Model-based Iterative Algorithm Research Articles

Retracted: Diagnostic Value of Model-Based Iterative Algorithm in Tuberculous Pleural Effusion

Retracted: Diagnostic Value of Model-Based Iterative Algorithm in Tuberculous Pleural Effusion

MoG-DS: model-guided deep convolutional network for joint denoising and super-resolution of a single-photon counting image

Single-photon counting (SPC) imaging has attracted considerable research attention in recent years due to its capability to detect targets under extremely low-light conditions. However, the spatial quality of SPC images is always unsatisfactory because they typically suffer from considerable effects of noise and their spatial resolution is low. Most traditional methods are dedicated to solving the noise problem while ignoring the improvement of spatial resolution. To address these challenging issues, we propose a novel model-guided deep convolutional network for joint denoising and super-resolution (SR) of SPC images. First, we introduce a model-based iterative optimization algorithm with deep regularizer to unify denoising and SR into one problem. Second, we construct a model-guided deep convolutional network by unfolding the aforementioned model-based iterative algorithm to achieve an optimal solution. All modules in the proposed network are interpretable due to the special model-guided design, and they enable good generalization in real situations. In addition, the deep regularizer and other parameters in the proposed network are jointly optimized in an end-to-end manner, which efficiently reduces the difficulty of parameter design. Extensive simulation and real experimental results are reported to demonstrate the superiority of the proposed method in terms of visual comparison and quantitative analysis, respectively.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations.

To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher (P=0.003). Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy.

Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data. Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed. The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm. Our algorithm has the potential to improve glycemic control in people with T1D using MDI. This work is a step forward towards a decision support system that improves their quality of life.

Acoustic Scattering Mediated Single Detector Optoacoustic Tomography.

Optoacoustic image formation is conventionally based upon ultrasound time-of-flight readings from multiple detection positions. Herein, we exploit acoustic scattering to physically encode the position of optical absorbers in the acquired signals, thus reducing the amount of data required to reconstruct an image from a single waveform. This concept is experimentally tested by including a random distribution of scatterers between the sample and an ultrasound detector array. Ultrasound transmission through a randomized scattering medium was calibrated by raster scanning a light-absorbing microparticle across a Cartesian grid. Image reconstruction from a single time-resolved signal was then enabled with a regularized model-based iterative algorithm relying on the calibration signals. The signal compression efficiency is facilitated by the relatively short acquisition time window needed to capture the entire scattered wave field. The demonstrated feasibility to form an image using a single recorded optoacoustic waveform paves a way to the development of faster and affordable optoacoustic imaging systems.

DIRA-3D—a model-based iterative algorithm for accurate dual-energy dual-source 3D helical CT

Quantitative dual-energy computed tomography may improve the accuracy of treatment planning in radiation therapy. Of special interest are algorithms that can estimate material composition of the imaged object. One example of such an algorithm is the 2D model-based iterative reconstruction algorithm DIRA. The aim of this work is to extend this algorithm to 3D so that it can be used with cone-beams and helical scanning. In the new algorithm, the parallel FBP method was replaced with the approximate 3D FBP-based PI-method. Its performance was tested using a mathematical phantom consisting of six ellipsoids. The algorithm substantially reduced the beam-hardening artefact and the artefacts caused by approximate reconstruction after six iterations. Compared to Alvarez-Macovski’s base material decomposition, DIRA-3D does not require geometrically consistent projections and hence can be used in dual-source CT scanners. Also, it can use several tissue-specific material bases at the same time to represent the imaged object.

Measurement of charge density in nanoscale materials using off-axis electron holography

Three approaches for the measurement of charge density distributions in nanoscale materials from electron optical phase images recorded using off-axis electron holography are illustrated through the study of an electrically biased needle-shaped sample. We highlight the advantages of using a model-based iterative algorithm, which allows a priori information, such as the shape of the object and the influence of charges that are located outside the field of view, to be taken into account. The recovered charge density can be used to infer the electric field and electrostatic potential.

Propagation-based phase-contrast tomography of a guinea pig inner ear with cochlear implant using a model-based iterative reconstruction algorithm.

Propagation-based phase-contrast computed tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high contrast between materials with similar attenuation properties. However, one of the most-widely used phase-retrieval algorithms imposes a homogeneity assumption onto the sample, which leads to artifacts for numerous applications where this assumption is violated. Prominent examples are biological samples with highly-absorbing implants. Using synchrotron radiation, we demonstrate by the example of a guinea pig inner ear with a cochlear implant electrode, how a recently developed model-based iterative algorithm for propagation-based phase-contrast computed tomography yields distinct benefits for such a task. We find that the model-based approach improves the overall image quality, removes the detrimental influence of the implant and accurately visualizes the cochlea.

Could new reconstruction CT techniques challenge MRI for the detection of brain metastases in the context of initial lung cancer staging?

To evaluate the diagnostic performance of brain CT images reconstructed with a model-based iterative algorithm performed at usual and reduced dose. 115 patients with histologically proven lung cancer were prospectively included over 15 months. Patients underwent two CT acquisitions at the initial staging, performed on a 256-slice MDCT, at standard (CTDIvol: 41.4 mGy) and half dose (CTDIvol: 20.7 mGy). Both image datasets were reconstructed with filtered back projection (FBP) and iterative model-based reconstruction (IMR) algorithms. Brain MRI was considered as the reference. Two blinded independent readers analysed the images. Ninety-three patients underwent all examinations. At the standard dose, eight patients presented 17 and 15 lesions on IMR and FBP CT images, respectively. At half-dose, seven patients presented 15 and 13 lesions on IMR and FBP CT images, respectively. The test could not highlight any significant difference between the standard dose IMR and the half-dose FBP techniques (p-value = 0.12). MRI showed 46 metastases on 11 patients. Specificity, negative and positive predictive values were calculated (98.9-100 %, 93.6-94.6 %, 75-100 %, respectively, for all CT techniques). No significant difference could be demonstrated between the two CT reconstruction techniques. • No significant difference between IMR100 and FBP50 was shown. • Compared to FBP, IMR increased the image quality without diagnostic impairment. • A 50 % dose reduction combined with IMR reconstructions could be achieved. • Brain MRI remains the best tool in lung cancer staging.

Submillisievert Computed Tomography of the Chest Using Model-Based Iterative Algorithm

The aim of this study was to define optimal tube potential for soft tissue and vessel visualization in dose-reduced chest CT protocols using model-based iterative algorithm in average and overweight patients. Thirty-six patients receiving chest CT according to 3 protocols (120 kVp/noise index [NI], 60; 100 kVp/NI, 65; 80 kVp/NI, 70) were included in this prospective study, approved by the ethics committee. Patients' physical parameters and dose descriptors were recorded. Images were reconstructed with model-based algorithm. Two radiologists evaluated image quality and lesion conspicuity; the protocols were intraindividually compared with preceding control CT reconstructed with statistical algorithm (120 kVp/NI, 20). Mean and standard deviation of attenuation of the muscle and fat tissues and signal-to-noise ratio of the aorta were measured. Diagnostic images (lesion conspicuity, 95%-100%) were acquired in average and overweight patients at 1.34, 1.02, and 1.08 mGy and at 3.41, 3.20, and 2.88 mGy at 120, 100, and 80 kVp, respectively. Data are given as CT dose index volume values. Model-based algorithm allows for submillisievert chest CT in average patients; the use of 100 kVp is recommended.

An iterative inversion algorithm with application to the polarimetric radar response of vegetation canopies

The retrieval of scene parameters from polarimetric radar data using an iterative inversion approach is considered. The theoretical development of a general, model-based iterative algorithm for inversion of polarimetric radar data is presented. Factors relevant to its implementation, such as sensor configuration, algorithm optimization and computational structure are discussed. The algorithm is applied to the specific problem of inverting the vector radiative transfer model for a simplified, representative vegetation canopy consisting of vertical trunks, leaves, and a rough ground surface. The results of this inversion are in excellent agreement with simulated data generated using the radiative transfer model. The convergence properties of the algorithm are evaluated, and it is found that successful convergence is achieved in about 90% to 95% of the cases tested for the implementation used in this work. An error analysis is presented which considers the effect of both systematic and measurement derived errors. Typical error bounds for the current application are approximately /spl plusmn/3%, allowing for /spl plusmn/0.5 dB accuracy in the measured radar data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An Object Locating Method with Uncertainties Applied to an Ultrasonic Multi-Sensor System

We present a model-based iterative algorithm for estimating the position and orientation of regular objects applied to multiple ultrasonic distance measurements. It is based on standard minimum error covariance estimation that minimizes the weighted square sum of the measures reflecting errors between the real and nominal objects. A discussion is also provided about the use of the variance/covariance estimate as a measure of uncertainty in designing the configuration of the ultra-sonic multi-sensor system.