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The regularization of retinal oxygen tension estimation was previously proposed with an assumption that phosphorescence intensity images are corrupted by additive Gaussian noise. Based on this assumption, a regularized least-squares estimate has been shown to be better than a conventional least-squares estimation. However, this assumption is inconsistent with the acquisition process of phosphorescence intensity images acquired using an intensified charge-coupled device camera. Almost the entire acquisition process is governed by the natural aspects of photons. Therefore, a method based on photon counting statistics is more appropriate. In this study, we propose a regularized oxygen tension estimation method based on photon counting statistics and a phosphorescence lifetime imaging model.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method., Competing Interests: The authors declare that they have no competing interests., (© 2024 Unal et al.)

Volumetric optical microscopy approaches that enable acquisition of three-dimensional (3D) information from a biological sample are attractive for numerous non-invasive imaging applications. The unprecedented structural details that these techniques provide have helped in our understanding of different aspects of architecture of cells, tissues, and organ systems as they occur in their natural states. Nonetheless, the instrumentation for most of these techniques is sophisticated, bulky, and costly, and is less affordable to most laboratory settings. Several miniature imagers based on webcams or low-cost sensors featuring easy assembly have been reported, for in situ imaging of biological structures at low costs. However, they have not been able to achieve the ability of 3D imaging throughout the entire volumes for spatiotemporal analyses of the structural changes in these specimens. Here we present a miniaturized optical tomography (mini-Opto) platform for low-cost, volumetric characterization of engineered living systems through hardware optimizations as well as applications of an optimized algebraic algorithm for image reconstruction.

Background: Iterative image reconstruction in Digital Breast Tomosynthesis (DBT) is a developing modality that produces three-dimensional (3D) reconstructed images of a breast to detect suspicious lesions. Algebraic reconstruction technique (ART), one of the iterative image reconstruction methods, was applied to reconstruct 3D data of breast and is becoming as one alternative method for the conventional image reconstruction techniques such as filtered back projection (FBP) in DBT imaging., Objective: A new majorization-minimization (MM) algorithm was presented for TV denoising of signals. In the field of DBT, however, the algorithm has not yet been applied. In this study, we proposed a new method of "ART+TV3D+MM," which applies (MM) algorithm to the images reconstructed by ART+TV3D for different imaging dose levels to investigate a possible reduction of radiation dose., Methods: Projections of a real breast phantom (CD Pasmam 1054) were acquired with a Siemens MAMMOMAT DBT system. The proposed new method was repeated and tested with 3 different radiation dose levels. The quality of the images reconstructed using the proposed new method were compared with those generated by the commonly used FBP method using both qualitative and quantitative assessments., Results: The new method showed superior results in terms of visual assessment, contrast to noise ratios (CNR), full width at half maximum (FWHM) values and 1D profiles compared with FBP of the Siemens MAMMOMAT. CNR values were evaluated for two different region of interests (ROIs). For instance, CNR values of ROI-2 of FBP and of new method were 1.670 and 1.978 at 100 mAs, respectively. Moreover, while CNR value of ROI-1 of FBP at 100 mAs was 0.955, CNR value of ROI-1 of using new method at 100 mAs was 48.163. FWHM values for FBP and the new method were 2.328 and 1.765 at 56 mAs, 2.032 and 1.661 at 100 mAs, and 2.111 and 1.736 at 199 mAs, respectively., Conclusions: The results support that using the new method of "ART+TV3D+MM" could help decrease the radiation dose level, which is one of the most critical limitations of DBT imaging.

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.

Background: After the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems., Methods: In this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV., Results: A tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV., Conclusions: RMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

This paper presents a compressed sensing based reconstruction method for 3D digital breast tomosynthesis (DBT) imaging. Algebraic reconstruction technique (ART) has been in use in DBT imaging by minimizing the isotropic total variation (TV) of the reconstructed image. The resolution in DBT differs in sagittal and axial directions which should be encountered during the TV minimization. In this study we develop a 3D anisotropic TV (ATV) minimization by considering the different resolutions in different directions. A customized 3D Shepp-logan phantom was generated to mimic a real DBT image by considering the overlapping tissue and directional resolution issues. Results of the ART, ART+3D TV and ART+3D ATV are compared using structural similarity (SSIM) diagram.

Phosphorescence lifetime imaging is commonly used to generate oxygen tension maps of retinal blood vessels by classical least squares (LS) estimation method. A spatial regularization method was later proposed and provided improved results. However, both methods obtain oxygen tension values from the estimates of intermediate variables, and do not yield an optimum estimate of oxygen tension values, due to their nonlinear dependence on the ratio of intermediate variables. In this paper, we provide an improved solution by devising a regularized direct least squares (RDLS) method that exploits available knowledge in studies that provide models of oxygen tension in retinal arteries and veins, unlike the earlier regularized LS approach where knowledge about intermediate variables is limited. The performance of the proposed RDLS method is evaluated by investigating and comparing the bias, variance, oxygen tension maps, 1-D profiles of arterial oxygen tension, and mean absolute error with those of earlier methods, and its superior performance both quantitatively and qualitatively is demonstrated.

Background: Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied., Methods: In this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices., Results: A 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART., Conclusions: Computer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.

Background: Monitoring retinal oxygenation is of primary importance in detecting the presence of some common eye diseases. To improve the estimation of oxygen tension in retinal vessels, regularized least-squares (RLS) method was shown to be very effective compared with the conventional least-squares (LS) estimation. In this study, we propose an accelerated RLS estimation method for the problem of assessing the oxygenation of retinal vessels from phosphorescence lifetime images., Methods: In the previous work, gradient descent algorithms were used to find the minimum of the RLS cost function. This approach is computationally expensive, especially when the oxygen tension map is large. In this study, using a closed-form solution of the RLS estimation and some inherent properties of the problem at hand, the RLS process is reduced to the weighted averaging of the LS estimates. This decreases the computational complexity of the RLS estimation considerably without sacrificing its performance., Results: Performance analyses are conducted using both real and simulated data sets. In terms of computational complexity, the proposed RLS estimation method is significantly better than RLS methods that use gradient descent algorithms to find the minimum of the cost function. Additionally, there is no significant difference between the estimates acquired by the proposed and conventional RLS estimation methods., Conclusion: The proposed RLS estimation method for computing the retinal oxygen tension is computationally efficient, and produces estimates with negligible difference from those obtained by iterative RLS methods. Further, the results of this study can be applied to other lifetime imaging problems that have similar properties.

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

The level of retinal oxygenation is potentially an important cue to the onset or presence of some common retinal diseases. An improved method for assessing oxygen tension in retinal blood vessels from phosphorescence lifetime imaging data is reported in this paper. The optimum estimate for phosphorescence lifetime and oxygen tension is obtained by regularizing the least-squares (LS) method. The estimation method is implemented with an iterative algorithm to minimize a regularized LS cost function. The effectiveness of the proposed method is demonstrated by applying it to simulated data as well as image data acquired from rat retinas. The method is shown to yield estimates that are robust to noise and whose variance is lower than that obtained with the classical LS method.

The problem of non-invasive detection of respiratory phases and onsets without making direct airflow measurement is addressed here. Currently available techniques require the use of multichannel recorded sounds of both chest and trachea. In this paper, we propose a method which detects both respiratory phases and onsets using only chest sound data. Prior signal information in both time and frequency from the chest sound is exploited to isolate the lung component of the sound and the quasi-periodicity of its short-term energy is used to develop a configuration of nonlinear filters and bandpass filters to estimate the respiratory phase onsets. Performance results for the proposed method are reported for the case of low and medium flow rates. The average onset localizing accuracy of the proposed method is shown to be comparable to that obtained with data from more than one recording channel.

Closure of the aortic valve (A2) and the pulmonary valve (P2) generates the second heart sound (S2). The time separation between A2 and P2 is known as the A2-P2 split and it has very important diagnostic potential. Methods proposed in the past to measure the split noninvasively are limited either by prior signal modeling assumptions or by reliance on manual processing in key steps. In this work, we propose a new method that is devised to noninvasively provide an automated measurement of the time split between A2 and P2 with minimal prior assumptions on signal models. Our method is based on tracking the changes of the instantaneous frequency (IF) of S2 via time frequency representation of the S2 obtained by smoothed Wigner-Ville Distribution. The cues provided by the changes in the IF trajectory are analyzed using an automated procedure to identify the onset of the P2 pulse. Simulations are carried out to demonstrate the effectiveness of the procedure in estimating the split. The performance of the method in the presence of noise varying between 6 dB and 8 dB for several trials and interference is investigated and the robustness of the method is demonstrated.