Undersampled Projection Research Articles

Comparing two MRI methods to assess fundamental sex differences in resting cerebral blood flow

Background: Cerebral blood flow (CBF) is a critical measurement for understanding healthy brain physiology prior to aging or neurologic diseases. Patterns of stroke and neurologic pathologies are often sex-specific. Therefore, it is important to understand basic sex differences in CBF, which are often associated with poor brain outcomes. Furthermore, prior literature examining sex differences in CBF is confounded by age, comorbidities, medications, lack of control of menstrual cycle and/or hormonal contraceptive use, and methodological discrepancies. The objective of this study was to compare two different magnetic resonance imaging (MRI) methods of quantifying CBF in young adults while controlling for the aforementioned confounding factors. We hypothesized that females would display higher CBF than males, and that there would be a high reliability between Arterial Spin Labeling (ASL) and 4D Flow MRI. Methods: 30 males and 26 females (23 ± 4yr) were studied on a single MRI (3T) visit. Subjects were fasted 8 hours and withheld caffeine, exercise, and NSAIDs for at least 24 hours. Females were not taking hormonal contraception and were studied on days 1-7 of the menstrual cycle. Microvascular CBF (mL/100g/min) was assessed via pseudo-continuous ASL (pcASL) while 4D Flow MRI acquired with Phase Contrast Vastly undersampled Isotropic Projection Reconstruction (PC VIPR) yielded macrovascular flow via the larger cerebral arteries, summing the internal carotid arteries and basilar artery for total CBF (mL/min). A T1-weighted MRI scan was used to provide brain volumes, allowing CBF from PC VIPR to be normalized and compared to ASL (mL/100g/min). The scans were taken consecutively, with subjects breathing normoxic gas while hemodynamics and end tidal CO2 were monitored. Regression analysis was used to determine reliability of two measures to test a sex difference in CBF. Results: Linear regression analysis demonstrated reliability between measurements of resting CBF via ASL and PC VIPR (p<0.001, R2=0.621). However, ASL measured significantly higher blood flows than PC VIPR in females (63.40 ± 8.55 vs. 56.47 ± 8.48 mL/100g/min, p<0.001) while male subjects had similar flows measured by both methods (45.75 ± 9.39 vs. 46.69 ± 8.19 mL/100g/min, p=0.464). Females displayed an increase in resting CBF compared to males, being 27.8% and 17.3% higher in the ASL and PC VIPR flow measurements (both p<0.001), respectively. Conclusions: These results demonstrate that ASL and PC VIPR CBF display a strong correlation, and that females have greater resting CBF compared to males that is detected by both MRI methods of quantification. However, ASL resulted in greater flow measurements in females, resulting in larger differences being observed (27.8% versus 17.3%). This discrepancy in CBF quantification warrants further investigation to determine the optimal mode of CBF measurement and comparison. Funding by NIH (HL150361). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.

The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions. This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves. In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

Design of prehospital spectral CT with a rotation-free rectangular gantry and deep learning reconstruction

Treatment of stroke depends on a narrow therapeutic time window and requires urgent intervention to be emergently pursued. Despite recent “fast” initiatives that have underscored “time is brain,” many patients still fail to present within the narrow time window to receive maximum treatment benefit from advanced stroke therapies. The convergence of emergency medical services, telemedicine, and mobile technology, including transportable computed tomography scanners, has presented a unique opportunity to advance patient stroke care in the prehospital field by shortening the time to hyperacute stroke treatment with a mobile stroke unit. However, in order to facilitate the integration of computed tomography (CT) scanners into small ambulances, their geometry should be streamlined and made compact. Additionally, it is imperative that radiation exposure from prehospital CT scans comply with established radiation protection guidelines to safeguard both patients and healthcare workers. In this study, we propose a newly designed CT system for a rotation-free rectangular gantry with a photon-counting detector (PCD) and a deep-learning reconstruction approach for undersampled projections. Undersampled projections obtained using the designed CT system were implemented using a deep learning method. The deep learning approach showed superior image quality without distortion compared to state-of-the-art reconstruction methods. Finally, we implemented imaging of brain microhemorrhages using low- and high-energy images obtained from the PCD. Our results indicate that the proposed scanner effectively shows potential for acute stroke detection in prehospital ambulances. Its effectiveness was validated by comparing its image performance with those of other methods such as FBP and compressed sensing.

Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L1-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L0-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery.

Hybrid compressive spectral imaging with coded aperture optimization based on coherence minimization

Compressive spectral imaging (CSI) obtains spectral images through reconstruction from under-sampled low-dimensional projections with spatial and spectral coding. However, the simultaneous realization of high spatial–spectral resolution is challenged due to limitations of a single CSI architecture. In this paper, a hybrid CSI system combining the coded aperture snapshot spectral imaging (CASSI) and the spectrometer-based single-pixel spectral imaging (SPSI), with the corresponding coded aperture optimization for both constituent systems are proposed. The hybrid CSI system sustains the complementary advantages of the spatial resolution in CASSI and the spectral fidelity in SPSI, ensuring high reconstruction quality in both spatial and spectral dimensions with increased sampling efficiency. The derivable matrix operation of the hybrid CSI system is formulated; meanwhile, based on coherence minimization, the individual optimization on the coded apertures in the CASSI or the SPSI system, and the joint optimization on the coded apertures in the hybrid system are demonstrated. The reconstruction is further improved with the optimization, indicated by the quantitative image quality metrics and the point spectra. Compared to the conventional unoptimized CASSI, the optimized hybrid CSI system improves the PSNR for more than 7 dB. This work may introduce opportunities for multipath compressive imaging systems toward high-efficiency, high-resolution future.

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis, while it does not meet the needs of industrial mass production because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT-reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor and observe the objects in different angles of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time this source-detector layout strategy has been adopted. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. Our experiment shows that the reconstruction resulting in undersampled projections is highly enhanced using a deep-learning neural network which meets the demand of non-destructive testing. Meanwhile, our proposed system structure can perform quick scans and reconstructions on larger objects. It solves the pain points of limited scan size and slow scanning speed of existing industrial CT scans.

Combination of compressed sensing-based iterative reconstruction and offset acquisition for I-123 FP-CIT SPECT: a simulation study

Objective(s):The purpose of this study was to validate undersampled single-photon emission computed tomography (SPECT) imaging using a combination of compressed sensing (CS) iterative reconstruction (CS-IR) and offset acquisition.Methods:Three types of numerical phantoms were used to evaluate image quality and quantification derived from CS with offset acquisition. SPECT images were reconstructed using filtered back-projection (FBP), maximum likelihood-expectation maximization (ML-EM), CS-IR, and CS-IR with offset acquisition. The efficacy of CS-IR with offset acquisition was examined in terms of spatial resolution, aspect ratio (ASR), activity concentration linearity, contrast, percent coefficient of variation (%CV), and specific binding ratio (SBR).Results:The full widths at half maximum remained unchanged as the number of projections decreased in CS-IR with offset acquisition. Changes in ASRs and linearities of count density were observed for ML-EM and CS-IR from undersampled projections. The %CV obtained by CS-IR with offset acquisition was substantially lower than that obtained by ML-EM and CS-IR. There were no significant differences between the %CVs obtained from 60 projections by CS-IR with offset acquisition and from 120 projections by FBP. Although the SBRs for CS-IR with offset acquisition tended to be slightly lower than for FBP, the SBRs for CS-IR with offset acquisition did not change with the number of projections.Conclusions:CS-IR with offset acquisition can provide good image quality and quantification compared with a commonly used SPECT reconstruction method, especially from undersampled projection data. Our proposed method could shorten overall SPECT acquisition times, which would benefit patients and enable quantification with dynamic SPECT acquisitions.

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network.

Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation. In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. Instead of learning the patient-specific structures, the proposed model learns a generalized pattern of utilizing the patient-specific information in the prior images to facilitate the under-sampled image enhancement. Specifically, the MeCNN consists of a merging-encoder and a decoder. The merging-encoder extracts image features from both the prior CT images and the under-sampled CBCT images, and merges the features at multi-scale levels via deep convolutions. The merged features are then connected to the decoders via shortcuts to yield high-quality CBCT images. The proposed model was tested on both the simulated CBCTs and the clinical CBCTs. The predicted CBCT images were evaluated qualitatively and quantitatively in terms of image quality and tumor localization accuracy. Mann-Whitney U test was conducted for the statistical analysis. P<0.05 was considered statistically significant. The proposed model yields CT-like high-quality CBCT images from only 36 half-fan projections. Compared to other methods, CBCT images augmented by the proposed model have significantly lower intensity errors, significantly higher peak signal-to-noise ratio, and significantly higher structural similarity with respect to the ground truth images. Besides, the proposed method significantly reduced the 3D distance of the CBCT-based tumor localization errors. In addition, the CBCT augmentation is nearly real-time. With the prior-image guidance, the proposed method is effective in reconstructing high-quality CBCT images from the highly under-sampled projections, considerably reducing the imaging dose and improving the clinical utility of the CBCT.

CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.

A phantom study comparing radial trajectories for accelerated cardiac 4D flow MRI against a particle imaging velocimetry reference.

Radial sampling is one method to accelerate 4D flow MRI acquisition, making feasible dual-velocity encoding (Venc) assessment of slow flow in the left ventricle (LV). Here, two radial trajectories are compared in vitro for this application: 3D radial (phase-contrast vastly undersampled isotropic projection, PC-VIPR) versus stack of stars (phase-contrast stack of stars, PC-SOS), with benchtop particle imaging velocimetry (PIV) serving as a reference standard. The study contained three steps: (1) Construction of an MRI- and PIV-compatible LV model from a healthy adult's CT images. (2) In vitro PIV using a pulsatile flow pump. (3) In vitro dual-Venc 4D flow MRI using PC-VIPR and PC-SOS (two repeat experiments). Each MR image set was retrospectively undersampled to five effective scan durations and compared with the PIV reference. The root-mean-square velocity vector difference (RMSE) between MRI and PIV images was compared, along with kinetic energy (KE) and wall shear stress (WSS). RMSE increased as scan time decreased for both MR acquisitions. RMSE was 3% lower in PC-SOS images than PC-VIPR images in 30-min scans (3.8 vs. 3.9 cm/s) but 98% higher in 2.5-min scans (9.5 vs. 4.8 cm/s). PIV intrasession repeatability showed a RMSE of 4.4 cm/s, reflecting beat-to-beat flow variation, while MRI had intersession RMSEs of 3.8/3.5 cm/s for VIPR/SOS, respectively. Speed, KE, and WSS were overestimated voxel-wise in 30-min MRI scans relative to PIV by 0.4/0.3 cm/s, 0.2/0.1 μJ/mL, and 36/43 mPa, respectively, for VIPR/SOS. PIV is feasible for application-specific 4D flow MRI protocol optimization. PC-VIPR is better-suited to dual-Venc LV imaging with short scan times.

Daikenchuto increases blood flow in the superior mesenteric artery in humans: A comparison study between four-dimensional phase-contrast vastly undersampled isotropic projection reconstruction magnetic resonance imaging and Doppler ultrasound

Respiratory-gated four-dimensional phase-contrast vastly undersampled isotropic projection reconstruction (4D PC-VIPR) is magnetic resonance (MR) imaging technique that enables analysis of vascular morphology and hemodynamics in a single examination using cardiac phase resolved 3D phase-contrast magnetic resonance imaging. The present study aimed to assess the usefulness of 4D PC-VIPR for the superior mesenteric artery (SMA) flowmetry before and after flow increase was induced by the herbal medicine Daikenchuto (TJ-100) by comparing it with Doppler ultrasound (DUS) as a current standard. Twenty healthy volunteers were enrolled in this prospective single-arm study. The peak cross-sectionally averaged velocity was measured by 4D PC-VIPR, peak velocity was measured by DUS, and flow volume (FV) of SMA and aorta were measured by 4D PC-VIPR and DUS 25 min before and after the peroral administration of TJ-100. The peak cross-sectionally averaged velocity, peak velocity, and FV of SMA measured by 4D PC-VIPR and DUS significantly increased after administration of TJ-100 (4D PC-VIPR: the peak cross-sectionally averaged velocity; p = 0.004, FV; p = 0.035, DUS: the peak velocity; p = 0.003, FV; p = 0.010). Furthermore, 4D PC-VIPR can analyze multiple blood vessels simultaneously. The ratio of the SMA FV to the aorta, before and after oral administration on the 4D PC-VIPR test also increased (p = 0.015). The rate of change assessed by 4D PC-VIPR and DUS were significantly correlated (the peak cross-sectionally averaged velocity and peak velocity: r = 0.650; p = 0.005, FV: r = 0.659; p = 0.004). Retrospective 4D PC-VIPR was a useful modality for morphological and hemodynamic analysis of SMA.

Novel FBP based sparse-view CT reconstruction scheme using self-shaping spatial filter based morphological operations and scaled reprojections

Computed tomography (CT) has been a major contributor in revolutionizing and commercializing the medical imaging industry. However, most of the commonly used CT reconstruction algorithms need sufficiently dense angular sampling views or a substantial dose of hazardous X-ray radiations. Reducing the radiation dose causes degradation in the quality of the reconstructed image, due to additional artifacts. The paper presents a novel algorithm for efficient CT reconstruction from under-sampled projections; which leads to radiation dose reduction with quality image reconstruction. The Sparse-View projection data is enhanced using a series of post-processing algorithms and computer based reprojection. The process involves enhancement through self-shaping/amoeba based morphological spatial filtering. The use of self-shaping spatial filter kernel in the area of under-sampled CT reconstruction is a novel contribution. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The proposed scheme is compared with classical reconstruction techniques for reconstruction image quality, accuracy, speed, and robustness in the presence of noise. Promising results indicate the efficacy of proposed scheme. An efficient scheme for image enhancement of Sparse-View CT is presented. The results demonstrate that the proposed scheme is visually and statistically better than classical CT reconstruction techniques, as evaluated using various image quality matrices. The presented scheme is more robust to noise in CT projections and effective for enhancing few-views reconstruction.

Role of Endothelin‐1 in Regulating Basal Macrovascular Cerebral Blood Flow: Healthy versus Metabolic Syndrome

Endothelin‐1 (ET‐1) is a potent vasoconstrictor that regulates vascular tone by binding to the ETA receptors on vascular smooth muscle. In healthy humans, blockade of ETA receptors increases resting forearm blood flow. ET‐1 appears to contribute to poor outcomes in animal models of cerebral ischemia, but the role of ET‐1 signaling in control of basal cerebral blood flow (CBF) in healthy adults is unknown. Interestingly, ET‐1 is upregulated in insulin resistance (IR) and resting CBF is reduced primarily in the anterior circulation, which may be due to enhanced ET‐1 signaling. We hypothesized: 1) ETA receptor blockade would increase CBF more in adults with IR, but play little to no role in regulating CBF in healthy adults and 2) ETA blockade would increase CBF more in the anterior than posterior circulation. Healthy normal weight adults (Control; n=6; BMI 23±2 kg/m2; age 24±4 yrs) and adults with Metabolic Syndrome (MetSyn; n=4; BMI 34±8 kg/m2; age 25±12 yrs) underwent magnetic resonance imaging (MRI) study visits. A subset of subjects completed a second MRI study visit (Control, n=6; MetSyn, n=2) with ETA blockade (10mg oral Ambrisentan). Phase‐contrast vastly undersampled isotropic projection reconstruction (PCVIPR) MRI was used to quantify total and regional CBF (CBF= blood velocity• artery cross sectional area). CBF in the anterior circulation was represented as the sum of CBF in the left and right internal carotid arteries (ICA) whereas CBF in the posterior circulation was represented by the basilar artery (BA). Total CBF was taken as the sum of flow in the left and right ICAs and BA. Heart rate (HR), mean arterial pressure (MAP), and end‐tidal CO2 (ETCO2) were monitored. Significance was determined using a mixed two‐way ANOVA or one‐way ANOVA and significance was set at p≤0.05. Results are mean±SD. HR, MAP, and ETCO2 were similar between Control and MetSyn (p&gt;0.05) and did not change with Ambrisentan in either group (p&gt;0.05). Resting total CBF was higher in Control compared to MetSyn (Control 704±87 vs. MetSyn 487±44 mL/min, p&lt;0.05). Ambrisentan did not change total CBF (p&gt;0.05). Resting anterior CBF was similar between groups (Control 535±82 vs. MetSyn 460±26 mL/min, p&gt;0.05), and was unchanged by Ambrisentan (p&gt;0.05). Resting posterior CBF was higher in Control compared to MetSyn (Control 168±33 vs. MetSyn 127±18 mL/min, p&lt;0.05) and Ambrisentan increased posterior CBF (main effect of Ambrisentan, p=0.05); however, the absolute and relative change in posterior CBF was similar between groups (%ΔCBF Control 9±15 vs. MetSyn 11±8% p&gt;0.05). IR adults exhibited lower resting total and posterior CBF compared to healthy adults. Contrary to our hypothesis, Ambrisentan did not increase total or anterior CBF in IR. Surprisingly, Ambrisentan increased CBF in the posterior circulation of healthy and IR adults similarly. Therefore, ET‐1 signaling appears to play a role in regulating vascular tone in the posterior region, however, IR adults do not exhibit enhanced sensitivity to ET‐1. A longer duration or increased severity of IR may be needed to alter cerebrovascular responses to ET‐1 signaling.Support or Funding InformationADA1‐16‐ICTS‐099

Does Hypoxia Affect Pulse Transit Time in Healthy Controls?

Hypoxia is known to increase cerebral blood flow and increase sympathetic nervous activity (SNA). Given the strong link between elevated vascular stiffness and cardiovascular diseases exhibiting elevated SNA, we tested whether a hypoxic stressor in healthy subjects would acutely change cerebral vascular stiffness. We hypothesized hypoxia would lead to increased cerebrovascular stiffness as measured by a reduced pulse wave transit time. Eight healthy controls (31±8 yrs, M=3, F=5), with a BMI of 18.5 – 25 kg/m2, laid supine in a MRI, breathing room air (Normoxia) for ~10 minutes followed by a hypoxia condition, in which subjects breathed a gas mixture of 11% O2 and 89% nitrogen through a mouthpiece. Inspired CO2 between 1 – 5% of inspired air was bled in to maintain a constant end‐tidal CO2. Heart rate, end‐tidal CO2, arterial oxygen saturation, and blood pressure were continuously monitored. A 3 Tesla MRI using 4‐dimensional flow phase contrast vastly under‐sampled isotropic projection reconstruction (PC VIPR; ~6 minutes) was used to capture cerebral blood flow (CBF = blood velocity • vessel cross sectional area) in the internal carotid arteries (ICA) and superior sagittal sinus (SSS). Cerebral blood flow across 20 equal segments of the average cardiac cycle was used to determine the time to peak flow in the ICAs and SSS during normoxia and hypoxia. Pulse transit time (PTT) was calculated as the difference in time to peak in the SSS from the time to peak in the ICAs. Data were analyzed via paired t‐tests and are presented as mean ± SD. Significance was taken as p≤0.05. Per design, hypoxia decreased SpO2 from 98%±1 to 80%±5 and increased CBF from 642±122 to 747±150 ml/min (p&lt;0.001), while end‐tidal CO2 did not change 40±2 and 39±2 mmHg. During normoxia, pulse transit time from ICA to SSS was 98±50 ms, whereas pulse transit time during hypoxia was 167±82 ms (one‐tailed P‐value=0.06). Contrary to our hypothesis, acutely increasing CBF with hypoxia led to increased pulse transit time between the ICA and SSS. These findings suggest conditions that increase cerebral blood flow may positively impact hemodynamics in a manner associated with decreased vascular stiffness.Support or Funding InformationADA 1‐12‐IN‐39

High-quality initial image-guided 4D CBCT reconstruction.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion-induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

Optimal Plane Selection for Measuring Post-prandial Blood Flow Increase within the Superior Mesenteric Artery: Analysis Using 4D Flow and Computational Fluid Dynamics.

Purpose:2D cine phase contrast (PC)-MRI is a standard velocimetry for the superior mesenteric artery (SMA); however, the optimal localization of the measurement plane has never been fully discussed previously. The purpose of this Institutional Review Board approved prospective and single arm study is to test whether flow velocimetry of the SMA with combined use of 2D cine PC-MRI and meal challenge is dependent on the localizations of the measurement planes and to seek optimal section for velocimetry.Methods:Seven healthy volunteers underwent cardiac phase resolved ECG gated 2D cine PC-MRI pre- and 30 min post-meal challenge at three measurement planes: proximal, curved mid section and distal straight section of the SMA at 3T. 4D Flow using 3D cine PC-MRI with vastly undersampled isotropic projection imaging (PC VIPR) was also performed right after 2D cine PC-MRI to delineate the flow dynamics within the SMA using streamline analysis. Two radiologists measured flow velocities, and rated the appearances of the abnormal flow in the SMA on streamlines derived from the 4D Flow and the computational fluid dynamics (CFD).Results:2D cine PC-MRI measured increased temporally averaged flow velocity (mm/s) after the meal challenge only in the proximal (129.3 vs. 97.8, P = 0.0313) and distal section (166.9 vs. 96.2, P = 0.0313), not in the curved mid section (113.1 vs. 85.5, P = 0.0625). The average velocities were highest and their standard errors (8.5–26.5) were smallest at the distal straight section both before and after the meal challenge as compared with other sections. The streamline analysis depicted more frequent appearances of vertical or helical flow in the curved mid section both on 4D Flow and CFD (κ: 0.27–0.68).Conclusion:SMA velocimetry with 2D cine PC-MRI was dependent on the localization of the measurement planes. Distal straight section, not in the curved mid section is recommended for MR velocimetry.

Hyperspectral image recovery based on fusion of coded aperture snapshot spectral imaging and RGB images by guided filtering

In compressive hyperspectral imaging systems, the key issue is to accurately and efficiently recover 3D spectral data cubes from spectrally undersampled 2D projections. In this paper, a guided-filtering-based reconstruction algorithm is proposed to recover hyperspectral data cube with a joint coded aperture snapshot spectral imaging (CASSI) and RGB imaging system. In order to preserve the spectral and spatial details in the CASSI and the RGB branch to the greatest extent, 2D projections from the two branches are reconstructed into hyperspectral images individually with a pre-trained dictionary. The appropriate combination of the information from the two branches is realized through guided image filtering, which is applied on the reconstruction result of the CASSI branch with the result from the RGB branch as the guidance image. In virtue of combination between the well-performed spectral fidelity of the CASSI branch and the high spatial accuracy of the RGB branch, the proposed guided-filtering-based compressive hyperspectral imaging (GFCHI) not only overcomes the block effect and blurring common in the state-of-the-art methods, but also improves spectral fidelity related to key applications of hyperspectral imaging. The quantitative metrics associated with spatial, spectral and overall performance demonstrate the state-of-the-art reconstruction quality of GFCHI.

Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging.

We investigated a projection interpolation method for reconstructing dynamic contrast-enhanced (DCE) heart images from undersampled x-ray projections with filtered backprojecton (FBP). This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. We conducted CT perfusion studies on 5 pigs with a standard full-view acquisition protocol (984 projections). We reconstructed DCE heart images with FBP from all and a quarter of the measured projections evenly distributed over 360°. We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). To generate MP maps, we used 3 sets of DCE heart images, and compared mean MP values and biases among the 3 protocols. Compared with synthesized full-view DCE images, sparse-view DCE images were more affected by streak artifacts arising from projection undersampling. Relative to the full-view protocol, mean bias in MP measurement associated with the sparse-view protocol was 10.0 mL/min/100 g (95%CI: −8.9 to 28.9), which was >3 times higher than that associated with the synthesized full-view protocol (3.3 mL/min/100 g, 95% CI: −6.7 to 13.2). The cubic-spline-view interpolation method improved MP measurement from DCE heart images reconstructed from only a quarter of the full projection set. This method can be used with the industry-standard FBP algorithm to reconstruct DCE images of the heart, and it can reduce the radiation dose of a whole-heart quantitative CT MP study to <2 mSv (at 8-cm coverage).

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.

Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm‐to‐cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts—reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to compressed sensing with 40% fewer projections. We further show that diverse training data classes, for example, ex vivo mouse tissue data, can be used for CNN‐based reconstructions of OPT data of other species including live zebrafish.

SparseCT: System concept and design of multislit collimators.

SparseCT, an undersampling scheme for compressed sensing (CS) computed tomography (CT), has been proposed to reduce radiation dose by acquiring undersampled projection data from clinical CT scanners (Koesters etal. in, SparseCT: Interrupted-Beam Acquisition and Sparse Reconstruction for Radiation Dose Reduction; 2017). SparseCT partially blocks the x-ray beam with a multislit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z-direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using CS algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper. We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x-ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose-weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the signal to noise ratio of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity-enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively. Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For fourfold undersampling with a 1.2mm focal spot, a minimum MSC slit width of three detector rows (projected to the detector surface) is needed for beam separation; for threefold undersampling, a minimum slit width of four detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of three or four detector rows yields similar image quality. The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x-ray beam penumbra effects showed that for threefold and fourfold dose reductions, an MSC slit width of four detector rows provided reasonable beam separation, undersampling efficiency, and image quality.