Reduce Image Acquisition Time Research Articles

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

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.

Quantifying DNA damage following light sheet and confocal imaging of the mammalian embryo

Embryo quality assessment by optical imaging is increasing in popularity. Among available optical techniques, light sheet microscopy has emerged as a superior alternative to confocal microscopy due to its geometry, enabling faster image acquisition with reduced photodamage to the sample. However, previous assessments of photodamage induced by imaging may have failed to measure more subtle impacts. In this study, we employed DNA damage as a sensitive indicator of photodamage. We use light sheet microscopy with excitation at a wavelength of 405 nm for imaging embryo autofluorescence and compare its performance to laser scanning confocal microscopy. At an equivalent signal-to-noise ratio for images acquired with both modalities, light sheet microscopy reduced image acquisition time by ten-fold, and did not induce DNA damage when compared to non-imaged embryos. In contrast, imaging with confocal microscopy led to significantly higher levels of DNA damage within embryos and had a higher photobleaching rate. Light sheet imaging is also capable of inducing DNA damage within the embryo but requires multiple cycles of volumetric imaging. Collectively, this study confirms that light sheet microscopy is faster and safer than confocal microscopy for imaging live embryos, indicating its potential as a label-free diagnostic for embryo quality.

Enhanced denoising for weak signal preservation in structured illumination microscopy

Structured illumination microscopy (SIM) is a powerful super-resolution technology in biological science because of its fast imaging speed, low phototoxicity, and full-field imaging. Despite this, SIM is hampered by out-of-focus background noise, which can obscure weak fluorescence signals and render them unrecognizable. Previous denoising algorithms tended to eliminate the noise along with the weak signals, causing a decrease in image quality. To address this issue, we propose a denoising algorithm based on out-of-focus plane information extraction (OPIE-SIM) that salvages the weak signal from the out-of-focus background noise. The OPIE-SIM algorithm enhances weak fluorescence signals by combining out-of-focus layer information with focal plane data and correcting the differences in point spread functions (PSF). This approach eliminates out-of-focus background noise and preserves the integrity of weak fluorescence structures while significantly reducing image acquisition time compared to traditional over-focusing imaging techniques. Through extensive simulations and experiments, we verified the feasibility of our approach. Compared with other denoising algorithms, our method generates images with a higher signal-to-noise ratio while maintaining the integrity of weak fluorescence structures.

Research on deep learning restoration algorithm of X-ray backscatter imaging based on virtual training dataset

The X-ray lobster eye lens, an innovative technique for focusing high-energy radiation, enables wide-field X-ray imaging. However, its inherent cross point spread function introduces noise and degradation into the resultant images. Conventional image restoration methods are inadequate for suppressing such noise. This paper introduces a backscatter image restoration technique utilizing a virtual training dataset. By convolving the point spread function (PSF) with an object to simulate the image degradation process, the method generates a multitude of convolved images for deep learning training, eliminating the need for manual annotation. Given the high structural similarity between the synthetic convolved images and actual backscatter images, the trained model effectively restores real backscatter images. The restoration process yields a structural similarity index (SSIM) of 0.86 and a mean intersection over union (MIoU) of 0.83 when compared to the reference images. This approach mitigates the limitations of sparse real backscatter datasets, substantially reducing image acquisition time, decreasing radiation flux, and enhancing system safety.

Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T – Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction

ObjectivesTo evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time. Materials and methodsLumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences. ResultsThe images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min. ConclusionsAdvanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times. Clinical relevanceAdvanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.

Designing Clinical MRI for Enhanced Workflow and Value.

MRI is an expensive and traditionally time-intensive modality in imaging. With the paradigm shift toward value-based healthcare, radiology departments must examine the entire MRI process cycle to identify opportunities to optimize efficiency and enhance value for patients. Digital tools such as "frictionless scheduling" prioritize patient preference and convenience, thereby delivering patient-centered care. Recent advances in conventional and deep learning-based accelerated image reconstruction methods have reduced image acquisition time to such a degree that so-called nongradient time now constitutes a major percentage of total room time. For this reason, architectural design strategies that reconfigure patient preparation processes and decrease the turnaround time between scans can substantially impact overall throughput while also improving patient comfort and privacy. Real-time informatics tools that provide an enterprise-wide overview of MRI workflow and Picture Archiving and Communication System (PACS)-integrated instant messaging can complement these efforts by offering transparent, situational data and facilitating communication between radiology team members. Finally, long-term investment in training, recruiting, and retaining a highly skilled technologist workforce is essential for building a pipeline and team of technologists committed to excellence. Here, we highlight various opportunities for optimizing MRI workflow and enhancing value by offering many of our own on-the-ground experiences and conclude by anticipating some of the future directions for process improvement and innovation in clinical MR imaging. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.

Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint

To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio. The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method. The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters. The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.

High-throughput calculation of organ-scale traits with reconstructed accurate 3D canopy structures using a UAV RGB camera with an advanced cross-circling oblique route

The measurement of organ-scale traits in large-scale fields remains a bottleneck in genotype-phenotype association studies for crop improvement. To address this issue, an advanced cross-circling oblique (CCO) route was proposed, consisting of multiple single-circle routes. Using multi-view images from lightweight UAVs with the CCO route, 3D crop canopies were reconstructed for maize, cotton, and sugar beet. Organ-scale traits were estimated from the CCO-derived and traditional five-directional oblique (FDO)-derived 3D canopy models and were compared to manual measurements. An algorithm was further proposed to compare the image utilisation efficiency between the CCO route and FDO route with three indicators, including the total utilisation (TU), effective utilisation (EU), and occlusion rate (OR). The results showed that the proposed CCO route can obtain complete canopy structures throughout the growth stages for crops with relatively wide-row planting, such as maize. For densely planted crops like cotton, the full canopy structure at early growth stages (47 d after sowing (DAS)) and the upper parts of the canopy architecture at later growth stages (71 and 90 DAS) could be obtained. The leaf length and width from different layers estimated from CCO-derived models were in good agreement with manual measurements for maize (R2 = 0.93, RMSE = 3.05 cm, nRMSE = 4.7% and R2 = 0.88, RMSE = 0.49 cm, nRMSE = 5.5% for leaf length and leaf width, respectively) and cotton (R2 = 0.81, RMSE = 1.16 cm, nRMSE = 8.6% and R2 = 0.93, RMSE = 0.99 cm, nRMSE = 7.2% for leaf length and leaf width, respectively). CCO-derived 3D canopy models also provided higher accuracy for organ-scale trait estimation than FDO-derived 3D canopy models (R2 of 0.80 versus 0.76 for both leaf length and width estimation, respectively). Regarding image utilisation, the CCO route outperformed the FDO route, with an average value of 124% higher in EU and 11.7% lower in OR, tested on sugar beet varieties with different canopy structures. The proposed CCO route adopts a non-linear mobile route to obtain canopy images from more perspectives, taking into account both the model accuracy and efficiency. Although the flight altitude of the CCO in this study was relatively low (4 m above the canopy), the same model accuracy can be achieved at higher flight altitudes using a higher-quality camera, thus significantly reducing image acquisition time. This study represents the first time that accurate crop structures in large-scale fields were characterised using a method featuring mobility, affordability, throughput, accuracy, and efficiency. The proposed method could provide novel opportunities for accelerating plant breeding and precision agriculture.

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging.

.SignificanceHyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space.AimWSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics.ApproachHigh-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection.ResultsOur super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results.ConclusionsThe proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

Face recognition in the visible and Near Infrared range has received a lot of attention in recent years. The current Multispectral (MS) imaging systems used for facial recognition are based on multiple cameras having multiple sensors. These acquisition systems are normally slow because they take one MS image in several shots, which makes them unable to acquire images in real time and to capture moving scenes. On the other hand, currently there are snapshot multispectral imaging systems which integrate a single sensor with Multispectral Filter Arrays (MSFA) allow having at each acquisition an image on several spectra. These systems drastically reduce image acquisition time and are able to capture moving scenes in real time. This paper proposes a study of robust facial recognition using Multispectral Filter Array acquisition system. For this goal, a MSFA one-shot camera was used to collect the images and a robust facial recognition method based on Fast Discrete Curvelet Transform and Convolutional Neural Network is proposed. This camera covers the spectral range from 650 nm to 950 nm. A comparison of the facial recognition system using Multispectral Filter Arrays camera is made with those that using multiple cameras. Experimental results proved that face recognition systems whose acquisition systems are designed using MSFA perform more efficiently with an accuracy of 100%.

Image quality and acquisition time assessments for phase oversampling in compressed sensing sensitivity encoding: Comparison with conventional SENSE

This study compared sensitivity encoding (SENSE) and compressed sensing sensitivity encoding (CS‐SENSE) for phase oversampling distance and assessed its impact on image quality and image acquisition time. The experiment was performed with a large diameter phantom using 16‐channel anterior body coils. All imaging data were divided into three groups according to the parallel imaging technique and oversampling distances: groups A (SENSE with phase oversampling distance of 150 mm), B (CS‐SENSE with phase oversampling distance of 100 mm), and C (CS‐SENSE with phase oversampling distance of 75 mm). No statistically significant differences were observed among groups A, B, and C regarding both T2 and T1 turbo spin‐echo (TSE) sequences using an acceleration factor (AF) of 2 (p = 0.301 and 0.289, respectively). In comparison with AF 2 of group A, the scan time of AF 2 of groups B and C was reduced by 11.2% and 23.5% (T2 TSE) and 15.8% and 22.7% (T1 TSE), respectively, while providing comparable image quality. Significant image noise and aliasing artifact were more evident at AF ≥ 2 in group A compared with groups B and C. CS‐SENSE with a less phase oversampling distance can reduce image acquisition time without image quality degradation compared with that of SENSE, despite the increase in aliasing artifact as the AF increased in both CS‐SENSE and SENSE.

Implementation and prospective clinical validation of AI‐based planning and shimming techniques in cardiac MRI

PurposeCardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce image acquisition time without compromising image quality (IQ), and improve magnetic field uniformity. Here, we aim to implement two AI‐based deep learning techniques for automatic slice alignment and cardiac shimming and evaluate their performance in clinical cardiac magnetic resonance imaging (MRI).MethodsTwo deep neural networks were developed, trained, and validated on pre‐acquired cardiac MRI datasets (>500 subjects) to achieve automatic slice planning and shimming (implemented in the scanner) for CMR. To examine the performance of our automated cardiac planning (EasyScan) and AI‐based shim (AI shim), two prospective studies were performed subsequently. For the EasyScan validation, 10 healthy subjects underwent two identical CMR protocols: with manual cardiac planning and with AI‐based EasyScan to assess protocol scan time difference and accuracy of cardiac plane prescriptions on a 1.5 T clinical MRI scanner. For the AI shim validation, a total of 20 subjects were recruited: 10 healthy and 10 cardio‐oncology patients with referrals for a CMR examination. Cine images were obtained with standard cardiac volume shim and with AI shim to assess signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), overall IQ (sharpness and MR image degradation), ejection fraction (EF), and absolute wall thickening. A hybrid statistical method using of nonparametric (Wilcoxon) and parametric (t‐test) assessments was employed for statistical analyses.ResultsCMR protocol with AI‐based plane prescriptions, EasyScan, minimized operator dependence and reduced overall scanning time by over 2 min (∼13 % faster, p < 0.001) compared to the protocol with manual cardiac planning. EasyScan plane prescriptions also demonstrated more accurate (less plane angulation errors from planes manually prescribed by a certified cardiac MRI technologist) cardiac planes than previously reported strategies. Additionally, AI shim resulted in improved B0 field homogeneity. Cine images obtained with AI shim revealed a significantly higher SNR (12.49%; p = 0.002) than those obtained with volume shim (volume shim: 32.90 ± 7.42 vs. AI shim: 37.01 ± 8.87) for the left ventricle (LV) myocardium. LV myocardium CNR was 12.48% higher for cine imaging with AI shim (149.02 ± 39.15) than volume shim (132.49 ± 33.94). Images obtained with AI shim resulted in sharper images than those obtained with volume shim (p = 0.012). The LVEF and absolute wall thickening also showed that differences exist between the two shimming methods. The LVEF by AI shim was shown to be slightly larger than LVEF by volume shim in two groups: 2.87% higher with AI shim for the healthy group and 1.70% higher with AI shim for the patient group. The LV absolute wall thickening (in mm) also showed that differences exist between shimming methods for each group with larger changes observed in the patient group (healthy: 3.31%, p = 0.234 and patient group: 7.29%, p = 0.059).ConclusionsCMR exams using EasyScan for cardiac planning demonstrated accelerated cardiac exam compared to the CMR protocol with manual cardiac planning. Improved and more uniform B0 magnetic field homogeneity also achieved using AI shim technique compared to volume shimming.

Super-Resolution Cine Image Enhancement for Fetal Cardiac Magnetic Resonance Imaging.

Fetal cardiac magnetic resonance imaging (MRI) improves the diagnosis of congenital heart defects, but is sensitive to fetal motion due to long image acquisition time. This may be overcome with faster image acquisition with low resolution, followed by image enhancement to provide clinically useful images. To combine phase-encoding undersampling with super-resolution neural networks to achieve high-resolution fetal cine cardiac MR images with short acquisition time. Prospective. Twenty-eight fetuses (gestational week 36 [interquartile range 33-38 weeks]). 1.5 T, balanced steady-state free precession (bSSFP) cine sequence. Images were acquired using fully sampled Doppler ultrasound-gated clinical bSSFP cine as reference, with equivalent cine sequences with decreased phase-encoding resolution (25%, 33%, and 50% of clinical standard). Two super-resolution methods based on convolutional neural networks were proposed and evaluated (phasrGAN and phasrresnet). Data were partitioned into training (36 cine slices), validation (3 cine slices), and test sets (67 cine slices) without overlap. Conventional reconstruction methods using bicubic interpolation and k-space zeropadding were used for comparison. Three blinded observers scored image quality between 1 and 10. Image scores are reported as median [interquartile range] and were compared using Mann-Whitney's nonparametric test with P < 0.05 showing statistically significant differences. Both proposed methods showed no significant difference in image quality compared to clinical images (8 [7-8.5]) down to 33% (phasrGAN 8 [6.5-8]; phasrresnet 8 [7-8], all P ≥ 0.19) phase-encoding resolution, i.e., up to three times faster image acquisition, whereas bicubic interpolation and k-space zeropadding showed significantly lower quality for 33% phase-encoding resolution (both 7 [6-8]). Super-resolution enhancement can be used for fetal cine cardiac MRI to reduce image acquisition time while maintaining image quality. This may lead to an improved success rate for fetal cine MR imaging, as the impact of fetal motion is lessened by shortened acquisitions. 1 TECHNICAL EFFICACY: Stage 2.

Feasibility study of simultaneous multislice diffusion kurtosis imaging with different acceleration factors in the liver

BackgroundSimultaneous multislice diffusion-weighted imaging (SMS-DWI) has been used to reduce image acquisition time. The purpose of this study was to investigate the feasibility of diffusion kurtosis imaging (DKI) based on the SMS technique in the liver and the influence of this method compared with that of conventional DWI sequences on image quality and DKI-derived quantitative parameters.MethodsForty volunteers underwent SMS-DWI sequences with acceleration factors of 2 and 3 (SMS2-DWI, SMS3-DWI) and conventional DWI (C-DWI) of the liver with three b-values (50, 800, 2000 s/mm2) in a 3T system. Qualitative image quality parameters and quantitative measurements of the signal-to-noise ratio (SNR), mean kurtosis (MK), mean apparent diffusivity (MD) and apparent diffusion coefficient (ADC) for the liver were compared between the three sequences.ResultsThe scan times of C-DWI, SMS2-DWI, and SMS3-DWI were 4 min 11 s, 2 min 2 s, and 1 min 34 s, respectively. For all image quality parameters, there were no significant differences observed between C-DWI and SMS2-DWI (all p > 0.05) in the images with b-values of 800 and 2000 s/mm2. C-DWI and SMS2-DWI exhibited better scores than SMS3-DWI (all p < 0.01) in the images with b-values of 2000 s/mm2. In the images with b-values of 800 s/mm2, C-DWI and SMS2-DWI exhibited better scores than SMS3-DWI for artefacts and overall image quality (all p < 0.01), and C-DWI exhibited better scores than SMS3-DWI for the visibility of intrahepatic vessels (p < 0.001). There were no significant differences in the sharpness of the right lobe edge (p = 0.144), conspicuity of the left lobe (p = 0.370) or visibility of intrahepatic vessels (p = 0.109) between SMS2-DWI and SMS3-DWI. There were no significant differences in the sharpness of the right lobe edge (p = 0.066) or conspicuity of the left lobe (p = 0.131) between C-DWI and SMS3-DWI. For the b-value of 800 s/mm2, there were no statistically significant differences between SMS2-DWI and C-DWI (p = 1.000) or between SMS2-DWI and SMS3-DWI (p = 0.059), whereas SMS3-DWI had a significantly lower SNR than C-DWI (p = 0.024). For the DKI-derived parameters (MK and MD) and ADC values, there were no significant differences between the three sequences (MK, p = 0.606; MD, p = 0.831; ADC, p = 0.264).ConclusionsSMS-DWI with an acceleration factor of 2 is feasible for the liver, resulting in considerable reductions in scan time while maintaining similar image quality, comparable DKI parameters and ADC values compared with those of C-DWI.

Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction

Iterative image reconstruction methods are widely used in PET due to their better image quality when compared to analytical methods. However, inaccurate quantification occurs in low activity concentration regions, which leads to biased quantification of PET images. The diagnosis of some neurodegenerative diseases, such as Alzheimer’s disease, is based on identifying such low-uptake regions. Furthermore, PET imaging in these populations should be as short as possible to limit head movements and improve patient comfort. This work aims to identify optimized reconstruction parameters of [18F]FDG PET brain images aiming to reduce image acquisition time with minimal impact on quantification. For this, [18F]FDG PET images of a Hoffman 3-D brain phantom were acquired. Analytical and iterative reconstruction methods were compared utilizing image quality and quantitative accuracy metrics. OSEM reconstruction algorithm was optimized (4 iterations and 32 subsets). It resulted in remarkably similar images compared to the current clinical settings, with a 50% reduction in scan time (5 min with a post-reconstruction filter of 4 mm). Future clinical studies are needed to confirm the results presented here.

Respiratory triggered diffusion-weighted imaging with a single diffusion sensitising gradient to reduce image acquisition time – A feasibility study in the workup of hepatocellular carcinoma

PurposeWe evaluated respiratory triggered unidirectional single-shot echo-planar imaging (u-SSEPI) as a time-saving measure in diffusion imaging of the upper abdomen. Specifically, we compared the ADC values obtained from unidirectional DWI (u-SSEPI) and routine DWI (4t-SSEPI) and also the diagnostic accuracies of unidirectional and routine DWI sequences in the identification of focal liver lesions in the setting of chronic liver disease (CLD). Materials and methodsThis prospective, IRB approved study, included 48 patients of CLD who underwent-DCE-MRI on a 1.5 T scanner for hepatocellular carcinoma (HCC) workup. In addition to 4t-SSEPI, u-SSEPI was acquired with the diffusion sensitising gradient being applied in only one direction; keeping all other parameters same as 4t-SSEPI. Two blinded radiologists evaluated the DWI studies for image quality and detection of liver lesions. A composite gold standard was established using DCE-MRI, follow-up imaging and patient clinical details. The apparent diffusion coefficients (ADCs) of the liver, spleen and the lesions were compared between the two sequences. ROC analysis evaluated the diagnostic accuracy of ADC from both the sequences in identifying HCC. ResultsEighty-eight lesions were identified using the composite gold standard. u-SSEPI resulted in 3 times faster image acquisition. No statistically significant differences were demonstrated between the unidirectional and routine DWI sequences for image quality parameters and lesion detection rates. Lesion wise comparison of the ADC values from both the sequences was not statistically different (p = 0.8) with a coefficient of variation = 12–14 %. The Bland- Altman plots and the Passing-Bablock regression analysis demonstrated a systematic and proportional bias between the ADC values obtained. The AUC of the ROC curve, however, was 0.63–observer1; 0.62–oobserver2 for routine DWI and 0.65; 0.62 for unidirectional DWI when ADC was used to identify HCC (the AUCs were not statistically different (p = 0.6−0.8)). ConclusionNo significant differences were demonstrated in the diagnostic accuracies of unidirectional and routine DWI in the diagnosis of HCC. Unidirectional diffusion may be further evaluated in other organs where diffusion is isotropic, especially in respiratory triggered sequences where the imaging time dividend is significant.

Telecentric design for digital-scanning-based HiLo optical sectioning endomicroscopy with an electrically tunable lens.

Confocal endoscopy has been widely used to obtain fine optically sectioned images. However, confocal endomicroscopic images are formed by point-by-point scanning in both lateral and axial directions, which results in long image acquisition time. Here, an endomicroscope with telecentric configuration is presented to achieve nonmechanical and rapid axial scanning for volumetric fluorescence imaging. In our system, optical sectioning in wide-field fashion is obtained through HiLo imaging with a digital micromirror device. Axial scanning, without mechanical moving parts, is conducted by digital focus adjustment using an electrically tunable lens, offering constant magnification and contrast. We demonstrate imaging performance of our system with optically sectioned images using fluorescently labeled beads, as well as ex vivo mice cardiac tissue samples. Our system provides multiple advantages, in terms of improved scanning range, and reduced image acquisition time, which shows great potentials for three-dimensional biopsies of volumetric biological samples.

Fast acquisition with seamless stage translation (FASST) for a trimodal x-ray breast imaging system.

A major technical obstacle to bringing x-ray multicontrast (i.e., attenuation, phase, and dark-field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure. The purpose of this work was to introduce a fast acquisition with seamless stage translation (FASST) technique to a prototype multicontrast breast imaging system for reduced image acquisition time that is clinically acceptable. The prototype system was constructed based on a Hologic full-field digital mammography + digital breast tomosynthesis combination system. During each FASST acquisition process, a motorized stage holding a diffraction grating travels continuously with a constant velocity, and a train of 15 short x-ray pulses (35ms each) was delivered by using the Zero-Degree Tomo mode of the Hologic system. Standard phase retrieval was applied to the 15 subimages without spatial interpolation to avoid spatial resolution loss. The method was evaluated using a physical phantom, a bovine udder specimen, and a freshly resected mastectomy specimen. The FASST technique was experimentally compared with single-shot acquisition methods and the standard phase stepping method. The image acquisition time of the proposed method is 3.7s. In comparison, conventional phase stepping took 105 s using the same prototype imaging system. The mean glandular dose of both methods was matched at 1.3mGy. No artifacts or spatial resolution loss was observed in images produced by FASST. In contrast, the single-shot methods led to spatial resolution loss and residual moiré artifacts. The FASST technique reduces the data acquisition time of the prototype multicontrast x-ray breast imaging system to 3.7s, such that it is comparable to a clinical digital breast tomosynthesis exam.

Reconstruction of atomic force microscopy image using compressed sensing

Atomic Force Microscopy (AFM) is one of the most popular and advanced tools for ultra high-resolution imaging and nanomanipulation of nano-scale matter. But AFM imaging typically takes a long time. High-speed and high-precision AFM measurement has attracted wide attention in recent several years. In traditional AFM, simple reduction in the number of measurement points may lose essential sample topography information. To resolve such problems, an AFM image reconstruction method based on Compressed Sensing (CS) theory is applied to reduce image acquisition time without cutting down the image quality. The benefit of using CS approach in AFM is shortening the imaging time, minimizing the interaction with the sample, and finally avoiding sample damage in AFM. Three kinds of testing samples with high and low frequency components were examined by a scanning electron microscope (SEM) and by AFM. An orthogonal Matching Pursuit (OMP) algorithm is employed to reconstruct an AFM image with different sampling rates. Subsequently the reconstruction results of sample topography images are analyzed and evaluated. Using the CS approach in AFM can greatly improve the AFM imaging process. Experimental results show that the obtained reconstructed images have different resolution and quality, depending on the surface morphology of the sample and sampling rates.

Feasibility of Using Multiplane Stress Imaging to Add Measurement of LV Ejection Fraction Response to Exercise to Regional Wall Motion Scoring During Stress Echo

Multiplane 4D imaging probes not only increase accuracy of stress echo by reducing image acquisition time whilst maintaining orthogonal triplane views, but also have the potential to add a biplane ejection fraction (LVEF) measurement to the standard regional wall motion scoring. Methods: We prospectively studied 67 consecutive patients undergoing a treadmill stress echocardiogram using a Bruce protocol. Multiplane images of the left ventricle were obtained at rest and post exercise using a GE Vivid 9 scanner and a 4D multiplane probe. All studies were performed by a single senior sonographer. The time taken to obtain the images post exercise was recorded and the LVEF measured off line using EchoPac™ BT12 software and Simpson's biplane method. Results: We included 67 patients (52 male, 15 female) over 12 months, ranging in age from 28 to 85 years, (63 ± 11 years, mean ± SD). The patients’ sizes varied with BSA ranging from 1.53–2.38 m2 (mean 1.92m2). Images suitable for standard regional wall motion scoring were obtained in 95%, within 35 ± 9 seconds, and the LVEF was measurable in 100% of these patients. Conclusion: Left ventricular ejection fraction response to exercise could be measured using multiplane 4D imaging in all patients with suitable stress echo images. Whilst the clinical utility of this will depend on the context in which it is used the results do suggest that it is feasible to incorporate this into standard stress echo protocols and may add important information to regional wall motion scoring.