Non-iterative methods incorporating a priori source distribution and data information for suppression of image noise and artefacts in 3D SPECT

Non-iterative methods have been developed for image reconstruction in 3D SPECT with uniform attenuation and distance-dependent spatial resolution. It was observed that these methods can, in general, be susceptible to data noise and other errors, yielding conspicuous image artefacts. In this work, we developed and evaluated a regularized inverse-filtering approach for effective suppression of noise and artefacts in 3D SPECT images without significantly compromising image resolution. The proposed approach allows the incorporation of a priori random image field and data information and can thus robustly control the degree of suppression of noise and artefacts in 3D SPECT images. Using computer simulations, we evaluated and compared quantitatively images reconstructed from data sets of various noise levels by the use of the proposed methods and the existing non-iterative methods. These numerical results clearly demonstrated that the proposed regularized inverse-filtering approach can effectively suppress image noise and artefacts that plague the existing non-iterative methods, thus yielding quantitatively more accurate 3D SPECT images. The proposed regularized inverse-filtering approach can also be generalized to other imaging modalities.     

Diameter measurement from images of fluorescent cylinders embedded in tissue

Absorption and scattering of light by tissue as well as limitations in the resolution of the optical system influence the appearance of tissue embedded objects in fluorescence images and may reduce the accuracy of measurements from these images. Although the principles of light scattering in tissue and optical resolution are well known, the interplay between the two in fluorescence imaging in an imaging cryomicrotome is not well understood. In this paper we present and investigate an image formation model in a reflection geometry, like an imaging cryomicrotome, that takes both light scattering by tissue as well as the point spread function of the lens into account. The validity of the model was investigated by comparison of diameter estimates of fluorescent cylinders as obtained from images acquired in a reflection geometry with those estimated from simulated images. The results reveal that diameter values estimated from the simulated images are in excellent agreement with the experimental estimates. Our approach in modeling the image formation process of embedded fluorescent structures allows for the prediction of accuracy of quantitative estimates from fluorescence images. The relationship between imaging parameters and bias can be applied to arrive at accurate diameter estimates of near cylindrical structures like blood vessels.     

Joint model of motion and anatomy for PET image reconstruction

Anatomy-based positron emission tomography (PET) image enhancement techniques have been shown to have the potential for improving PET image quality. However, these techniques assume an accurate alignment between the anatomical and the functional images, which is not always valid when imaging the chest due to respiratory motion. In this article, we present a joint model of both motion and anatomical information by integrating a motion-incorporated PET imaging system model with an anatomy-based maximum a posteriori image reconstruction algorithm. The mismatched anatomical information due to motion can thus be effectively utilized through this joint model. A computer simulation and a phantom study were conducted to assess the efficacy of the joint model, whereby motion and anatomical information were either modeled separately or combined. The reconstructed images in each case were compared to corresponding reference images obtained using a quadratic image prior based maximum a posteriori reconstruction algorithm for quantitative accuracy. Results of these studies indicated that while modeling anatomical information or motion alone improved the PET image quantitation accuracy, a larger improvement in accuracy was achieved when using the joint model. In the computer simulation study and using similar image noise levels, the improvement in quantitation accuracy compared to the reference images was 5.3% and 19.8% when using anatomical or motion information alone, respectively, and 35.5% when using the joint model. In the phantom study, these results were 5.6%, 5.8%, and 19.8%, respectively. These results suggest that motion compensation is important in order to effectively utilize anatomical information in chest imaging using PET. The joint motion-anatomy model presented in this paper provides a promising solution to this problem.     

基于Contourlet变换的CT和锥形束CT图像配准算法

目的提出一种基于Contourlet变换,用于放射治疗定位的CT与锥形束CT（cone beam CT,CBCT）图像配准的方法。方法利用Contourlet变换多尺度多方向的分辨特性,将待配准图像进行Contourlet变换分解,分解后的高频方向子带合成梯度图像,采用归一化互信息作为相似性测度,把梯度图像与低频方向子带以加权函数结合,进行临床医学图像的刚性配准,有效弥补了互信息配准中缺少空间信息的不足。结果通过已知空间变换参数图像的配准结果验证了算法的准确性。配准后lO幅图像变换参数的误差极小,且均方根误差接近于0。结论该图像配准算法精确度高,并具有很好的鲁棒性,有助于提高图像引导放射治疗（image guid edradiation therapy,IGRT）中解剖组织结构和靶区的定位精度。     

Ultrasound-guided microwave imaging of breast cancer: tissue phantom and pilot clinical experiments

Microwave imaging promises high contrast between tumor and normal breast tissues, but its spatial resolution is limited. Here, we present a multimodality approach for high-resolution microwave imaging, where microwave image reconstruction is structurally guided by ultrasound imaging. The combined imaging concept is demonstrated using tissue phantom measurements obtained from a 16 x 15 transmitter/receiver microwave imaging system and a modified B-mode ultrasound system. With the geometry of the target and background known a priori from ultrasound, successful dielectric property images are recovered using a finite element-based reconstruction algorithm. We show that a target as small as 1.2 mm in diameter can be imaged with the multimodality approach, whereas it is impossible to detect such a small-size object using microwave imaging alone. The pilot clinical studies on two cases suggest that breast tumors can be much more accurately detected by the multimodality method.     

Effect of optical property estimation accuracy on tomographic bioluminescence imaging: simulation of a combined optical-PET (OPET) system

Inevitable discrepancies between the mouse tissue optical properties assumed by an experimenter and the actual physiological values may affect the tomographic localization of bioluminescent sources. In a previous work, the simplifying assumption of optically homogeneous tissues led to inaccurate localization of deep sources. Improved results may be obtained if a mouse anatomical map is provided by a high-resolution imaging modality and optical properties are assigned to segmented tissues. In this work, the feasibility of this approach was explored by simulating the effect of different magnitude optical property errors on the image formation process of a combined optical-PET system. Some comparisons were made with corresponding simulations using higher spatial resolution data that are typically attainable by CCD cameras. In addition, simulation results provided insights on some of the experimental conditions that could lead to poor localization of bioluminescent sources. They also provided a rough guide on how accurately tissue optical properties need to be known in order to achieve correct localization of point sources with increasing tissue depth under low background noise conditions.     

A quality assurance phantom for the performance evaluation of volumetric micro-CT systems

Small-animal imaging has recently become an area of increased interest because more human diseases can be modeled in transgenic and knockout rodents. As a result, micro-computed tomography (micro-CT) systems are becoming more common in research laboratories, due to their ability to achieve spatial resolution as high as 10 microm, giving highly detailed anatomical information. Most recently, a volumetric cone-beam micro-CT system using a flat-panel detector (eXplore Ultra, GE Healthcare, London, ON) has been developed that combines the high resolution of micro-CT and the fast scanning speed of clinical CT, so that dynamic perfusion imaging can be performed in mice and rats, providing functional physiological information in addition to anatomical information. This and other commercially available micro-CT systems all promise to deliver precise and accurate high-resolution measurements in small animals. However, no comprehensive quality assurance phantom has been developed to evaluate the performance of these micro-CT systems on a routine basis. We have designed and fabricated a single comprehensive device for the purpose of performance evaluation of micro-CT systems. This quality assurance phantom was applied to assess multiple image-quality parameters of a current flat-panel cone-beam micro-CT system accurately and quantitatively, in terms of spatial resolution, geometric accuracy, CT number accuracy, linearity, noise and image uniformity. Our investigations show that 3D images can be obtained with a limiting spatial resolution of 2.5 mm(-1) and noise of +/-35 HU, using an acquisition interval of 8 s at an entrance dose of 6.4 cGy.     

Software-based respiratory gating for small animal conebeam CT

In volumetric CT imaging of small animals, the breathing motion of the lungs during the image acquisition process results in inconsistent projection data being acquired. When reconstructed, these inconsistent data may produce images with reduced spatial resolution and image contrast. In order to minimize these effects, various approaches have been utilized to capture the respiratory signal of the animals under study and to only obtain CT data at specific moments in the respiratory cycle. These approaches typically utilize hardware-based physiological monitoring equipment in order to record the respiratory signal and either prospectively or retrospectively correlate this signal with acquired CT projection data. In this work, a new method of CT respiratory gating is described that does not rely on external physiological monitoring. Rather, determination of the respiratory phase of the animal is performed by postprocessing the acquired projection data. With this approach, any CT projection data can be respiratory gated with minimal effort. Validation of the method has been performed using a dynamic phantom and accuracy in tidal volumes determined to be within 16%. Rats and mice have been scanned and processed using the proposed method and compared to physiological-based measurement. With the proposed method, image quality is significantly improved in addition to providing quantitative information regarding tidal lung volumes.     

Regression-based identification of behavior-encoding neurons during large-scale optical imaging of neural activity at cellular resolution

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.     

An anatomically driven anisotropic diffusion filtering method for 3D SPECT reconstruction

In this study, we aim to reconstruct single-photon emission computed tomography images using anatomical information from magnetic resonance imaging as a priori knowledge about the activity distribution. The trade-off between anatomical and emission data is one of the main concerns for such studies. In this work, we propose an anatomically driven anisotropic diffusion filter (ADADF) as a penalized maximum likelihood expectation maximization optimization framework. The ADADF method has improved edge-preserving denoising characteristics compared to other smoothing penalty terms based on quadratic and non-quadratic functions. The proposed method has an important ability to retain information which is absent in the anatomy. To make our approach more stable to the noise-edge classification problem, robust statistics have been employed. Comparison of the ADADF method is performed with a successful anatomically driven technique, namely, the Bowsher prior (BP). Quantitative assessment using simulated and clinical neuroreceptor volumetric data show the advantage of the ADADF over the BP. For the modelled data, the overall image resolution, the contrast, the signal-to-noise ratio and the ability to preserve important features in the data are all improved by using the proposed method. For clinical data, the contrast in the region of interest is significantly improved using the ADADF compared to the BP, while successfully eliminating noise.     

Three‐dimensional dynamic contrast‐enhanced MRI for the accurate,extensive quantification of microvascular permeability in atherosclerotic plaques

Atherosclerotic plaques that cause stroke and myocardial infarction are characterized by increased microvascular permeability and inflammation. Dynamic contrast‐enhanced MRI (DCE‐MRI) has been proposed as a method to quantify vessel wall microvascular permeability in vivo. Until now, most DCE‐MRI studies of atherosclerosis have been limited to two‐dimensional (2D) multi‐slice imaging. Although providing the high spatial resolution required to image the arterial vessel wall, these approaches do not allow the quantification of plaque permeability with extensive anatomical coverage, an essential feature when imaging heterogeneous diseases, such as atherosclerosis. To our knowledge, we present the first systematic evaluation of three‐dimensional (3D), high‐resolution, DCE‐MRI for the extensive quantification of plaque permeability along an entire vascular bed, with validation in atherosclerotic rabbits. We compare two acquisitions: 3D turbo field echo (TFE) with motion‐sensitized‐driven equilibrium (MSDE) preparation and 3D turbo spin echo (TSE). We find 3D TFE DCE‐MRI to be superior to 3D TSE DCE‐MRI in terms of temporal stability metrics. Both sequences show good intra‐ and inter‐observer reliability, and significant correlation with ex vivo permeability measurements by Evans Blue near‐infrared fluorescence (NIRF). In addition, we explore the feasibility of using compressed sensing to accelerate 3D DCE‐MRI of atherosclerosis, to improve its temporal resolution and therefore the accuracy of permeability quantification. Using retrospective under‐sampling and reconstructions, we show that compressed sensing alone may allow the acceleration of 3D DCE‐MRI by up to four‐fold. We anticipate that the development of high‐spatial‐resolution 3D DCE‐MRI with prospective compressed sensing acceleration may allow for the more accurate and extensive quantification of atherosclerotic plaque permeability along an entire vascular bed. We foresee that this approach may allow for the comprehensive and accurate evaluation of plaque permeability in patients, and may be a useful tool to assess the therapeutic response to approved and novel drugs for cardiovascular disease. Copyright © 2015 John Wiley & Sons, Ltd.     

Use of penalty terms in gradient-based iterative reconstruction schemes for optical tomography

It is well known that the reconstruction problem in optical tomography is ill-posed. In other words, many different spatial distributions of optical properties inside the medium can lead to the same detector readings on the surface of the medium under consideration. Therefore, the choice of an appropriate method to overcome this problem is of crucial importance for any successful optical tomographic image reconstruction algorithm. In this work we approach the problem within a gradient-based iterative image reconstruction scheme. The image reconstruction is considered to be a minimization of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain a priori information about the system. For the efficient minimization of this objective function the gradient with respect to the spatial distribution of optical properties is calculated. Besides presenting the underlying concepts in our approach to overcome ill-posedness in optical tomography, we will show numerical results that demonstrate how prior knowledge, represented as penalty terms, can improve the reconstruction results.     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

Fluorescence diffuse optical tomography with functional and anatomical a priori information: feasibility study

Fluorescence diffuse optical tomography (FT) is an emerging molecular imaging technique that can spatially resolve both fluorophore concentration and lifetime parameters. In this study, we investigate the performance of a frequency-domain FT system for small inclusions that are embedded in a heterogeneous background. The results demonstrate that functional and structural a priori information is crucial to be able to recover both parameters with high accuracy. The functional a priori information is defined by the absorption and scattering maps at both excitation and emission wavelengths. Similarly, the boundaries of the small inclusion and different regions in the background are utilized as the structural a priori information. Without a priori information, the fluorophore concentration of a 5 mm inclusion in a 40 mm medium is recovered with 50% error, while the lifetime cannot be recovered at all. On the other hand, when both functional and structural information are available, the true lifetime can be recovered and the fluorophore concentration can be estimated only with 5% error. This study shows that a hybrid system that can acquire diffuse optical absorption tomography (DOT), FT and anatomical images in the same setting is essential to be able to recover the fluorophore concentration and lifetime accurately in vivo.     

A custom-built PET phantom design for quantitative imaging of printed distributions

This note presents a practical approach to a custom-made design of PET phantoms enabling the use of digital radioactive distributions with high quantitative accuracy and spatial resolution. The phantom design allows planar sources of any radioactivity distribution to be imaged in transaxial and axial (sagittal or coronal) planes. Although the design presented here is specially adapted to the high-resolution research tomograph (HRRT), the presented methods can be adapted to almost any PET scanner. Although the presented phantom design has many advantages, a number of practical issues had to be overcome such as positioning of the printed source, calibration, uniformity and reproducibility of printing. A well counter (WC) was used in the calibration procedure to find the nonlinear relationship between digital voxel intensities and the actual measured radioactive concentrations. Repeated printing together with WC measurements and computed radiography (CR) using phosphor imaging plates (IP) were used to evaluate the reproducibility and uniformity of such printing. Results show satisfactory printing uniformity and reproducibility; however, calibration is dependent on the printing mode and the physical state of the cartridge. As a demonstration of the utility of using printed phantoms, the image resolution and quantitative accuracy of reconstructed HRRT images are assessed. There is very good quantitative agreement in the calibration procedure between HRRT, CR and WC measurements. However, the high resolution of CR and its quantitative accuracy supported by WC measurements made it possible to show the degraded resolution of HRRT brain images caused by the partial-volume effect and the limits of iterative image reconstruction.     

Statistical inversion for medical x-ray tomography with few radiographs: II. Application to dental radiology

Diagnostic and operational tasks in dental radiology often require three-dimensional information that is difficult or impossible to see in a projection image. A CT-scan provides the dentist with comprehensive three-dimensional data. However, often CT-scan is impractical and, instead, only a few projection radiographs with sparsely distributed projection directions are available. Statistical (Bayesian) inversion is well-suited approach for reconstruction from such incomplete data. In statistical inversion, a priori information is used to compensate for the incomplete information of the data. The inverse problem is recast in the form of statistical inference from the posterior probability distribution that is based on statistical models of the projection data and the a priori information of the tissue. In this paper, a statistical model for three-dimensional imaging of dentomaxillofacial structures is proposed. Optimization and MCMC algorithms are implemented for the computation of posterior statistics. Results are given with in vitro projection data that were taken with a commercial intraoral x-ray sensor. Examples include limited-angle tomography and full-angle tomography with sparse projection data. Reconstructions with traditional tomographic reconstruction methods are given as reference for the assessment of the estimates that are based on the statistical model.     

MR-based keyhole SPECT for small animal imaging

The rationale for multi-modality imaging is to integrate the strengths of different imaging technologies while reducing the shortcomings of an individual modality. The work presented here proposes a limited-field-of-view (LFOV) SPECT reconstruction technique that can be implemented on a multi-modality MR/SPECT system that can be used to obtain simultaneous MRI and SPECT images for small animal imaging. The reason for using a combined MR/SPECT system in this work is to eliminate any possible misregistration between the two sets of images when MR images are used as a priori information for SPECT. In nuclear imaging the target area is usually smaller than the entire object; thus, focusing the detector on the LFOV results in various advantages including the use of a smaller nuclear detector (less cost), smaller reconstruction region (faster reconstruction) and higher spatial resolution when used in conjunction with pinhole collimators with magnification. The MR/SPECT system can be used to choose a region of interest (ROI) for SPECT. A priori information obtained by the full field-of-view (FOV) MRI combined with the preliminary SPECT image can be used to reduce the dimensions of the SPECT reconstruction by limiting the computation to the smaller FOV while reducing artifacts resulting from the truncated data. Since the technique is based on SPECT imaging within the LFOV it will be called the keyhole SPECT (K-SPECT) method. At first MRI images of the entire object using a larger FOV are obtained to determine the location of the ROI covering the target organ. Once the ROI is determined, the animal is moved inside the radiofrequency (rf) coil to bring the target area inside the LFOV and then simultaneous MRI and SPECT are performed. The spatial resolution of the SPECT image is improved by employing a pinhole collimator with magnification >1 by having carefully calculated acceptance angles for each pinhole to avoid multiplexing. In our design all the pinholes are focused to the center of the LFOV. K-SPECT reconstruction is accomplished by generating an adaptive weighting matrix using a priori information obtained by simultaneously acquired MR images and the radioactivity distribution obtained from the ROI region of the SPECT image that is reconstructed without any a priori input. Preliminary results using simulations with numerical phantoms show that the image resolution of the SPECT image within the LFOV is improved while minimizing artifacts arising from parts of the object outside the LFOV due to the chosen magnification and the new reconstruction technique. The root-mean-square-error (RMSE) in the out-of-field artifacts was reduced by 60% for spherical phantoms using the K-SPECT reconstruction technique and by 48.5-52.6% for the heart in the case with the MOBY phantom. The K-SPECT reconstruction technique significantly improved the spatial resolution and quantification while reducing artifacts from the contributions outside the LFOV as well as reducing the dimension of the reconstruction matrix.     

High-resolution MRI of the human palatine tonsil and its schematic anatomic 3D reconstruction

The palatine tonsils form an important part of the human immune system. Together with the other lymphoid tonsils of Waldeyer's tonsillar ring, they act as the first line of defense against ingested or inhaled pathogens. Although histologically stained sections of the palatine tonsil are widely available, they represent the tissue only in two dimensions and do not provide reference to three-dimensional space. Such a representation of a tonsillar specimen based on imaging data as a 3D anatomical reconstruction is lacking both in scientific publications and especially in textbooks. As a first step in this direction, the objective of the present work was to image a resected tonsil specimen with high spatial resolution in a 9.4 T small-bore pre-clinical MRI and to combine these data with data from the completely sectioned and H&E stained same palatine tonsil. Based on the information from both image modalities, a 3D anatomical sketch was drawn by a scientific graphic artist. In perspective, such studies could help to overcome the difficulty of capturing the spatial extent and arrangement of anatomical structures from 2D images and to establish a link between three-dimensional anatomical preparations and two-dimensional sections or illustrations, as they have been found so far in common textbooks and anatomical atlases.     

Clinically feasible reconstruction of 3D whole-body PET/CT data using blurred anatomical labels

We present the results of utilizing aligned anatomical information from CT images to locally adjust image smoothness during the reconstruction of three-dimensional (3D) whole-body positron emission tomography (PET) data. The ability of whole-body PET imaging to detect malignant neoplasms is becoming widely recognized. Potentially useful, however, is the role of whole-body PET in quantitative estimation of tracer uptake. The utility of PET in oncology is often limited by the high level of statistical noise in the images. Reduction in noise can be obtained by incorporating a priori image smoothness information from correlated anatomical information during the reconstruction of PET data. A combined PET/CT scanner allows the acquisition of accurately aligned PET and x-ray CT whole-body data. We use the Fourier rebinning algorithm (FORE) to accurately convert the 3D PET data to two-dimensional (2D) data to accelerate the image reconstruction process. The 2D datasets are reconstructed with successive over-relaxation of a penalized weighted least squares (PWLS) objective function to model the statistics of the acquisition, data corrections, and rebinning. A 3D voxel label model is presented that incorporates the anatomical information via the penalty weights of the PWLS objective function. This combination of FORE + PWLS + labels was developed as it allows for both reconstruction of 3D whole-body data sets in clinically feasible times and also the inclusion of anatomical information in such a way that convergence can be guaranteed. Since mismatches between anatomical (CT) and functional (PET) data are unavoidable in practice, the labels are 'blurred' to reflect the uncertainty associated with the anatomical information. Simulated and experimental results show the potential advantage of incorporating anatomical information by using blurred labels to calculate the penalty weights. We conclude that while the effect of this method on detection tasks is complicated and unclear, there is an improvement on the estimation task.     

Fuzzy modeling of electrical impedance tomography images of the lungs

OBJECTIVES: Aiming to improve the anatomical resolution of electrical impedance tomography images, we developed a fuzzy model based on electrical impedance tomography's high temporal resolution and on the functional pulmonary signals of perfusion and ventilation. INTRODUCTION: Electrical impedance tomography images carry information about both ventilation and perfusion. However, these images are difficult to interpret because of insufficient anatomical resolution, such that it becomes almost impossible to distinguish the heart from the lungs. METHODS: Electrical impedance tomography data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was administered. The fuzzy model was elaborated in three parts: a modeling of the heart, the pulmonary ventilation map and the pulmonary perfusion map. Image segmentation was performed using a threshold method, and a ventilation/perfusion map was generated. RESULTS: Electrical impedance tomography images treated by the fuzzy model were compared with the hypertonic saline injection method and computed tomography scan images, presenting good results. The average accuracy index was 0.80 when comparing the fuzzy modeled lung maps and the computed tomography scan lung mask. The average ROC curve area comparing a saline injection image and a fuzzy modeled pulmonary perfusion image was 0.77. DISCUSSION: The innovative aspects of our work are the use of temporal information for the delineation of the heart structure and the use of two pulmonary functions for lung structure delineation. However, robustness of the method should be tested for the imaging of abnormal lung conditions. CONCLUSIONS: These results showed the adequacy of the fuzzy approach in treating the anatomical resolution uncertainties in electrical impedance tomography images.