Image Reconstruction Correction Research Articles

A quantitative CBCT pipeline based on 2D antiscatter grid and grid-based scatter sampling for image-guided radiation therapy.

Quantitative accuracy is critical for expanding the role of cone beam CT (CBCT) imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy. Despite advances in CBCT image quality improvement methods, quantitative accuracy gap between CBCT and multi-detector CT (MDCT) remains. In this work, a physics-driven approach was investigated that combined robust scatter rejection, raw data correction and iterative image reconstruction to further improve CBCT image quality and quantitative accuracy, referred to as quantitative CBCT (qCBCT). QCBCT approach includes tungsten 2D antiscatter grid hardware, residual scatter correction with grid-based scatter sampling, image lag, and beam hardening correction for offset detector geometry linac-mounted CBCT. Images were reconstructed with iterative image reconstruction to reduce image noise. qCBCT was evaluated using a variety of phantoms to investigate the effect of object size and its composition on image quality, and image quality was benchmarked against clinical CBCT and gold standard MDCT images used for treatment planning. QCBCT provided statistically significant improvement in CT number accuracy and reduced image artifacts when compared to clinical CBCT images. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 17±9 and 38±29 HU, respectively. Magnitude of phantom size dependent HU variations were comparable between MDCT and qCBCT images. With iterative reconstruction, contrast-to-noise ratio improved by 25% when compared to clinical CBCT protocols. Combination of novel scatter suppression techniques and other data correction methods in qCBCT provided CT number accuracy comparable to gold standard MDCT used for treatment planning. This approach may potentially improve CBCT's promise in fulfilling the tasks that demand high quantitative accuracy, such as online dose calculations and treatment response assessment, in image guided radiation therapy.

A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks

With the development and innovation of new techniques for 5G, 5G networks can provide extremely large capacity, robust integrity, high bandwidth, and low latency for multimedia image sharing and storage. However, it will surely exacerbate the privacy problems intrinsic to image transformation. Due to the high security and reliability requirements for storing and sharing sensitive images in the 5G network environment, verifiable steganography-based secret image sharing (SIS) is attracting increasing attention. The verifiable capability is necessary to ensure the correct image reconstruction. From the literature, efficient cheating verification, lossless reconstruction, low reconstruct complexity, and high-quality stego images without pixel expansion are summarized as the primary goals of proposing an effective steganography-based SIS scheme. Compared with the traditional underlying techniques for SIS, cellular automata (CA) and matrix projection have more strengths as well as some weaknesses. In this paper, we perform a complimentary of these two techniques to propose a verifiable secret image sharing scheme, where CA is used to enhance the security of the secret image, and matrix projection is used to generate shadows with a smaller size. From the steganography perspective, instead of the traditional least significant bits replacement method, matrix encoding is used in this paper to improve the embedding efficiency and stego image quality. Therefore, we can simultaneously achieve the above goals and achieve proactive and dynamic features based on matrix projection. Such features can make the proposed SIS scheme more applicable to flexible 5G networks. Finally, the security analysis illustrates that our scheme can effectively resist the collusion attack and detect the shadow tampering over the persistent adversary. The analyses for performance and comparative demonstrate that our scheme is a better performer among the recent schemes with the perspective of functionality, visual quality, embedding ratio, and computational efficiency. Therefore, our scheme further strengthens security for the images in 5G networks.

Preliminary exploration of hard X-ray coherent diffraction imaging method at SSRF

Coherent X-ray diffraction imaging (CDI) method is a powerful X-ray imaging technique with high resolution up to nanometer scale. Most of the synchrotron radiation facilities and free electron laser facilities are equipped with this state-of-the-art imaging technique and have made many outstanding achievements in multiple scientific areas. Up to now, although scanning CDI (ptychography) method based on a soft X-ray source has been opened to users, the hard X-ray CDI experimental platform has not been built at Shanghai Synchrotron Radiation Facility (SSRF) which can research some relatively thick specimens and easily extend to three-dimensional imaging. As some new beamlines with undulator source were put into operation recently, it is possible and feasible to build up the CDI experimental platform with hard X-ray. In this article, we report the hard X-ray CDI experimental platform development process and preliminary experimental results of coherent diffraction pattern and image reconstruction at SSRF. Based on the operating BL19U2 biological small-angle X-ray scattering (SAXS) beamline at SSRF, the hard X-ray coherent beam is obtained through effective optical path designation at 12 keV and 13.5 keV. The hard X-ray optimization includes tuning several slits, double crystal monochromator (DCM), horizontal deflection mirror, focusing mirror system and pinhole, etc. Furthermore, hard X-ray CDI experiments are conducted. The spatial coherent length of the incident beam is also measured from the pinhole diffraction pattern. This platform can provide both conventional mode and scanning mode (ptychography) for the coherent diffraction imaging method, and the correct image reconstruction from the experimental diffraction patterns proves that the platform has the experimental capability for hard X-ray CDI. In the conventional forward scattering CDI mode, coherent diffraction patterns of pinhole are collected and used to analyse the coherence property of the optimized X-ray beam. The structure of pinhole is also reconstructed from the diffraction pattern. In the scanning CDI mode, a zone plate is used as a sample. The central area of zone plate is reconstructed correctly. About 90 nm/pixel resolution of reconstruction is achieved which is extremely dependent on the X-ray flux density from the undulator source emission. Hard X-ray CDI experimental platform based on the synchrotron radiation facility is first built in China. It will provide effective software and hardware supporting for the development and application of hard X-ray CDI experiments in China in the future.

Quantitative SPECT/CT-Technique and Clinical Applications.

The continuous development of SPECT over the past 50years has led to improved image quality and increased diagnostic confidence. The most influential developments include the realization of hybrid SPECT/CT devices, as well as the implementation of attenuation correction and iterative image reconstruction techniques. These developments have led to a preference for SPECT/CT devices over SPECT-only systems and to the widespread adoption of the former, strengthening the role of SPECT/CT as the workhorse of Nuclear Medicine imaging. New trends in the ongoing development of SPECT/CT are diverse. For example, whole-body SPECT/CT images, consisting of acquisitions from multiple consecutive bed positions in the manner of PET/CT, are increasingly performed. Additionally, in recent years, some interesting approaches in detector technology have found their way into commercial products. For example, some SPECT cameras dedicated to specific organs employ semiconductor detectors made of cadmium telluride or cadmium zinc telluride, which have been shown to increase the obtainable image quality by offering a higher sensitivity and energy resolution. However, the advent of quantitative SPECT/CT which, like PET, can quantify the amount of tracer in terms of Bq/mL or as a standardized uptake value could be regarded as most important development. It is a major innovation that will lead to increased diagnostic accuracy and confidence, especially in longitudinal studies and in the monitoring of treatment response. The current work comprises two main aspects. At first, physical and technical fundamentals of SPECT image formation are described and necessary prerequisites of quantitative SPECT/CT are reviewed. Additionally, the typically achievable quantitative accuracy based on reports from the literature is given. Second, an extensive list of studies reporting on clinical applications of quantitative SPECT/CT is provided and reviewed.

FPGA-Based Pipelined Architecture for Real-Time Estimation of Sensitivity Maps Using Pre-Scan Method in Parallel MRI

Parallel MRI (pMRI) is a widely used technique for increasing the MRI acquisition speed, by using multiple receiver coils with spatially discriminated sensitivities. Sensitivity encoding (SENSE) is a parallel MRI technique used for reconstructing the aliasing free images from the under-sampled MRI data using the spatial information obtained from multiple receiver coils. Precise receiver coil sensitivity estimation is critical for the correct image reconstruction from the under-sampled data in SENSE. Pre-scan method is a quick and conventionally used method for correct estimation of the receiver coil sensitivities, to be used in SENSE. In this work, an application-specific hardware implemented on FPGA (Field Programmable Gate Array) for real-time sensitivity maps estimation using the pre-scan method is proposed. The proposed architecture has the potential to be installed on the receiver coil data acquisition system, which would provide sensitivity maps for image reconstruction, without moving the raw data to the MRI workstation. Parallelism is utilized in the proposed pipelined architecture to make it even faster. From the experimental results, it is shown that the proposed architecture estimates the sensitivity maps in only 1.466[Formula: see text]ms for eight receiver coils. Furthermore, high mean SNR (30[Formula: see text][Formula: see text]dB), low root-mean-square-error ([Formula: see text]) and low artifact power ([Formula: see text]) are achieved for the under-sampled human head, cardiac and phantom data sets (acceleration factor = 2), reconstructed with SENSE algorithm utilizing the sensitivity maps estimated by the proposed architecture.

Hybrid PET-MRI Applications in Movement Disorders.

Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [18F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [11C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [18F]FDG PET and [11C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.

Measured PET Data Characterization with the Negative Binomial Distribution Model

Accurate statistical model of PET measurements is a prerequisite for a correct image reconstruction when using statistical image reconstruction algorithms, or when pre-filtering operations must be performed. Although radioactive decay follows a Poisson distribution, deviation from Poisson statistics occurs on projection data prior to reconstruction due to physical effects, measurement errors, correction of scatter and random coincidences. Modelling projection data can aid in understanding the statistical nature of the data in order to develop efficient processing methods and to reduce noise. This paper outlines the statistical behaviour of measured emission data evaluating the goodness of fit of the negative binomial (NB) distribution model to PET data for a wide range of emission activity values. An NB distribution model is characterized by the mean of the data and the dispersion parameter α that describes the deviation from Poisson statistics. Monte Carlo simulations were performed to evaluate: (a) the performances of the dispersion parameter α estimator, (b) the goodness of fit of the NB model for a wide range of activity values. We focused on the effect produced by correction for random and scatter events in the projection (sinogram) domain, due to their importance in quantitative analysis of PET data. The analysis developed herein allowed us to assess the accuracy of the NB distribution model to fit corrected sinogram data, and to evaluate the sensitivity of the dispersion parameter α to quantify deviation from Poisson statistics. By the sinogram ROI-based analysis, it was demonstrated that deviation on the measured data from Poisson statistics can be quantitatively characterized by the dispersion parameter α, in any noise conditions and corrections.

Markerless rat head motion tracking using structured light for brain PET imaging of unrestrained awake small animals

Preclinical positron emission tomography (PET) imaging in small animals is generally performed under anesthesia to immobilize the animal during scanning. More recently, for rat brain PET studies, methods to perform scans of unrestrained awake rats are being developed in order to avoid the unwanted effects of anesthesia on the brain response. Here, we investigate the use of a projected structure stereo camera to track the motion of the rat head during the PET scan. The motion information is then used to correct the PET data. The stereo camera calculates a 3D point cloud representation of the scene and the tracking is performed by point cloud matching using the iterative closest point algorithm. The main advantage of the proposed motion tracking is that no intervention, e.g. for marker attachment, is needed. A manually moved microDerenzo phantom experiment and 3 awake rat [18F]FDG experiments were performed to evaluate the proposed tracking method. The tracking accuracy was 0.33 mm rms. After motion correction image reconstruction, the microDerenzo phantom was recovered albeit with some loss of resolution. The reconstructed FWHM of the 2.5 and 3 mm rods increased with 0.94 and 0.51 mm respectively in comparison with the motion-free case. In the rat experiments, the average tracking success rate was 64.7%. The correlation of relative brain regional [18F]FDG uptake between the anesthesia and awake scan reconstructions was increased from on average 0.291 (not significant) before correction to 0.909 (p < 0.0001) after motion correction. Markerless motion tracking using structured light can be successfully used for tracking of the rat head for motion correction in awake rat PET scans.

Spatiotemporal blue noise coded aperture design for multi-shot compressive spectral imaging.

Multi-shot coded aperture snapshot spectral imaging (CASSI) systems capture the spectral information of a scene using a small set of coded focal plane array (FPA) compressive measurements. Compressed sensing (CS) reconstruction algorithms are then used to reconstruct the underlying spectral 3D data cube from an underdetermined system of linear equations. Multiple snapshots result in a less ill-posed inverse problem and improved reconstructions. The only varying components in CASSI are the coded apertures, whose structure is crucial inasmuch as they determine the minimum number of FPA measurements needed for correct image reconstruction and the corresponding attainable quality. Traditionally, the spatial structures of the coded aperture entries are selected at random, leading to suboptimal reconstruction solutions. This work presents an optimal structure design of a set of coded apertures by optimizing the concentration of measure of the multi-shot CASSI sensing matrix and its incoherence with respect to the sparse representation basis. First, the CASSI matrix system representation in terms of the ensemble of random projections is established. Then, the restricted isometry property (RIP) of the CASSI projections is determined as a function of the coded aperture entries. The optimal coded aperture structures are designed under the criterion of satisfying the RIP with high probability, coined spatiotemporal blue noise (BN) coded apertures. Furthermore, an algorithm that implements the BN ensembles is presented. Extensive simulations and a testbed implementation are developed to illustrate the improvements of the BN coded apertures over the traditionally used coded aperture structures, in terms of spectral image reconstruction PSNR and SSIM.

Probability of correct reconstruction in compressive spectral imaging

Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the 3-dimensional (3D) spatio-spectral information of a scene using a set of 2-dimensional (2D) random coded Focal Plane Array (FPA) measurements. A compressed sensing reconstruction algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the statistical structure of the coded apertures. The Restricted Isometry Property (RIP) of the CASSI sensing matrix is used to determine the probability of correct image reconstruction and provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction. Further, the RIP can be used to determine the optimal structure of the coded projections in CASSI. This article describes the CASSI optical architecture and develops the RIP for the sensing matrix in this system. Simulations show the higher quality of spectral image reconstructions when the RIP property is satisfied. Simulations also illustrate the higher performance of the optimal structured projections in CASSI.

Advances in PET/MR instrumentation and image reconstruction.

The combination of positron emission tomography (PET) and MRI has attracted the attention of researchers in the past approximately 20 years in small-animal imaging and more recently in clinical research. The combination of PET/MRI allows researchers to explore clinical and research questions in a wide number of fields, some of which are briefly mentioned here. An important number of groups have developed different concepts to tackle the problems that PET instrumentation poses to the exposition of electromagnetic fields. We have described most of these research developments in preclinical and clinical experiments, including the few commercial scanners available. From the software perspective, an important number of algorithms have been developed to address the attenuation correction issue and to exploit the possibility that MRI provides for motion correction and quantitative image reconstruction, especially parametric modelling of radiopharmaceutical kinetics. In this work, we give an overview of some exemplar applications of simultaneous PET/MRI, together with technological hardware and software developments.

A Novel DFP Correction Image Reconstruction Algorithm for Electrical Capacitance Tomography System

To solve the ’soft-fleld’ nature and the ill-posed problem in electrical capacitance tomography technology, a DFP Correction image reconstruction algorithm for electrical capacitance tomography is presented. On analysis of the basic principles of the ECT system, deduced mathematical model of DFP Correction algorithm to solve the problem of electrical capacitance tomography, and analyzed the convergence of the algorithm by the monotony of the iterative error. The feasibility of using this algorithm for ECT problems is also discussed. It shows that it is easy to meet the convergence condition and error of image reconstruction is small. Experimental results and simulation data indicate that the algorithm can provide high quality images and favorable stabilization compared with LBP, Landweber, CG and Conjugate Gradient algorithms and this new algorithm presents a feasible and efiective way to research on image reconstruction algorithm for electrical capacitance tomography System.

Large object investigation by digital holography with effective spectrum multiplexing under single-exposure approach

We present a method to investigate large object by digital holography with effective spectrum multiplexing under single-exposure approach. This method splits the original reference beam and redirects one of its branches as a second object beam. Through the modified Mach-Zehnder interferometer, the two object beams can illuminate different parts of the large object and create a spectrum multiplexed hologram onto the focal plane array of the charge-coupled device/complementary metal oxide semiconductor camera. After correct spectrum extraction and image reconstruction, the large object can be fully observed within only one single snap-shot. The flexibility and great performance make our method a very attractive and promising technique for large object investigation under common 632.8 nm illumination.

The role of PET quantification in neurological imaging: FDG and amyloid imaging in dementia

Positron emission tomography (PET) with FDG or amyloid tracers is an important diagnostic tool, which also provides imaging biomarkers for patient selection in therapeutic trials in Alzheimer’s disease. PET is also a quantitative technique with the potential to measure disease severity and progression. Limitations in spatial resolution result in partial volume effects that can be minimized by correction algorithms and image reconstruction with resolution recovery. Quantitative local rates of cerebral glucose metabolism can be calculated from FDG uptake. Reaching the highest degree of accuracy still requires blood sampling, while bloodless techniques have improved and can provide useful approximations. Advanced image processing techniques allow semiautomatic standardised assessment of diagnostic metabolic patterns and disease severity. Amyloid imaging provides excellent categorical classification of fibrillary amyloid deposition, but quantitative measures derived from amyloid tracer uptake are less well understood.

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

IntroductionDynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. MethodsA new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. ResultsThe proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [18F]FDG and [15O]H2O PET imaging, successfully generating realistic macro- and micro-parametric maps. ConclusionsThe envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols.

Fourier depth of field

Optical systems used in photography and cinema produce depth-of-field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for defocused areas where the incoming radiance has high variance, since many samples are then required for a noise-free Monte Carlo integration. On the other hand, using many aperture samples is wasteful in focused areas where the integrand varies little. Similarly, image sampling in defocused areas should be adapted to the very smooth appearance variations due to blurring. This article introduces an analysis of focusing and depth-of-field in the frequency domain, allowing a practical characterization of a light field's frequency content both for image and aperture sampling. Based on this analysis we propose an adaptive depth-of-field rendering algorithm which optimizes sampling in two important ways. First, image sampling is based on conservative bandwidth prediction and a splatting reconstruction technique ensures correct image reconstruction. Second, at each pixel the variance in the radiance over the aperture is estimated and used to govern sampling. This technique is easily integrated in any sampling-based renderer, and vastly improves performance.

Aperture collimation correction and maximum-likelihood image reconstruction for near-field coded aperture imaging of single photon emission computerized tomography

Coded aperture (CA) imaging originally developed in X-ray astronomy has not been widely used in nuclear medicine due to the decoding complexity of near-field CA images. In this paper, we present a near-field CA imaging technique and image reconstruction method for high sensitivity and high resolution single photon emission computerized tomography (SPECT). Our approach makes three contributions. First, a correction method for the aperture collimation effect is used to eliminate the near-field artifacts without dual acquisitions of mask and anti-mask images. Second, a maximum-likelihood expectation-maximization (MLEM) deconvolution method is used to restore CA images. Finally, a new MLEM-based algorithm is used to partially reconstruct three-dimensional (3-D) objects from a single projection of CA images. Experiments conducted using a dual-head SPECT system equipped with a parallel-hole collimator and a CA module show a tenfold increase in count sensitivity and significant improvement in image resolution with CA collimation as compared to parallel-hole collimation. Experiments conducted using the same dual-head SPECT system equipped with a pinhole collimator show that when the object is closer to the pinhole collimator the CA image resolution is only slightly inferior to the pinhole collimated image. We found that the MLEM deconvolution method provides an inherent nonnegativity constraint on pixel values and remarkably reduces background activities of CA images. The MLEM reconstruction algorithm for CA images is capable of reconstructing 3-D objects from a single projection and can be potentially extended to full 3-D SPECT reconstruction for CA images.

Correct-image reconstruction in the presence of severe anamorphism by means of digital holography

It is demonstrated for the first time to the authors' knowledge that digital holographic image reconstruction can be applied to correction of image reconstruction in the presence of severe anamorphism and aberrations. It is shown that anamorphism and aberrations can be taken into account and compensated for separately to yield a correct-image reconstruction. Anamorphism and aberrations are introduced by a holographic recording system based on a reflective grating interferometer (RGI). The strong anamorphic behavior of the RGI prevents reconstruction of a correct image unless that behavior is compensated for in the process of numerical holographic reconstruction.

Genetic relaxed iterative Fourier transform algorithm for image reconstruction from Fourier transform magnitude

AbstractThis paper proposes an algorithm for image reconstruction from the Fourier transform magnitude by means of a genetic algorithm. Since the iterative Fourier transform algorithm involves repetitive application of constraints in the image domain and Fourier domain, correct image reconstruction is not necessarily achieved, and incorrect images may be obtained at certain initial settings (stagnation phenomenon). This study offers a solution to this stagnation problem, specifically, the genetic relaxed iterative Fourier transform algorithm. In it, genetic operations (selection, crossover, and mutation) are employed to avoid stagnation. Computer simulations show that the proposed algorithm is free of stagnation: that is, correct image reconstruction is obtained irrespective of the initial settings. In addition, the proposed algorithm proves faster than the conventional iterative Fourier transform algorithm, and the initial settings are easy to choose because the parameters do not depend on the image features. In this study, binary images are considered. © 2001 Scripta Technica, Syst Comp Jpn, 32(5): 55–63, 2001

A Generalized Finite Sized Dipole Model for Radar and Medical Imaging Part Ii: Near Field Formulation for Magnetic Resonance Imaging

This paper presents a magnetic dipole-based formulation of near-electromagnetic field magnetic resonance imaging (MRI) problem and the development of an algorithm to perform correct medical diagnosis and image reconstruction from near-field measurements of radio frequency pulses in MRI. Magnetic resonance imaging uses electromagnetic signals to obtain high-resolution images of the human biological structure. This information is represented by color coded images, where the intensity of the color shows the concentration of the different constituent matter present. The constituent matter, have different chemical and electromagnetic properties and these properties define the nature of the electromagnetic signals radiated from these regions. The radiating regions in the biological structure are modeled by discrete magnetic dipoles and a more complete time-domain signal, encompassing the near electromagnetic fields. Using this new signal model, intensity inhomogeneity artifact in MR images is explained and corrected.