse signal deblurring.

Typical reconstruction limits for distributed compressed sensing based on ℓ2,1-norm minimization and Bayesian optimal reconstruction

The distributed compressed sensing framework provides an efficient compression scheme of multichannel signals that are sparse in some domains and highly correlated with one another. In particular, a signal model called the joint sparse model 2 (JSM-2) or multiple measurement vector problem, in which all sparse signals share their support, is important for dealing with practical problems such as magnetic resonance imaging and magnetoencephalography. In this paper, we investigate the typical reconstruction performance of JSM-2 problems for two schemes. One is ℓ2,1-norm minimization reconstruction and the other is Bayesian optimal reconstruction. Employing the replica method, we show that the reconstruction performance of both schemes which exploit the knowledge of the sharing of the signal support overcomes that of their corresponding approaches for the single-channel compressed sensing problem. We also develop a computationally feasible approximate algorithm for performing the Bayes optimal scheme to validate our theoretical estimation. Our replica-based analysis numerically indicates that the spinodal point of the Bayesian reconstruction disappears, which implies that a fundamental reconstruction limit can be achieved by the BP-based approximate algorithm in a practical amount of time when the number of channels is sufficiently large. The results of the numerical experiments of both reconstruction schemes agree excellently with the theoretical evaluation.

Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction.

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

Reduction of the scanning time by total variation minimization reconstruction for X-ray tomography in a SEM.

Total variation minimization is applied to the particular case of X-ray tomography in a scanning electron microscope. To prove the efficiency of this reconstruction method, noise-free and noisy data based on the Shepp & Logan phantom have been simulated. These simulations confirm that Total variation minimization-reconstruction algorithm better manages data containing low number of projections with respect to simultaneous iterative reconstruction technique or filtered backprojection, even in the presence of noise. The algorithm has been applied to real data sets, with a low angular sampling and a high level of noise. Two samples containing micro-interconnections have been analyzed and 3D reconstructions show that Total variation minimization-based algorithm performs well even with 60 projections in order to properly recover a 500 nm diameter void inside a copper interconnection.

心筋SPECT 検査での多焦点ファンビームコリメータにおける画像再構成条件の最適化

The aim of this study was to optimize the reconstruction parameters for ordered subset conjugate gradient minimization (OSCGM) reconstruction using a multifocus fan beam collimator in myocardial perfusion single photon emission computed tomography (SPECT). We attempted to validate the following performance of OSCGM reconstruction parameters (iteration and subset). SPECT images were acquired using a dual-head gamma camera with IQ mode acquisition systems from a RH-2 cardiac phantom containing a 99m-Tc solution. The performance was evaluated using reconstruction parameters (product of subset and iteration: SI) with image contrast, LV volume [using quantitative perfusion SPECT (QPS)], root mean square uncertainty (RMSU), and normalized mean squared error (NMSE). The best results (contrast, uniformity, LV volume, and NMSE) were found for SI: 30. LV volume indicated the true volume for subset: 1 and iteration: 30, and LV volume was underestimated by 10% for iteration >20, and subset >1. The results of this myocardial perfusion SPECT study suggest the optimal OSCGM reconstruction parameter to be subset: 1 and iteration: 30 using a multifocus fan beam collimator.

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: a proof of concept study

Non-coplanar beams are important for treatment of both cranial and noncranial tumors. Treatment verification of such beams with couch rotation/kicks, however, is challenging, particularly for the application of cone beam CT (CBCT). In this situation, only limited and unconventional imaging angles are feasible to avoid collision between the gantry, couch, patient, and on-board imaging system. The purpose of this work is to develop a CBCT verification strategy for patients undergoing non-coplanar radiation therapy. We propose an image reconstruction scheme that integrates a prior image constrained compressed sensing (PICCS) technique with image registration. Planning CT or CBCT acquired at the neutral position is rotated and translated according to the nominal couch rotation/translation to serve as the initial prior image. Here, the nominal couch movement is chosen to have a rotational error of 5° and translational error of 8 mm from the ground truth in one or more axes or directions. The proposed reconstruction scheme alternates between two major steps. First, an image is reconstructed using the PICCS technique implemented with total-variation minimization and simultaneous algebraic reconstruction. Second, the rotational/translational setup errors are corrected and the prior image is updated by applying rigid image registration between the reconstructed image and the previous prior image. The PICCS algorithm and rigid image registration are alternated iteratively until the registration results fall below a predetermined threshold. The proposed reconstruction algorithm is evaluated with an anthropomorphic digital phantom and physical head phantom. The proposed algorithm provides useful volumetric images for patient setup using projections with an angular range as small as 60°. It reduced the translational setup errors from 8 mm to generally <1 mm and the rotational setup errors from 5° to <1°. Compared with the PICCS algorithm alone, the integration of rigid registration significantly improved the reconstructed image quality, with a reduction of mostly 2–3 folds (up to 100) in root mean square image error. The proposed algorithm provides a remedy for solving the problem of non-coplanar CBCT reconstruction from limited angle of projections by combining the PICCS technique and rigid image registration in an iterative framework. In this proof of concept study, non-coplanar beams with couch rotations of 45° can be effectively verified with the CBCT technique.

Advanced reconstruction algorithms for electron tomography: From comparison to combination

In this work, the simultaneous iterative reconstruction technique (SIRT), the total variation minimization (TVM) reconstruction technique and the discrete algebraic reconstruction technique (DART) for electron tomography are compared and the advantages and disadvantages are discussed. Furthermore, we describe how the result of a three dimensional (3D) reconstruction based on TVM can provide objective information that is needed as the input for a DART reconstruction. This approach results in a tomographic reconstruction of which the segmentation is carried out in an objective manner.

Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment

Tomographic synthetic aperture radar (SAR) inversion, including SAR tomography and differential SAR tomography, is essentially a spectral analysis problem. The resolution in the elevation direction depends on the elevation aperture size, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, such as TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution (SR) reconstruction algorithms are desired. Considering the sparsity of the signal in elevation, a compressive sensing based super-resolving algorithm, named “Scale-down by L1 norm Minimization, Model selection, and Estimation Reconstruction” (SL1MMER, pronounced “slimmer”), was proposed by the authors in a previous paper. The ultimate bounds of the technique on localization accuracy and SR power were investigated. In this paper, the essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis. It is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances. Furthermore, the SR capability of SL1MMER is demonstrated using TerraSAR-X real data examples. For a high rise building complex, the percentage of detected double scatterers is almost doubled compared to classical linear estimators. Among them, half of the detected double scatterer pairs have elevation distances smaller than the Rayleigh elevation resolution. This confirms the importance of SR for this type of applications.

Sci-Thur PM: YIS - 05: Tomographic dosimetry using scintillating fibers and its application to 2D and 3D dosimetry.

To present the proof of concept and the experimental validation of tomographic dosimetry (tomodosimetry), where a tomographic acquisition of the incident deposited dose is performed using long scintillating fibers. 2D tomodosimetry: 50 long scintillating fibers were aligned on a 20cm diameter disk inside a 30cm diameter masonite phantom. 3D tomodosimetry: 128 long scintillating fibers of various orientation were simulated on the surface of two cylindrical regions of radius 7.5 and 3.75cm inside a 20cm diameter, 20cm long cylindrical phantom. In both case, the dose projections were acquire each 5 degrees over a 180 degrees (2D) or 360 degrees (3D) rotation of the device, and the dose in each scintillating fiber plane was reconstructed using a total variation minimization reconstruction iterative algorithm at a resolution of 1×1mm2 . The 3D dose was obtained by interpolating between in each cylindrical plane in the 3D prototype. 3%/3mm gamma tests conducted in the isocentre plane for both configurations achieved a success rate of more than 99% of the dose pixels in the region over 50% of the maximum dose. Absolute dose differences in the high dose low gradient region of each scintillating fiber plane were on average below 1% for the 2D configuration and below 1.3% for the 3D configuration. This work illustrates the potential and capacity of scintillating fiber based 2D and 3D tomodosimeters. The presented methodology allows for millimeter resolution dosimetry in a whole 2D plane or 3D volumes in real-time using only a limited number of detectors.