Control of the higher eigenmodes of a microcantilever: Applications in atomic force microscopy

BackgroundTo compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).MethodsConventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used.ResultsSignificantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001 for all comparisons). Higher image quality, higher diagnostic confidence for bone and soft tissue adjacent to the prosthesis, and less severe metal artifacts were observed on DL-MAR when compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.014). CNRs were higher for DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001). Higher CNRs were observed on DL-MAR images compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.010).ConclusionsDL-MAR showed higher image quality, diagnostic confidence, and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients.Relevance statementDL-MAR resulted into improved image quality, stronger reduction of metal artifacts, and improved diagnostic confidence compared to conventional and virtual monoenergetic images with and without metal artifact reduction, bringing DL-based metal artifact reduction closer to clinical application.Key points• Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT.• A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR.• DL-MAR showed best image quality and diagnostic confidence.• Highest contrast-to-noise ratios were observed on the DL-MAR images.Graphical

To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm. 30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists. For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p < 0.05). In terms of subjective image evaluation, all ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p < 0.05), and 60% ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher. In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non-enhanced chest CT images reconstructed with 60% ASIR-V have the highest image quality. Advances in knowledge: This is the first clinical study to evaluate the clinical value of ASIR-V in the same patients using the same CT scanner in the non-enhanced chest CT scans. It suggests that ASIR-V provides the better image quality and higher diagnostic confidence in comparison with ASIR algorithm.

In this thesis, two inversion-based feedforward control approaches have been developed and implemented to high-speed large-range atomic force microscope (AFM) imaging and nanofabrication, respectively. High-speed large-range AFM imaging and nanofabrication are needed in many areas and have attracted great interests. Challenges, however, must be overcome because in high-speed largerange AFM operation, large positioning error of the AFM probe relative to the sample can be generated due to the adverse effects of the hardware. The nonlinear hysteresis and the vibrational dynamics effects of the piezotube actuator must be addressed during the high-speed lateral scanning of the AFM imaging over large imaging size. In addition, precision positioning of the AFM probe in the vertical direction is even more challenging (than the lateral scanning) because the desired trajectory (i.e., the sample topography profile) is unknown in general, and the probe positioning is also effected by and sensitive to the probe-sample interaction. Finally, in AFM multi-axis nanofabrication, additional positioning errors are induced by the large cross-axis dynamics coupling effect. The large positioning errors generated during high-speed, large-range fabrication will lead to large defects in the fabricated structures or devices. In the thesis, firstly, to compensate for the adverse effects generated during high-speed large-range AFM imaging, an integrated approach is proposed, which combines the enhanced inversion-based iterative control technique (EIIC) for lateral x-y axis scanning with a dual-stage piezoactuator for the vertical z-axis positioning. The main contribution of this integrated approach is the combination of an advanced control algorithm with an advanced hardware platform. This approach is demonstrated in experiments by imaging a large-size (50 μm) calibration sample at high speed (50 Hz scan rate). Then secondly, to achieve high-speed large-range multi-axis AFM nanofabrication, a recently developed model-less inversion-based iterative control technique (MIIC) is utilized to overcome the adverse effects involved in high-speed large-range multi-axis AFM nanofabrication. By using this advanced control technique,

Introduction CT coronary angiography (CTCA) is widely applied in coronary artery disease. It is therefore important that imaging protocols are optimised to obtain diagnostic images at the lowest possible radiation dose. We aimed to establish the feasibility of single heart-beat imaging in 320-multidetector CTCA, and assess variables that affect image quality. Methods Consecutive patients (n=249, 38% male) underwent contrast enhanced prospective electrocardiogram-gated single heart-beat CTCA. We assessed images before and after the introduction of sublingual glyceryl trinitrate to the CTCA protocol. Tube current and voltage were selected based on body-mass index (BMI) and acquisition window was selected based on heart rate (70–80% or 30–80% if the heart rate was below or above 65 beats/min respectively). Images were assessed by two trained observers and image quality was graded on a 4-point scale (1, excellent; 4, poor). Obstructive disease was defined as a stenosis of greater than 70% diameter. Results The mean heart rate was 60 beats/min (95% CI 59 to 62), BMI 29 kg/m2 (28, 30), age 58 years (56, 59) and dose-length product 283 mGy cm (266, 301). During scanning, 133(51%) received sublingual glyceryl trinitrate (GTN), 9(4%) had ectopic beats, and 12(5%) had atrial fibrillation. Diagnostic image quality was obtained in 99% with mean image quality 1.4 (1.3, 1.5). For patients with a BMI of 30 or above (n=111) diagnostic image quality was obtained in 109 scans (98%) and excellent image quality was obtained in 78 scans (70%). For patients with a heart rate above 65 bpm (n=58) diagnostic image quality was obtained in 98% (57 scans) and excellent image quality was obtained in 36% (21 scans). The mean dose-length product was 283 mGy cm (266, 301). An increase in heart rate led to an increase in radiation dose to 377 mGy cm (286, 268) for patients with a heart rate of greater than 80 beats/min (p&lt;0.001). A higher radiation dose was also associated with a BMI above 30 (DLP 231 (213, 248) vs 353 (330, 377), p&lt;0.001) and the presence of arrhythmia (DLP 275 (259, 290) vs 384 (301, 467), p 0.013). Age, sex, atrial fibrillation, ectopics, diabetes mellitus (12%) and the presence of obstructive coronary disease were not related to image quality. In multivariate regression analysis a lower heart rate and GTN were associated with improved image quality (β 0.439, p&lt;0.001 and β −0.199, p=0.001 respectively). Figure 1 The effect of heart rate, BMI and arrhythmia on the proportion of CTCA with diagnostic image quality. Conclusions Optimal image quality in single heart-beat 320-multidetector CTCA is achievable at low radiation doses in 99% of unselected patients. Image quality is further improved by lower heart rate and sublingual GTN. This study has established the use of half-segment reconstruction in all patients undergoing wide volume CTCA irrespective of heart rate, ectopic beats and the presence of atrial fibrillation. A widened window of acquisition can be used to improve image quality in patients with a heart rate greater than 65 beats per minute. In addition we have established that the use of GTN improves image quality in 320 multidetector CTCA.

Cardiovascular magnetic resonance (CMR) imaging is a powerful tool for assessing cardiac anatomy and function but remains limited by average image quality due to artefacts and long acquisition times, and complex and often too long breath-holds. Deep learning methods have recently been applied and show potential to shorten scan times by 70-80% while improving image quality, enhancing clinical efficiency. The aim of this study is to summarise the different AI-enabled methods for improving CMR image quality, including scanning time, as a key determinant for artefact reduction. A scoping review was conducted according to PRISMA guidelines. The articles were screened and reviewed by two researchers. A qualitative thematic synthesis was conducted and a CASP-mediated risk of bias assessment was performed. The eligible articles were thirty-one. These articles were thematically categorised in four subgroups, based on emerging themes: scan acceleration, artefact detection, artefact reduction, image reconstruction. A table with significant results for each theme has been presented and results were discussed qualitatively. AI demonstrated consistent improvements across the four subgroups. For scan acceleration, deep learning achieved approximately a 70-80% reduction in scan duration maintaining or even improving image quality. For artefact detection, convolutional neural networks achieved on average a 90% accuracy in detecting artefacts, across multiple metrics, indicating reliable artefact identification and strong agreement with human experts. AI models effectively reduce artefacts and enhance image quality, achieving consistently better reconstruction accuracy, sharper edges, and faster processing compared to conventional methods. Finally, for image reconstruction, generative adversarial networks enhanced structural similarity by approximately 56% (SSIM 0.591 → 0.925). Together, these results illustrate the potential of AI to optimise CMR image quality. AI can be an effective tool in addressing many of the CMR imaging challenges and thus improving image quality.

Summary This case study summarizes the data reprocessing effort that has been applied to seismic data located in the Algerian on-shore over a structurally complex, low porosity, low permeability reservoir and the improvements in final image quality that have been achieved. Subsequent to processing, the seismic data were inverted to rock properties. The final results show seismic data with significant improvements in image quality and structural definition, whilst the inversion to rock properties has enabled lithology and porosity characterization with increased confidence. Furthermore, borehole seismic surveys were acquired and integrated in the 3D volume to design the trajectory of horizontal and side-track wells.

Optoacoustic (photoacoustic) imaging has already showcased the capacity to offer high-resolution small animal visualization in vivo in a variety of cancer, cardiovascular, or neuroimaging applications. In particular, multispectral optoacoustic tomography (MSOT) has shown imaging along the spectral and the time dimensions, enabling sensing of multiple molecules over time and, more recently, in real time. Furthermore, cross-sectional imaging of at least 20 mm diameter has been showcased in vivo in animals and humans using 64-element curved transducers placed along a single curved line. Herein, we investigated the imaging improvements gained by utilizing a larger number of detectors and inquired whether more detectors will result in measurable image quality improvements. For this reason, we implemented MSOT using 64-, 128-, and 256-element transducers and imaged the same phantoms and animals under similar conditions. Further, corroborated by numerical simulation analysis, our findings quantify the improvements in resolution and overall image quality for the increasing number of detectors used pointing to significant improvements in image quality for the 256 detector array, over 64 or 128 detectors.

In this paper, the issues of controlling and improving the image quality of a high-temperature vision system are considered. The image quality improvement is needed to measure the surface properties of metals and alloys. Two levels of image quality control and improvement are defined in the system. The first level in hardware aims at adjusting the system configuration to obtain the highest contrast and weakest aura images. When optimal configuration is obtained, the second level in software is applied. In this stage, image enhancement algorithms are applied which have been developed with consideration of distortions arising from the vision system components and specificity of images acquired during the measurement process. The developed algorithms have been applied in the vision system to images. The influence on the accuracy of wetting angles and surface tension determination are considered.

PurposeTo reduce motion artifacts (MA) and noise in brain MRI using deep learning to promote clinical utility.MethodsT1-weighted (T1W), T2-weighted (T2W), and fluid attenuated inversion recovery (FLAIR) images of the brain (including sagittal, coronal, and axial sections) of 20 healthy volunteers were collected using a 3.0T MR system. Simulated images with horizontal and vertical phase directions exhibiting varying white noise and MA (n = 115200) were created for each sequence and trained in deep learning (36000 pairs), validation (200 pairs), and testing (200 pairs, 2000 pairs) datasets. Images with MA and noise and images without MA and noise were included. A training model was constructed to remove noise and MA. The model’s ability to remove noise and MA was evaluated by the structural similarity index (SSIM) and peek signal to noise ratio (PSNR). The SSIM and PSNR between the ground-truth and output images were calculated (SSIMout, PSNRout), and the SSIM and PSNR between the ground-truth and input images were calculated (SSIMinp, PSNRinp). The ratio of SSIMinp to SSIMout was then evaluated as the improvement ratio of SSIM (IMPRs) and the ratio of PSNRinp to PSNRout as the improvement ratio of PSNR (IMPRp). In addition, 10 radio technologists performed visual evaluation.ResultsThe SSIMout were >0.95 and 33 dB, respectively, for T1W, T2W, and FLAIR images with different contrasts. The mean value of SSIMinp was 0.72. Noise and MA removal effects were observed in images, with an average value of 72 dB. Visual evaluation revealed that the reduction effect in the output image was higher than that observed in the input image.ConclusionThe method proposed herein, which uses separate training models for the T1W, T2W, and FLAIR sequences, is a valuable approach for removing MA and noise, independent of the imaging direction and artifact orientation. An improvement in image quality was achieved by generating a large amount of training data through simulation.

The potential for using electron paramagnetic resonance (EPR) imaging in biological applications has been limited by the lack of ideal single-line imaging probes. The commonly used nitroxides exhibit multiple lines, causing either hyperfine-based limitations in the maximum obtainable image resolution or hyperfine-based artifacts in the reconstructed image. The application of a numerical method, based on forward-subtraction principles for removing hyperfine artifacts in the measured projections is reported. It is demonstrated by using computer simulations, imaging of phantoms, and imaging of rat hearts, that marked enhancement in image quality and resolution can be obtained by removing the hyperfine-imposed limit on the gradient magnitude and performing postacquisition corrections for removing hyperfine artifacts in the image.

Time-of-flight technology (TOF) is at the leading edge of advancements in PET/CT imaging, and is made possible due to the availability of new scanner design, scintillating materials and fast computers. The location of annihilation is estimated according to the time difference between scintillation events produced by two 511KeV gamma photons travelling in opposite directions. The additional information is incorporated into image reconstruction to suppress noise propagation and increase signal-to-noise ratio. As shown in previous studies, these properties can be translated in to clinical usage to obtain improved image quality using the same acquisition time, while also enhancing image quality under shortened acquisition or reduce tracer dosage injected to patients.\n\nSemi-quantification units such as standardized uptake value (SUV) and standard deviation (SD) are measured on static PET images and are widely used clinically to quantify the rate of cell metabolism. Signal-to-noise ratio (SNR) and coefficient of variation (CV) can be calculated to assess image quality. However, these parameters depend on image reconstruction and are therefore expected to change with the use of TOF.\n\nBreath holding (BH) PET/CT imaging protocol has been suggested to help evaluation of lesions affected by respiratory motion. However, the PET image quality is significantly degraded since the acquisition time is shortened (e.g. 30 seconds). Noise on BH PET image may cause inaccuracy in semi-quantitation and difficulties in visual assessment. TOF can be applied to improve the image quality and enhance subtle lesions, thus helping the characterization of small lung nodules (SLN) with potential clinical impact.\n\nThis thesis aims to evaluate the impact of TOF on PET image quality and the quantification of SUV in clinical practice. Firstly, the performance of a TOF PET/CT scanner is tested using a phantom mimicking human body trunk with ‘lesions’. Using the same acquisition time (2 minutes/bed), TOF images outperformed conventional images in terms of lesion detectability and background uniformity, especially for small spheres and under low lesion-to-background ratio. Moreover, TOF images acquired using a shorter scan time (1.5 minutes/bed) also maintained acceptable image quality. In the second study, the influence on SUV measurement and image quality (in terms of CV) are evaluated in twelve normal organ structures on whole-body FDG PET/CT images. TOF significantly decreased SUVmax (9/12) and SUVmean (8/12) among the twelve normal organ structures investigated. It also improved the image quality, particularly in solid organs in the abdomen. The combined utility of TOF and BH PET/CT imaging protocol for the detection of SLN is demonstrated in the third study. Lesions showing PET activity, which were misaligned with anatomical location on CT, had higher SUVmax and comparable SNR on BH images. PET/CT fusion was also markedly improved. The additional use of TOF further enhanced lesion detection and improved SNR.\n\nThe results demonstrate the benefits of using TOF in clinical PET/CT, including its advantage with breath holding PET/CT imaging. This information not only offers a better understanding of this increasingly popular technology in modern PET/CT scanners, but also highlights its potential applications in clinical practice.

Prospective motion correction using data from optical tracking systems has been previously shown to reduce motion artifacts in MR imaging of the head. We evaluate a novel optical embedded tracking system. The home-built optical embedded tracking system performs image processing within a 7 T scanner bore, enabling high speed tracking. Corrected and uncorrected in vivo MR volumes are acquired interleaved using a modified 3D FLASH sequence, and their image quality is assessed and compared. The latency between motion and correction of the slice position was measured to be (19 ± 5) ms, and the tracking noise has a standard deviation no greater than 10 μm/0.005° during conventional MR scanning. Prospective motion correction improved the edge strength by 16 % on average, even though the volunteers were asked to remain motionless during the acquisitions. Using a novel method for validating the effectiveness of in vivo prospective motion correction, we have demonstrated that prospective motion correction using motion data from the embedded tracking system considerably improved image quality.

A subjective and objective comparison of tissue contrast and imaging artifacts present in routine spin echoes and in iterative decomposition of asymmetric spin echoes for soft tissue neck MRI

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m2 were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

The purpose of this study was to investigate the efficacy of a retrospective adaptive motion correction technique known as autocorrection for reducing motion-induced artifacts in high-resolution three-dimensional time-of-flight MR angiography of the circle of Willis. Ten consecutive volunteers were imaged with an unenhanced gradient-recalled echo three-dimensional time-of-flight MR angiography sequence of the circle of Willis. Each volunteer was asked to rotate approximately 2 degrees after completion of one third and one half of the acquisition in the axial, sagittal, and oblique planes (45 degrees to the axial and sagittal planes). A single static data set was also acquired for each volunteer. Unprocessed and autocorrected maximum-intensity-projection images were reviewed as blinded image pairs by six radiologists and were compared on a five-point image quality scale. Mean improvement in image quality after autocorrection was 1.4 (p < 0.0001), 1.1 (p < 0.0001), and 0.2 (p = 0.003) observer points (maximum value, 2.0), respectively, for examinations corrupted by motion in the axial, oblique, and sagittal planes. All three axes had statistically significant improvement in image quality compared with the uncorrected images. Changes in image quality after the application of the autocorrection algorithm to static angiogram data were not statistically significant (mean change in score = -0.13 points; p = 0.29). Autocorrection can reduce artifacts in motion-corrupted MR angiography of the circle of Willis without distorting motion-free examinations.