Dynamic cell tracking using time-lapse MRI with variable temporal resolution Cartesian sampling.

To assess the impact of parallel imaging algorithms on image quality and volumetric accuracy of CINE magnetic resonance imaging (MRI) with high temporal and spatial resolution using a new 32-channel dedicated cardiac phased array coil. Fourteen individuals underwent steady-state free precession (SSFP) CINE MRI using a 32-element phased-array coil and parallel imaging acceleration using spatiotemporal sensitivity encoding (TSENSE). Acquisition acceleration ranged from R = 2 to 7. In conjunction with data extracted from phantom measurements, contrast-to-noise ratio (CNR) performance was evaluated for each acceleration factor and subjective image quality was evaluated by two independent readers. In addition, volumetric assessment was performed for each acceleration factor based on a single breath-hold multi-slice data acquisition. Results were compared to nonTSENSE measurements. CNR for non-accelerated CINE (R = 1) was 45.7 +/- 12.8 and showed a constant decrease with increase in acceleration of 51% at R = 4 and 86% at R = 7. CNR losses accompanied reductions in subjective image quality. Volumetric evaluation was accurate for R <or= 4, with significant underestimation of ejection fraction (EF) at higher accelerations. This study shows that one-dimensional acceleration factors up to R = 4 allow accurate SSFP CINE MRI even though CNR is significantly reduced. This allows for a marked reduction in scan time and allows for multi-slice CINE imaging with high spatial and temporal resolution.

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

To extend susceptibility weighted imaging (SWI) to multiple echoes with an adapted homodyne filtering of phase images for the computation of venograms with improved signal to noise ratio (SNR) and contrast to noise ratio (CNR) and to produce high resolution maps of R(2) relaxation. Three-dimensional multi echo gradient echo data were acquired with five equidistant echoes ranging from 13 to 41 ms. The phase images of each echo were filtered with filter parameters adjusted to the echo time, converted into a phase mask, and combined with the corresponding magnitude images to obtain susceptibility weighted images. The individual images were then averaged. Conventional single echo data were acquired for comparison. Maps of R(2) relaxation rates were computed from the magnitude data. Field maps derived from the phase data were used to correct R(2) for the influences from background inhomogeneities of the static magnetic field. Compared with the single echo images, the combined images had an increase in SNR by 46% and an improvement in CNR by 34 to 80%, improved visibility of small venous vessels and reduced blurring along the readout direction. The R(2) values of different tissue types are in good agreement with values from the literature. Acquisition of SWI with multiple echoes leads to an increase in SNR and CNR and it allows the computation of high resolution maps of R(2) relaxation.

To evaluate in a rat brain glioma model intraindividual tumor enhancement at 1.5 T using gadobutrol (Gadovist), a nonionic, macrocyclic chelate currently in clinical trials in the United States, in comparison with both an ionic macrocyclic chelate, gadoterate meglumine (Dotarem), and an ionic linear chelate, gadopentetate dimeglumine (Magnevist), and to compare the degree of tumor enhancement with gadobutrol at 1.5 and 3 T. A total of 24 rats, divided into three groups with n = 8 animals per group, were evaluated. Animals in group 1 received injections of gadobutrol and gadopentetate dimeglumine, whereas those in group 2 received gadobutrol and gadoterate meglumine. Injections were performed in random order and separated by 24 hours. Magnetic resonance imaging (MRI) examinations were performed immediately following each contrast injection with a 1.5 T MR system. Animals in group 3 received gadobutrol injections using the same protocol but with scans performed at 1.5 and 3 T. In all examinations, T1-weighted images were acquired precontrast, 1 minute postcontrast, and at 4 consecutive 2-minute intervals thereafter. A contrast dose of 0.1 mmol/kg was used in all instances. In groups 1 and 2, tumor signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were higher for gadobutrol compared to both other agents at each timepoint postcontrast injection. The improvement in tumor CNR with gadobutrol, depending on time, was between 12% and 40% versus gadopentetate dimeglumine, with the difference achieving statistical significance at 7 minutes. The improvement in tumor CNR with gadobutrol, depending on time, was between 15% and 27% versus gadoterate meglumine, with the difference statistically significant at 5 and 9 minutes. In group 3 the improvement in tumor SNR and CNR seen with the increase in field strength from 1.5 to 3 T for gadobutrol was statistically significant at all acquired timepoints (P < 0.002). CNR mean values ranged from 10.4 +/- 2.9 to 24.6 +/- 5.0 at 1.5 T and from 20.5 +/- 5.9 to 47.8 +/- 15.7 at 3 T depending on the timepoint postcontrast. Consistently greater tumor enhancement was noted at all measured timepoints following contrast injection with gadobutrol compared to both gadopentetate dimeglumine and gadoterate meglumine at 1.5 T. A substantial further improvement in tumor enhancement was noted using gadobutrol at 3 T.

Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related side effects. The deep-learning image reconstruction strategies were used to quantitatively evaluate the enhanced contrast-to-noise ratio (CNR) and the dose reduction effect of subtracted images. This study aimed to elucidate a comprehensive understanding of the quantitative image quality features of the conventional filtered back projection (FBP) and the advanced intelligent clear-IQ engine (AiCE), a deep learning reconstruction technique. The comparison was made in subtracted images with variable concentrations of contrast agents at variable tube currents and voltages, enhancing our knowledge of these two techniques. Data were obtained using a state-of-the-art 320-detector CT scanner. Image reconstruction was performed using FBP and AiCE with various intensities. The image quality evaluation was based on eight iodine concentrations in the phantom setup. The efficiency of AiCE relative to FBP was assessed by computing parameters including the root mean square error (RMSE), dose-dependent CNR, and potential dose reduction. The results showed that elevated concentrations of iodine and increased tube currents improved AiCE performance regarding CNR enhancement compared to FBP. AiCE also demonstrated a potential dose reduction ranging from 13.7 to 81.9% compared to FBP, suggesting a significant reduction in radiation exposure while maintaining image quality. The employment of deep learning image reconstruction with AiCE presented a significant improvement in CNR and potential dose reduction in CT angiography. This study highlights the potential of AiCE to improve vascular image quality and decrease radiation exposure risk, thereby improving diagnostic precision and patient care in vascular imaging practices.

PurposeTo determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.Materials and MethodsThis HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.ResultsNoise reduction of up to 59% (28.7/65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0/18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24/61) noise reduction and 67% (0.74/1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.ConclusionIodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.

We present a new spatial compounding method to improve the contrast of ultrasonic images for non-delayed sequential beamforming (NDSB). Sequential beamforming adopts more than one beamformer to reconstruct B-mode images which has the advantage of simple front-end electronics and fast data transfer rate. Via field pattern analysis, we propose a compounding method where two more sub-images can be reconstructed along with the NDSB sub-image. These sub-images can be seen as being produced with different transmit origins; thus, their summation enhances image contrast. Image quality was analyzed in terms of spatial resolution, contrast ratio (CR), and contrast-to-noise ratio (CNR). The proposed compounding method improves the lateral resolution up to 41%. In vitro results confirm a 13.0-dB CR and 4.0-dB CNR improvement. In vivo results reveal 10.9-dB and 6.0-dB improvement in CR and CNR for cross-section jugular vein and 8.0-dB and 4.5-dB improvement in CR and CNR for the longitudinal-section carotid artery.

In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations in balancing resolution and frame rate, which can result in suboptimal diagnosticaccuracy. This study aims to introduce a modular beamforming pipeline that overcomes these challenges and enhances PW image quality. By dividing the beamforming process into two modules: a multi-attention U-Net based model for capturing complex dependencies in time-delayed data and a super-resolution model for scaling up to the original B-mode image grid. We seek to improve PW image quality and modularity in the ultrasound imagingprocess. We implemented a modular beamforming approach, comprising a multi-attention U-Net model and a super-resolution model. We conducted experiments using simulated, experimental, and in vivo data from the PICMUS dataset to evaluate the performance of our pipeline against conventional methods such as PW1, PW9, and U-Net. Key metrics assessed included contrast-to-noise ratio (CNR), contrast ratio (CR), generalized Contrast-to-Noise Ratio (gCNR), andresolution. Our model demonstrated superior performance across all metrics. In simulated data, the model achieved a 0.99 improvement in CNR, a 3.5 dB increase in CR, and 18% in gCNR compared to PW1. Experimental data showed a 0.7 enhancement in CNR and a 8.6 dB improvement in CR and 24% increase in gCNR when compared to PW1. In vivo data also revealed significant improvements, with a 1.9 dB increase in CR and a 0.15 enhancement in CNR over PW1. The enhanced performance in the anechoic cyst region underscores the model's effectiveness in improving imagequality. The proposed modular beamforming approach offers significant advantages, including adaptability and improved image quality, despite the complexity of managing two models concurrently. The pipeline's flexibility in frame rate and image quality allows for customization based on specific clinical applications, making it a promising alternative to traditionalmethods.

Breast specific gamma imaging or molecular breast imaging (BSGI) obtains 2D images of (99m)Tc sestamibi distribution in the breast. Molecular breast tomosynthesis (MBT) maps the tracer distribution in 3D by acquiring multiple projections over a limited angular range. Here, the authors compare the performance of the two technologies in terms of spatial resolution, lesion contrast, and contrast-to-noise ratio (CNR) in phantom studies under conditions of clinically relevant sestamibi dose and imaging time. The systems tested were a Dilon 6800 and a MBT prototype developed at the University of Virginia. Both systems comprise a pixelated sodium iodide scintillator, an array of position sensitive photomultipliers, and a parallel hole collimator. The active areas and energy resolution of the systems are similar. System sensitivity, spatial resolution, lesion contrast, and CNR were measured using a Petri dish, a point source phantom, and a breast phantom containing simulated lesions at two depths, respectively. A single BSGI projection was acquired. Five MBT projections were acquired over ±20°. For both modalities, the total scan count density was comparable to that observed for each in typical 10 min human scans following injection of 22 mCi (814 MBq) of (99m)Tc-sestamibi. To assess the impact of reducing the tracer dose, the pixel counts of projection images were later binomially subsampled by a factor of 2 to give images corresponding to an injected activity of approximately 11 mCi (407 MBq). Both unprocessed (pixelated) BSGI projections and interpolated (smoothed) BSGI images displayed by default on the Dilon 6800 workstation were analyzed. Volumetric images were reconstructed from the MBT projections using a maximum likelihood expectation maximization algorithm and extracted slices were analyzed. Over a depth range of 1.5-7.5 cm, BSGI spatial resolution was 5.6-11.5 mm in unprocessed projections and 5.7-12.0 mm in interpolated images. Over the same range, the in-slice MBT spatial resolution was 6.7-9.4 mm. Lesion contrast was significantly improved with MBT relative to BSGI for five out of eight lesions imaged at either the 22 mCi or the 11 mCi dose level (p < 0.05). At both dose levels, significant improvements in CNR with MBT were also found for five out of eight lesions (9.8, 7.8, 6.2 mm lesions at water depth of 1.7 cm and 9.8, 7.8 mm lesions at water depth of 4.5 cm, p < 0.05). The 6.2 and 4.9 mm lesions located at 4.5 cm below the water surface were not visible in either modality at either activity level. Under conditions of equal dose, imaging time and similar detectors, compared to BSGI, MBT provided higher lesion contrast, higher CNR, and spatial resolution that was less depth dependent.

In vivo detection of animal xenograft is a major application of positron emission tomography (PET) imaging. This study investigated the relationship among the scan time, radioactivity, and radiation dose to achieve optimal lesion detectability while minimizing physiologic and pharmacologic effects of imaging procedures in small animal PET studies. A small-animal PET system was modeled based on Monte Carlo simulation to generate the emission image and dose distribution. A multivariate approach was used to investigate the simultaneous effects of tumor size, target-to-background ratio (TBR), scan duration, and injected radioactivity on the contrast-to-noise ratio (CNR) and recovery coefficient (RC). Among the four predictors, TBR and scan time were the most relevant contributors of CNR and RC variations, respectively. In 1.86 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> Bq/ml injected activity, the absorbed doses for a body and tumor with TBR = 2 were 2.46 and 5.39 cGy, respectively. A substantial improvement in CNR or RC was not observed in images acquired with radiotracer activity larger than 9.3 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> Bq/ml and scan duration longer than 30 min. The coefficient of determination was greater than 0.93 for both regression models. Although the improvement of counting statistics by increasing scan duration and injected activity can reduce statistical noise and improve apparent spatial resolution, it is crucial to maintain the radiation exposure and anesthetic dose received by animals as low as possible to reduce biological damage.

A novel information retrieval algorithm for X-ray grating-based phase-contrast imaging based on the deconvolution of the object and the reference phase stepping curve (PSC) as proposed by Modregger et al. was investigated in this paper. We applied the method for the first time on data obtained with a polychromatic spectrum and compared the results to those, received by applying the commonly used method, based on a Fourier analysis. We confirmed the expectation, that both methods deliver the same results for the absorption and the differential phase image. For the darkfield image, a mean contrast-to-noise ratio (CNR) increase by a factor of 1.17 using the new method was found. Furthermore, the dose saving potential was estimated for the deconvolution method experimentally. It is found, that for the conventional method a dose which is higher by a factor of 1.66 is needed to obtain a similar CNR value compared to the novel method. A further analysis of the data revealed, that the improvement in CNR and dose efficiency is due to the superior background noise properties of the deconvolution method, but at the cost of comparability between measurements at different applied dose values, as the mean value becomes dependent on the photon statistics used.

To develop a variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution for dynamic MRI applications and to validate it in real-time phase contrast MRI (PC-MRI). The proposed method, called CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA), begins by producing a sequence of phase encoding indices based on the golden ratio increment. Then, variable density is introduced by nonlinear stretching of the indices. Finally, the elements of the resulting sequence are rounded up to the nearest integer. The performance of CAVA is evaluated using PC-MRI data from a pulsatile flow phantom and real-time, free-breathing data from ten healthy volunteers. CAVA enabled image recovery at various temporal resolutions that were selected retrospectively. For the pulsatile flow phantom, image quality and flow quantification accuracy from CAVA were comparable to that from another pseudo-random sampling pattern with fixed temporal resolution. In addition, flow quantification results based on CAVA were in good agreement with a breath-held segmented acquisition. By allowing retrospective binning of the MRI data, CAVA provides an avenue to retrospectively adjust the temporal resolution of PC-MRI.

For noninvasive and quantitative measurements of global two-dimensional (2D) heart wall motion, speckle tracking methods have been developed and applied. In these conventional methods, the frame rate is limited to about 200 Hz, corresponding to the sampling period of 5 ms. However, myocardial function during short periods, as obtained by these conventional speckle tracking methods, remains unclear owing to low temporal and spatial resolutions of these methods. Moreover, an important parameter, the optimal kernel size, has not been thoroughly investigated. In our previous study, the optimal kernel size was determined in a phantom experiment under a high signal-to-noise ratio (SNR), and the determined optimal kernel size was applied to the in vivo measurement of 2D displacements of the heart wall by block matching using normalized cross-correlation between RF echoes at a high frame rate of 860 Hz, corresponding to a temporal resolution of 1.1 ms. However, estimations under low SNRs and the effects of the difference in echo characteristics, i.e., specular reflection and speckle-like echoes, have not been considered, and the evaluation of accuracy in the estimation of the strain rate is still insufficient. In this study, the optimal kernel sizes were determined in a phantom experiment under several SNRs and, then, the myocardial strain rate was estimated such that the myocardial function can be measured at a high frame rate. In a basic experiment, the optimal kernel sizes at depths of 20, 40, 60, and 80 mm yielded similar results: in particular, SNR was more than 15 dB. Moreover, it was found that the kernel size at the boundary must be set larger than that at the inside. The optimal sizes of the correlation kernel were seven times and four times the size of the point spread function around the boundary and inside the silicone rubber, respectively. To compare the optimal kernel sizes, which was determined in a phantom experiment, with other sizes, the radial strain rates estimated using different kernel sizes were examined using the normalized mean-squared error of the estimated strain rate from the actual one obtained by the 1D phase-sensitive method. Compared with conventional kernel sizes, this result shows the possibility of the proposed correlation kernel to enable more accurate measurement of the strain rate. In in vivo measurement, the regional instantaneous velocities and strain rates in the radial direction of the heart wall were analyzed in detail at an extremely high temporal resolution (frame rate of 860 Hz). In this study, transition in contraction and relaxation was able to be detected by 2D tracking. These results indicate the potential of this method in the high-accuracy estimation of the strain rates and detailed analyses of the physiological function of the myocardium.

BackgroundThis study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.MethodsThe current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.ResultsThe deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 17.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and 14.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about −30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}), while high precision deep learning increased the contrast (about 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}).ConclusionThis study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.

Objective. To develop a dual-energy (DE) algorithm with spatially varying weighting factors for material selection and noise suppression. Approach. Calibration step-phantoms, with overlapping slabs of solid water and bone with different thicknesses, were used to obtain the pre-calibrated material selection and noise reduction weighting factors. The Material selection weighting factors were calculated by finding a zero of contrast-to-noise-ratio (CNR) between regions with two overlapping materials and regions of only target material, while noise suppression weighting factors were determined by maximizing signal-to-noise ratio for overlapping regions. The pre-calibrated weighting factors were fitted with low and high energy radiograph of two Rando phantoms to create maps of material selection and noise suppression weighting factors, which used with DE algorithm and anti-correlated noise reduction (ACNR) algorithm to generate DE images. Three different implementations, including two different sizes of Rando phantoms and two different orientations (oblique and anterior-posterior), were investigated. Soft-tissue and bone only images of Rando phantoms were obtained with five combinations of DE algorithms and CNR, contrast, and noise values of selected regions of interest were compared to evaluate the performance of the novel method: simple log subtraction (SLS), SLS with uniform ACNR, adaptive DE (aDE), aDE with uniform ACNR, and aDE and adaptive ACNR (aACNR). Main results. Compared to SLS, the aDE algorithm demonstrated improved image quality in all three orientations. CNR increased with better contrast for both soft-tissue and bone images. Implementation of aACNR algorithm resulted in further reduction of image noise and improvements in CNR at the cost of contrast. However, aACNR algorithm showed better contrast compared to ACNR method. Significance. A novel DE algorithm was proposed, which showed improved material selection and noise suppression as compared to the conventional DE techniques and can be easily implemented in a clinical environment for real-time DE image generation.