Increasing the darkfield contrast-to-noise ratio using a deconvolution-based information retrieval algorithm in X-ray grating-based phase-contrast imaging

Lateral geniculate nucleus volumetry at 3T and 7T: Four different optimized magnetic-resonance-imaging sequences evaluated against a 7T reference acquisition

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.

Grating-based x-ray phase-contrast imaging is a fast developing new modality not only for medical imaging, but as well for other fields such as material sciences. While these many possible applications arise, the knowledge of the noise behavior is essential. In this work, the authors used a least squares fitting algorithm to calculate the noise behavior of the three quantities absorption, differential phase, and dark-field image. Further, the calculated error formula of the differential phase image was verified by measurements. Therefore, a Talbot interferometer was setup, using a microfocus x-ray tube as source and a Timepix detector for photon counting. Additionally, simulations regarding this topic were performed. It turned out that the variance of the reconstructed phase is only dependent of the total number of photons used to generate the phase image and the visibility of the experimental setup. These results could be evaluated in measurements as well as in simulations. Furthermore, the correlation between absorption and dark-field image was calculated. These results provide the understanding of the noise characteristics of grating-based phase-contrast imaging and will help to improve image quality.

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.

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.

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.

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.

Conventional X-ray absorption contrast imaging does not depict soft tissues, such as cartilage, in sufficient detail. For visualization of the soft tissues, X-ray phase-contrast imaging is more sensitive than absorption-contrast imaging. The basic concept of the X-ray phase-contrast imaging used in this study is similar to that of differential interference contrast (Nomarski) microscopy. We applied Talbot-Lau X-ray interferometry to visualize the joint structures in the right hand and knee of a donated cadaver. This imaging system simultaneously produced three different types of images: an absorption image, a differential phase image, and a visibility image. The interface between the articular cartilage of the metacarpo-phalangeal joint and fluid or the bony cortex was clearly demonstrated on the differential phase image, whereas this interface was unclear on the absorption image. Within the knee joint, the surface of the articular cartilage was demonstrated both on the differential phase and visibility images; the medial collateral ligament and medial meniscus were also visualized successfully. These results are clinically significant for the diagnosis and therapeutic estimation of rheumatoid arthritis and related joint diseases. This feasibility study on the clinical application of this imaging tool was a collaborative effort of researchers in the fields of physics, radiology, and gross anatomy.

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.

Optical coherence tomography angiography (OCTA) suffers from more motion artifacts compared to optical coherence tomography, which are the primary source of image quality degradation in in vivo imaging. We propose an OCTA algorithm in this Letter that differs from motion compensation methods widely employed to reduce motion artifacts and effectively mitigates these artifacts. The algorithm integrates differential phase and intensity images based on the standard deviation of the flow signals. The performance of this algorithm is evaluated on mouse retina in vivo imaging. Compared to three conventional OCTA algorithms, the OCTA images obtained by the proposed algorithm exhibit fewer motion artifacts and less noise in tissue regions, with maximum improvements of 1.38 dB and 0.82 in signal-to-noise ratio and contrast-to-noise ratio (CNR), respectively, in the maximum projection of en face images.

In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

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.

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.