Decrease Acquisition Time Research Articles

High-count-rate particle tracking in proton and carbon radiotherapy with Timepix2 operated in ultra-short acquisition time

This work investigates the operational acquisition time limits of Timepix3 and Timepix2 detectors operated in frame mode for high-count rate of high deposited energy transfer particles. Measurements were performed using alpha particles from a 241Am laboratory source and proton and carbon ion beams from a synchrotron accelerator. The particle count rate upper limit is determined by overlapping per-pixel particle signals, identifiable by the hits per pixel counter > 2, indicating the need to decrease acquisition time. On the other hand, the lower limit is the time required to collect the particle deposited charge while maintaining spectral properties. Different acquisition times were evaluated for an AdvaPIX Timepix3 detector (500 μm Silicon sensor) with standard per-pixelDigital to Analog Converter (DAC)settings and a Minipix Timepix2 detector (300 μm Silicon sensor) with standard and customized settings the pulse shaping parameter and threshold. For AdvaPIX Timepix3, spectra remained accurate down to 100 μs frame acquisition time; at 10 μs, loss of collected charge occurred, suggesting either avoiding this acquisition time or applying a correction. Timepix2 allowed acquisition times down to 100 ns for single particle track measurements even for high energy loss, enabled by a new Timepix2 feature delaying shutter closure until full particle charge collection. This work represents the first measurement utilizing Timepix-chips pixel detectors in an accelerator beam of clinical energy and intensity without the need to decrease the beam current. This is made possible by exploiting the short shutter feature in Timepix2 and a customized per-pixel energy calibration of the Timepix2 detector with a larger discharging signal value which allowed for shorterTime-over-Threshold (ToT)signal. These customized settings extend the operation of the pixel detectors to higher event rates up to 109 particles/cm2/s.

Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.

To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm. Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student's paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis. Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (-19.5 ± 4.3 vs -19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99). Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality. Question Cine CMR acquisitions are time-intensive and vulnerable to artifacts. Findings Low-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35-42% without a significant difference in volumetric results or subjective image quality. Clinical relevance DL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.

Accelerated Diffusion-Weighted Magnetic Resonance Imaging of the Liver at 1.5 T With Deep Learning-Based Image Reconstruction: Impact on Image Quality and Lesion Detection.

To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI). Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity. DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly (P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe (P < 0.001), sharpness of intrahepatic vessel margin (P < 0.001), sharpness of the pancreatic contour (P < 0.001), in-plane motion artifact (P = 0.002), and overall image quality (P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe (P = 0.006), sharpness of the pancreatic contour (P = 0.020), and in-plane motion artifact (P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression (P = 0.004) and sharpness of the pancreatic contour (P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1. Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.

Clinical utility of a rapid two-dimensional balanced steady-state free precession sequence with deep learning reconstruction

BackgroundCardiovascular magnetic resonance (CMR) cine imaging is still limited by long acquisition times. This study evaluated the clinical utility of an accelerated two-dimensional (2D) cine sequence with deep learning reconstruction (Sonic DL) to decrease acquisition time without compromising quantitative volumetry or image quality. MethodsA sub-study using 16 participants was performed using Sonic DL at two different acceleration factors (8x and 12x). Quantitative left-ventricular volumetry, function and mass measurements were compared between the two acceleration factors against a standard cine method. Following this sub-study, 108 participants were prospectively recruited and imaged using a standard cine method and the Sonic DL method with the acceleration factor that more closely matched the reference method. Two experienced clinical readers rated images based on their diagnostic utility and performed all image contouring. Quantitative contrast difference and endocardial border sharpness were also assessed. Left- and right-ventricular volumetry, left-ventricular mass and myocardial strain measurements were compared between cine methods using Bland-Altman plots, Pearson’s correlation, and paired t-tests. Comparative analysis of image quality was measured using Wilcoxon-signed-rank tests and visualized using bar graphs. ResultsSonic DL at an acceleration factor of 8 more closely matched the reference cine method. There were no significant differences found across left ventricular volumetry, function, or mass measurements. In contrast, an acceleration factor of 12 resulted in a 6% reduction of measured ejection fraction when compared to the standard cine method and a 4% reduction of measured ejection fraction when compared to Sonic DL at an acceleration factor of 8. Thus, Sonic DL at an acceleration factor of 8 was chosen for downstream analysis. In the larger cohort, this accelerated cine sequence was successfully performed in all participants and significantly reduced the acquisition time of cine images compared to the standard 2D method (reduction of 40%, p < 0.0001). Diagnostic image quality ratings and quantitative image quality evaluations were statistically not different between the two methods (p > 0.05). Left- and right-ventricular volumetry and circumferential and radial strain were also similar between methods (p > 0.05) but left-ventricular mass and longitudinal strain were over-estimated using the proposed accelerated cine method (mass over-estimated by 3.36g/m2, p < 0.0001; longitudinal strain over-estimated by 1.97%, p = 0.001). ConclusionsThis study found that an accelerated 2D cine method with DL reconstruction at an acceleration factor of 8 can reduce CMR cine acquisition time by 40% without significantly affecting volumetry or image quality. Given the increase of scan time efficiency, this undersampled acquisition method using deep learning reconstruction should be considered for routine clinical CMR.

Fully automated computational measurement of noise in positron emission tomography

ObjectivesTo introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.Methods[18F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI.ResultsIrrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600.ConclusionAn algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.Clinical relevance statementAn automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions.Key Points• Noise is an important quantitative marker that strongly impacts image quality of PET images.• An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.• An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated “sufficient image quality” and 117 images rated “insufficient image quality”. The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists.

Impact of Reduced Image Noise on Deauville Scores in Patients with Lymphoma Scanned on a Long-Axial Field-of-View PET/CT-Scanner

Total body and long-axial field-of-view (LAFOV) PET/CT represent visionary innovations in imaging enabling either improved image quality, reduction in injected activity-dose or decreased acquisition time. An improved image quality may affect visual scoring systems, including the Deauville score (DS), which is used for clinical assessment of patients with lymphoma. The DS compares SUVmax in residual lymphomas with liver parenchyma, and here we investigate the impact of reduced image noise on the DS in patients with lymphomas scanned on a LAFOV PET/CT. Sixty-eight patients with lymphoma underwent a whole-body scan on a Biograph Vision Quadra PET/CT-scanner, and images were evaluated visually with regard to DS for three different timeframes of 90, 300, and 600 s. SUVmax and SUVmean were calculated from liver and mediastinal blood pool, in addition to SUVmax from residual lymphomas and measures of noise. SUVmax in liver and in mediastinal blood pool decreased significantly with increasing acquisition time, whereas SUVmean remained stable. In residual tumor, SUVmax was stable during different acquisition times. As a result, the DS was subject to change in three patients. Attention should be drawn towards the eventual impact of improvements in image quality on visual scoring systems such as the DS.

Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate.

Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. Retrospective. Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b=50 sec/mm2 , 1000 sec/mm2 , calculated b=1500 sec/mm2 . CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1=nondiagnostic, 4=excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. P=0.05. Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P=0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P=0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P=0.98), reader 2: 0.65, 0.66 (P=0.99), reader 3: 0.57, 0.60 (P=0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P=1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P=0.25), negative predictive value (0.88, 0.88, P=0.48). Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. 3. Stage 2.

Imaging of Single-Photon Orbital-Angular-Momentum Bell States

Bell states are the most fundamental resource in realizing quantum information tasks and have a very unique position in quantum mechanics. Utilizing orbital angular momentum (OAM) to encode single-photon Bell states enables the realization of high-dimensional Hilbert space, which is crucial for the field of quantum information. In this paper, we design a single-photon OAM Bell-state evolution device based on the Sagnac interferometer, which can make one-to-one correspondence between the input Bell states and the output states. Moreover, we also develop a single-photon single-pixel imaging (SPI) technique, which improves spatial resolution and decreases acquisition time in the meantime, to capture interference images of output states. The results show that single-photon OAM Bell states can be fully recognized by comparing the differences of interference images. We innovatively utilize the SPI technique for single-photon OAM Bell-state recognition. This indicates that the SPI technique effectively promotes the study of quantum information based on OAM, while the quantum information based on OAM provides a clear application scenario for the SPI technique.

Simulated Reduced-Count Whole-Body FDG PET: Evaluation in Children and Young Adults Imaged on a Digital PET Scanner.

BACKGROUND. Digital PET scanners with increased sensitivity may allow shorter scan acquisition times or reductions in administered radiopharmaceutical activities. OBJECTIVE. The purpose of this study was to evaluate in children and young adults the impact of shorter simulated acquisition times on the quality of whole-body FDG PET images obtained using a digital PET/CT system. METHODS. This retrospective study included 27 children and young adults (nine male and 18 female patients) who underwent clinically indicated whole-body FDG PET/CT examinations performed using a 25-cm axial FOV PET/CT system at 90 s per bed position (expressed hereafter as seconds per bed). Raw list-mode data were reprocessed to simulate acquisition times of 60, 55, 50, 45, 40, and 30 s/bed. Three radiologists independently reviewed reconstructed images and assigned Likert scores for lesion conspicuity, normal structure conspicuity, image quality, and image noise. A separate observer recorded the SUVmax, SUVmean, and SD of the SUV (SUVSD) for liver, thigh, and the most FDG-avid lesion. The SUVSD/SUVmean (the SUVSD divided by the SUVmean) was calculated as a surrogate of image noise. ANOVA, the Friedman test, and the Dunn test were used to compare qualitative measures (combining reader scores) and SUV measurements. RESULTS. The mean patient age was 10.8 ± 8.3 (SD) years, mean BMI was 18.7 ± 2.9, and mean administered FDG activity was 4.44 ± 0.37 MBq/kg (0.12 ± 0.01 mCi/kg). No qualitative measure showed a significant difference versus 90 s/bed for the simulated acquisition at 60 s/bed (all p > .05). Significant differences (all p < .05) versus 90 s/bed were observed for lesion conspicuity at at most 40 s/bed, conspicuity of normal structures and overall image quality at at most 45 s/bed, and image noise at at most 55 s/bed. SUVmean was not significantly different from 90 s/bed for any site for any reduced-count simulation (all p > .05). SUVSD/SUVmean and SUVmax showed gradual increases with decreasing acquisition times and were significantly different from 90 s/bed only for liver at 60 s/bed (for SUVmax: 1.00 ± 0.00 vs 1.05 ± 0.03, p = .02; for SUVSD/SUVmean: 0.09 ± 0.02 vs 0.11 ± 0.02, p = .04). CONCLUSION. Favorable findings for the simulated acquisition at 60 s/bed suggest that, in children and young adults who undergo imaging performed using a 25-cm FOV digital PET scanner, acquisition time or administered FDG activity may be decreased by approximately 33% from the clinical standard without significantly impacting image quality. CLINICAL IMPACT. A 25-cm axial FOV digital scanner may allow FDG PET/CT examinations to be performed with reduced radiation exposure or faster scan acquisition times.

Optical Resonances of Chiral Metastructures in the Mid‐infrared Spectral Range

AbstractMetamaterials with localized surface plasmon resonances have been the subject of intense research for a wealth of applications ranging from active photonic devices to analytical sensing units that utilizes the local field enhancement to improve sensitivity and decrease acquisition time. Herein, we focus on the modelled properties of a series of spiral metastructures that present multiple chiral plasmon modes in the 1–10 μm mid‐IR spectral range and that could be utilized to enhance vibrational circular dichroism measurements to further identify molecular, supramolecular and structural chirality centres. Finite‐difference time‐domain electromagnetic modelling was conducted to calculate the infrared spectra of the metastructures in response to a circularly polarized excitation. Several geometric parameters were altered to further tune the position of the resonances. In addition, the near‐field distribution of the optical resonances was calculated providing a spatial snapshot of the chiral modes. Preliminary spiral structures were fabricated using electron beam lithography and their vibrational circular response was measured.

Measuring the 3J HNHa coupling by a simple 2D-intra-HNCA IP/AP-E.COSY with simultaneous encoding of 15N chemical shift and 1J HaCa evolution

The 3J coupling values are commonly used in biomolecular NMR to extract structural information. Here we present a novel intra HNCA IP/AP E.COSY pulse sequence that allows to measure 3J HNHa coupling constants by a simple and rapid two-dimensional 1H-15N correlation experiment where the 15N frequency is encoded at the same time as the 1J HaCa coupling evolution. The advantage with respect to the conventional 3D HNCA E.COSY pulse sequence is the dimensionality reduction to a simple 2D experiment, which decreases acquisition time and facilitates data analysis. The performance of this new experiment is demonstrated with an ubiquitin sample at 500 MHz.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI

Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

3D MRI in Musculoskeletal Oncology.

Advances in magnetic resonance imaging (MRI) technology now enable the feasible three-dimensional (3D) acquisition of images. With respect to the imaging of musculoskeletal (MSK) tumors, literature is beginning to accumulate on the use of 3D MRI acquisition for tumor detection and characterization. The benefits of 3D MRI, including general advantages, such as decreased acquisition time, isotropic resolution, and increased image quality, are not only inherently useful for tumor imaging, but they also contribute to the feasibility of more specialized tumor-imaging techniques, such as whole-body MRI, and are reviewed here. Disadvantages of 3D acquisition, such as motion artifact and equipment requirements, do exist and are also discussed. Although further study is needed, 3D MRI acquisition will likely prove increasingly useful in the evaluation of patients with tumors of the MSK system.

Compressed sensing time-of-flight magnetic resonance angiography with high spatial resolution for evaluating intracranial aneurysms: comparison with digital subtraction angiography.

Compressed sensing is used for accelerated acquisitions with incoherently under-sampled k-space data, and intracranial time-of-flight magnetic resonance angiography is suitable for compressed sensing. Compressed sensing time-of-flight is beneficial in decreasing acquisition time and increasing spatial resolution while maintaining acquisition time. In this retrospective study, we aimed to evaluate the image quality and diagnostic performance of compressed sensing time-of-flight with high spatial resolution and compare with parallel imaging time-of-flight using digital subtraction angiography as a reference. In total, 39 patients with 46 intracranial aneurysms underwent parallel imaging and compressed sensing time-of-flight in the same imaging session and digital subtraction angiography before or after magnetic resonance angiography. The overall image quality, artefacts and diagnostic confidence were assessed by two observers. The contrast ratio, maximal aneurysm diameters and diagnostic performance were evaluated. Compressed sensing time-of-flight showed significantly better overall image quality, degree of artefacts and diagnostic confidence in both observers, with better inter-observer agreement. The contrast ratio was significantly higher for compressed sensing time-of-flight than for parallel imaging time-of-flight in both observers (source images, P < 0.001; maximum intensity projection images, P < 0.05 for both observers); however, the measured maximal diameters of aneurysms were not significantly different. Compressed sensing time-of-flight showed higher sensitivity, specificity, accuracy and positive and negative predictive values for detecting aneurysms than parallel imaging time-of-flight in both observers, with better inter-observer agreement. Compressed sensing time-of-flight was preferred over parallel imaging time-of-flight by both observers; however, parallel imaging time-of-flight was preferred in cases of giant and large aneurysms. Compressed sensing-time-of-flight provides better image quality and diagnostic performance than parallel imaging time-of-flight. However, neuroradiologists should be aware of under-sampling artefacts caused by compressed sensing.

A rapid protocol to evaluate coronary flow reserve with myocardial scintigraphy: A prospective study using Regadenoson

Cardiac positron emission tomography (PET) is superior to single-photon emission computed tomography (SPECT) thanks to its quantitative analysis capabilities, especially for measuring myocardial blood flow (MBF) and myocardial flow reserve (MFR). The recent development of new cadmium zinc telluride (CZT) gamma cameras has enabled dynamic acquisition, allowing measurement of MBF and MFR. This prospective study aimed to determine the fastest protocol, to analyse MBF and perfusion imaging. Methods and resultsFrom February to June 2018, 10 patients were referred to our centre and prospectively enrolled in our study to evaluate the MBF and MFR, with 5 different reconstruction list-mod. The primary endpoint was to assess the reproducibility of the different reconstruction, and time of acquisition. The secondary endpoint was the evaluation of the image quality of the MPI. The 6-min tests highlighted no significant difference in the flow measurement and MFR (P=0,15). Differences become significant by decreasing acquisition time. The image quality did not differ significantly (P=0.6). ConclusionWe determined a fast (12mn acquisition) reliable and reproducible protocol to evaluate MBF and MFR without any loss of MPI information.

Evaluation of a new multipurpose whole-body CzT-based camera: comparison with a dual-head Anger camera and first clinical images

BackgroundEvaluate the physical performance of the VERITON CzT camera (Spectrum Dynamics, Caesarea, Israel) that benefits from new detection architecture enabling whole-body imaging compared to that of a conventional dual-head Anger camera.MethodsDifferent line sources and phantom measurements were performed on each system to evaluate spatial resolution, sensitivity, energy resolution and image quality with acquisition and reconstruction parameters similar to those used in clinical routine. Extrinsic resolution was assessed using 99mTc capillary sources placed successively in air, in a head and in a body phantom filled with background activity. Spectral acquisitions for various radioelements used in nuclear medicine (99mTc, 123I, 201Tl, 111In) were performed to evaluate energy resolution by computing the FWHM of the measured photoelectric peak. Tomographic sensitivity was calculated by recording the total number of counts detected during tomographic acquisition for a set of source geometries representative of different clinical situations. Sensitivity was also evaluated in focus mode for the CzT camera, which consisted of forcing detectors to collect data in a reduced field-of-view. Image quality was assessed with a Jaszczak phantom filled with 350 MBq of 99mTc and scanned on each system with 30-,20-,10- and 5-min acquisition times.ResultsExtrinsic and tomographic resolution in the brain and body phantoms at the centre of the FOV was estimated at 3.55, 7.72 and 6.66 mm for the CzT system and 2.47, 7.75 and 7.72 mm for the conventional system, respectively. The energy resolution measured at 140 keV was 5.46% versus 9.21% for the Anger camera and was higher in a same manner for all energy peaks tested. Tomographic sensitivity for a point source in air was estimated at 236 counts·s−1·MBq−1 and increased to 1159 counts·s−1·MBq−1 using focus mode, which was 1.6 times and 8 times greater than the sensitivity measured on the scintillation camera (144 counts·s−1·MBq−1). Head and body measurements also showed higher sensitivity for the CzT camera in particular with focus mode. The Jaszczak phantom showed high image contrast uniformity and a high signal-to-noise ratio on the CzT system, even when decreasing acquisition time by 6-fold. Representative clinical cases are shown to illustrate these results.ConclusionThe CzT camera has a superior sensitivity, higher energy resolution and better image contrast than the conventional SPECT camera, whereas spatial resolution remains similar. Introduction of this new technology may change current practices in nuclear medicine such as decreasing acquisition time and activity injected to patient.

The four-minute approach revisited: accelerating MRI-based multi-factorial age estimation

ObjectivesThis feasibility study aimed to investigate the reliability of multi-factorial age estimation based on MR data of the hand, wisdom teeth and the clavicles with reduced acquisition time.MethodsThe raw MR data of 34 volunteers—acquired on a 3T system and using acquisition times (TA) of 3:46 min (hand), 5:29 min (clavicles) and 10:46 min (teeth)—were retrospectively undersampled applying the commercially available CAIPIRINHA technique. Automatic and radiological age estimation methods were applied to the original image data as well as undersampled data to investigate the reliability of age estimates with decreasing acquisition time. Reliability was investigated determining standard deviation (SSD) and mean (MSD) of signed differences, intra-class correlation (ICC) and by performing Bland-Altman analysis.ResultsAutomatic age estimation generally showed very high reliability (SSD < 0.90 years) even for very short acquisition times (SSD ≈ 0.20 years for a total TA of 4 min). Radiological age estimation provided highly reliable results for images of the hand (ICC ≥ 0.96) and the teeth (ICC ≥ 0.79) for short acquisition times (TA = 16 s for the hand, TA = 2:21 min for the teeth), imaging data of the clavicles allowed for moderate acceleration (TA = 1:25 min, ICC ≥ 0.71).ConclusionsThe results demonstrate that reliable multi-factorial age estimation based on MRI of the hand, wisdom teeth and the clavicles can be performed using images acquired with a total acquisition time of 4 min.

Acquisition and Analysis of Multi-Global Navigation Satellite System Signals Based on Fast Fourier Transform

This paper presents an essential technique for acquiring multi-GNSS signals using FFT. The fundamental approach of multi-GNSS satellite navigation is that the navigation information transmitted with the help of stand-alone GNSS system of numerous constellations is acquired by the receiver. The reason for the acquisition is to apprehend how many total numbers of satellites are currently visible to the user segment; also, the acquisition must determine the code phase and frequency of satellite signals. Although, there are three search domains, which are Doppler frequency, code phase, and code identification, in the acquisition processing, the emphasis of this paper will be on the search of the code phase. FFT based acquisition can decrease acquisition time of DSSS signals due to an increase in the power of DSP chips. The use of FFT based algorithms to perform acquisition of Direct Sequence Spread Spectrum (DSSS) is still the hot topic of research. The main purpose of this study is the analysis of the Multi-GNSS signal acquisition process using the Fast Fourier Transform (FFT) method. Based on MATLAB software platform; GPS, GLONASS, and BeiDou signals are acquired from the real IF signal records of GPS, GLONASS, and BeiDou. The output results of the FFT based acquisition are presented and discussed.

The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging

Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths that can potentially benefit radiology. Specifically, adversarial networks have the potential to decrease radiation exposure to patients through minimizing repeat imaging due to artifact, decreasing acquisition time, and generating higher quality images from low-dose or no-dose studies. The authors provide an overview of a specific type of adversarial network called a “generalized adversarial network” and review its uses in current medical imaging research.