A Spatial Compounding Method for Non-Delayed Sequential Beamforming

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.

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.

Pulse-inversion-based tissue harmonic imaging has been utilized for many years because it can effectively eliminate the harmonic leakage and produce low side-lobe. However, the pulse inversion method is sensitive to imaging object movements, which may result in motion artifacts. Spatial resolution and contrast were limited. To improve ultrasound image quality by a new pulse-inversion-based tissue harmonic imaging technique. Continuous wavelet transform is applied to investigate the correlation between mother wavelet and the received echoes from two opposite pulses. To get a better correlation, a novel mother wavelet named 'tissue wavelet' is designed based on the Khokhlov-Zabolotskaya- Kuznetsov (KZK) wave equation. Radio frequency data were obtained from open Ultrasonix SonixTouch imaging system. Experiments were carried on ultrasonic tissue phantom, human carotid artery and human liver. The average improvement of lateral spatial resolution is 49.52% compared to pulse-inversion-based tissue second-harmonic Imaging (PIHI). Contrast ratio (CR) and contrast-to-noise ratio (CNR) increased by 5.55 dB and 1.40 dB over PIHI. Tissue wavelet performs better than Mexh and Morl wavelet in lateral spatial resolution, CR, and CNR. The proposed technique effectively improves the imaging quality in lateral spatial resolution, CR, and CNR.

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.

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.

PurposeTo evaluate 18F-FDG myocardial metabolism imaging (MMI) using a total-body PET/CT scanner and explore the feasible scan duration to guide the clinical practice.MethodsA retrospective analysis was conducted on 41 patients who underwent myocardial perfusion-metabolism imaging to assess myocardial viability. The patients underwent 18F-FDG MMI with a total-body PET/CT scanner using a list-mode for 600 s. PET data were trimmed and reconstructed to simulate images of 600-s, 300-s, 120-s, 60-s, and 30-s acquisition time (G600-G30). Images among different groups were subjectively evaluated using a 5-point Likert scale. Semi-quantitative evaluation was performed using standardized uptake value (SUV), myocardial to background activity ratio (M/B), signal to noise ratio (SNR), contrast to noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV). Myocardial viability analysis included indexes of Mismatch and Scar. G600 served as the reference.ResultsSubjective visual evaluation indicated a decline in the scores of image quality with shortening scan duration. All the G600, G300, and G120 images were clinically acceptable (score ≥ 3), and their image quality scores were 4.9 ± 0.3, 4.8 ± 0.4, and 4.5 ± 0.8, respectively (P > 0.05). Moreover, as the scan duration reduced, the semi-quantitative parameters M/B, SNR, CNR, and CR decreased, while SUV and CV increased, and significant difference was observed in G300-G30 groups when comparing to G600 group (P < 0.05). For myocardial viability analysis of left ventricular and coronary segments, the Mismatch and Scar values of G300-G30 groups were almost identical to G600 group (ICC: 0.968-1.0, P < 0.001).ConclusionSufficient image quality for clinical diagnosis could be achieved at G120 for MMI using a total-body PET/CT scanner, while the image quality of G30 was acceptable for myocardial viability analysis.

To compare the image quality and acquisition time of thoracic great vessels using a novel, 3D isotropic non-contrast-enhanced MR angiography that combines respiratory compensation, electrocardiogram-triggering, compressed sensing, and Dixon water-fat separation at 3T (CS-MRA) to a conventional MRA employing parallel imaging and spectral attenuated inversion recovery (cMRA). This study included 20 healthy volunteers and 19 patients with thoracic vascular diseases. All scans were performed on a 3T MRI scanner using a T2-prepared 3D fast low angle shot (FLASH) sequence. Visual image quality of thoracic great vessels was assessed using a 4-point scale. Quantitative parameters including SNR, contrast-to-noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV) were evaluated. Aortic diameters were measured, and reproducibility was assessed using Bland-Altman analysis and the intraclass correlation coefficient (ICC). Image acquisition times were also compared. Visual assessment demonstrated significantly higher scores with CS-MRA compared to cMRA across all evaluated thoracic vessels in both volunteers and patients (median score: 3 vs. 2, P < 0.05). Quantitative analysis showed that CS-MRA yielded significantly higher SNR, CNR, and CR (P < 0.05 for all), along with better signal homogeneity (lower CV). CS-MRA showed excellent intra- and inter-observer agreement for aortic diameter measurements, with ICCs ≥ 0.91 in volunteers and ≥ 0.95 in patients. CS-MRA also achieved significantly shorter acquisition times (volunteers: 5.3 ± 1.4 min vs. 6.0 ± 1.7 min, P < 0.01; patients: 5.1 [4.6-6.3] min vs. 6.9 [5.0-9.6] min, P < 0.01). CS-MRA provides superior image quality, comparable measurement reproducibility, and significantly reduced acquisition times compared to cMRA. These findings support the clinical utility of CS-MRA as an efficient and reliable non-contrast technique for thoracic vascular evaluation.

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.

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.

INTRODUCTION: At most institutions, the favored contrast MR sequence is T1-weighted imaging (T1WI). However, lesion enhancement is occasionally inconspicuous on T1WI.Objective: This study aims to evaluate the diagnostic value of contrast-enhanced FLAIR compared to contrast-enhanced T1WI for intracranial tumors and to offer data for further clinical judgment. METHODS: 88 consecutive cases of intracranial tumors referred for contrast-enhanced brain MRI were analyzed. FLAIR and T1 were used alternately in equal percentages as the first contrast-enhanced sequence to avoid delayed contrast-enhancement effects of the lesions. Six quantitative criteria were considered: lesion-to-white matter contrast ratio (CR) and contrast-to-noise ratio (CNR), lesion-to-gray matter CR and CNR, and lesion-to-cerebrospinal fluid CR and CNR. For qualitative evaluation, two experienced radiologists assessed lesion conspicuity on contrast-enhanced-T1WI and FLAIR sequences using the following three scales: 1, FLAIR superior; 2, sequences equal; 3, T1 superior. RESULTS: For quantitative measurement, the contrast enhanced-FLAIR lesion-to-white matter, lesion-to-cerebrospinal fluid CR, and CNR values were statistically superior to those of the contrast enhanced-T1 weighted images (p = 0.001 in all). However, lesion-to-gray matter CR and CNR were slightly higher on CE-FLAIR, but with no statistically significant difference (p = 0.159, 0.184, respectively). For qualitative evaluation, both radiologists assessed that contrast enhanced-FLAIR images were superior to contrast enhanced-T1 weighted images for the evaluation of lesion conspicuity, especially when it was performed as the second sequence. CONCLUSION: FLAIR sequence was superior or comparable to T1 sequence, especially when performed as a second post-contrast sequence. Using contrast enhanced-FLAIR as a routine MRI sequence will increase diagnostic confidence.

In mammography, thick or dense breast regions persistently suffer from reduced contrast-to-noise ratio (CNR) because of degraded contrast from large scatter intensities and relatively high noise. Area x-ray beam equalization can improve image quality by increasing the x-ray exposure to under-penetrated regions without increasing the exposure to other breast regions. Optimal equalization parameters with respect to image quality and patient dose were determined through computer simulations and validated with experimental observations on a step phantom and an anthropomorphic breast phantom. Three parameters important in equalization digital mammography were considered: attenuator material (Z = 13–92), beam energy (22–34 kVp) and equalization level. A Mo/Mo digital mammography system was used for image acquisition. A prototype 16 × 16 piston driven equalization system was used for preparing patient-specific equalization masks. Simulation studies showed that a molybdenum attenuator and an equalization level of 20 were optimal for improving contrast, CNR and figure of merit (FOM = CNR2/dose). Experimental measurements using these parameters showed significant improvements in contrast, CNR and FOM. Moreover, equalized images of a breast phantom showed improved image quality. These results indicate that area beam equalization can improve image quality in digital mammography.

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.

Objective To assess image quality and optimal keV levels of spectral CT virtual monochromatic series in atypical vertebral osteoblastic metastases of lung cancer. Methods Patients with pathologically proven lung cancer who had undergone spectral CT (Discovery CT750 HD, GE) of the chest or abdomen more than twice were analyzed retrospectively. Vertebral osteoblastic metastases were confirmed by follow-up CT examinations, biopsy ,emission CT, PET/CT. The bone lesions were missed due to atypicalappearance or presented with suspicious density at the first exmaination were chosen. A total of twenty two patients with 72 lesions were enrolled. Conventional polychromatic images (140 kVp) and monochromatic images were generated (40 to 140 keV at an interval of 10 keV). The signal to noise ratio(SNR) and contrast to noise ratio (CNR) of the lesions were measured and calculated at all the 12 series images by two radiologists. The two radiologists scored the different series of images independently. Paired sample t test was used to compare SNR and CNR among 40 to 140 keV and 140 kVp images. The inter-observer agreement between the two radiologists was evaluated using Kappa statistics and the difference of different images intrarobserver was compared using Wilcoxon signed rank test. Results The SNR of 140 kVp was 48.03±20.17, and those of 50, 60 70 keV images were 58.52±22.92, 64.07±24.88, 59.54±22.04. SNR of 50, 60, 70 keV images was higher than that of 140 kVp (t values were 13.366,16.278, 18.889,P<0.01), 60 keV images had the highest SNR. The CNR of 140 kVp was 7.64±4.52 and those of 50, 60, 70 and 80 keV images were 7.95±4.99, 8.12±5.08, 8.16±5.05, 7.89±4.81. CNR of 50, 60, 70 and 80 keV images were higher than that of 140 kVp image (t values were 3.519, 5.050, 6.231, 4.413, P<0.01), 60 keV and 70 keV images had the higher CNR than 50 keV and 80 keV. Good interobserver agreement was found for the evaluation of image quality (Kappa =0.864,P<0.05).Moreover, the scores of both the two observes for 60 keV images were higher than 140 kVp images and higher than other monochromatic images. Conclusion Atypical vertebral osteoblastic metastases of lung cancer are more conspicuous in virtual monochromatic images as compared wtih 140 kVp polychromatic images and the optimal energy level was 60 keV, repectively. Key words: Lung neoplasms; Bone neoplasms; Neoplasm metastasis; Tomography, X-ray computed

Synthetic transmit aperture (STA) has been widely investigated in ultrasound system recently with characteristics of high frame rate and low hardware cost. Since the high-resolution image (HRI) of STA is formed by summation of low-resolution images (LRIs), it is susceptible to inter-firing motions. In this paper, we propose a low-complexity global motion compensation algorithm. We use the common region of interest (ROIcom) between different transmissions of STA imaging to beamform backward and forward beam vectors. Then, the magnitude and direction of motion can be evaluated by cross-correlations between specific beam vectors in STA imaging. Compared with the uncompensated image in two-dimentional (2D) motion environment, the proposed motion compensation algorithm can improve the contrast ratio (CR) and contrast-to-noise ratio (CNR) by 13.73 and 2.04 dB, respectively. Also, the proposed algorithm improves the CR and CNR about 7.84 and 1.36 dB comparing with the reference work, respectively. In the Field II breath model, the proposed method also improves the CR and CNR about 6.65 and 1.04 dB than the reference method, respectively. Moreover, we propose a low-complexity delay generator in the architecture design to further reduce the computational complexity of the whole beamforming system. Finally, we verify the proposed low-complexity motion compensation beamforming engine by using the VLSI implementation with CMOS 90 nm technology. In the post-layout result, the core size is 2.39 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at 125 MHz operating frequency and the frame rate of the beamforming system is 42.23 frames per second.

Introduction:Mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) was shown to have a transient reduction in diffusion. Such changes would be used as an early detection to reduce excessive treatments and promote recovery without sequelae. The current research evaluated the high b-value (b = 3000 s/mm2) diffusion-weighted imaging (DWI) assessment in MERS.Methods:Sixteen pediatric patients showed MERS used DWI (b = 1000 and 3000 s/mm2). To record number of lesions, the signal intensities, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), contrast ratios (CRs), the apparent diffusion coefficients (ADCs) were measured in the normal parenchyma and lesions.Results:Lesions were more apparent with high b-value. The ADC values and CNR in the lesions and surrounding normal brain parenchyma were relatively low at a high compared to standard b-value DWI (SNR: 144.67 ± 33.03, 85.72 ± 31.50; CNR: 20.82 ± 17.64, 49.62 ± 33.06; for b = 1000 and 3000 s/mm2). The CR was significantly higher at a high compared to low b-value DWI (CR: 0.06 ± 0.07 versus 0.40 ± 0.14).Conclusion:High b-value DWI could detect more lesions and could obviously improve the detection of lesions in pediatric patients with MERS.