SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. METHODS: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. RESULTS: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. CONCLUSION: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

Reversibility of Cognitive Deficits and Functional Connectivity With Transfusion in Children With Sickle Cell Disease.

BACKGROUND AND OBJECTIVES: People with sickle cell disease (SCD) are at risk of cognitive dysfunction independent of stroke. Diminished functional connectivity in select large-scale networks and white matter integrity reflect the neurologic consequences of SCD. Because chronic transfusion therapy is neuroprotective in preventing stroke and strengthening executive function abilities in people with SCD, we hypothesized that red blood cell (RBC) transfusion facilitates the acute reversal of disruptions in functional connectivity while white matter integrity remains unaffected. METHODS: Children with SCD receiving chronic transfusion therapy underwent a brain MRI measuring white matter integrity with diffusion tensor imaging and resting-state functional connectivity within 3 days before and after transfusion of RBCs. Cognitive assessments with the NIH Toolbox were acquired after transfusion and then immediately before the following transfusion cycle. RESULTS: Sixteen children with a median age of 12.5 years were included. Global assessments of functional connectivity using homotopy (p = 0.234) or modularity (p = 0.796) did not differ with transfusion. Functional connectivity within the frontoparietal network significantly strengthened after transfusion (median intranetwork Z-score 0.21 [0.17-0.30] before transfusion, 0.29 [0.20-0.36] after transfusion, p < 0.001), while there was not a significant change seen within the sensory motor, visual, auditory, default mode, dorsal attention, or cingulo-opercular networks. Corresponding to the change within the frontoparietal network, there was a significant improvement in executive function abilities after transfusion (median executive function composite score 87.7 [81.3-90.7] before transfusion, 90.3 [84.3-93.7] after transfusion, p = 0.021). Participants with stronger connectivity in the frontoparietal network before transfusion had a significantly greater improvement in the executive function composite score with transfusion (r = 0.565, 95% CI 0.020-0.851, p = 0.044). While functional connectivity and executive abilities strengthened with transfusion, there was not a significant change in white matter integrity as assessed by fractional anisotropy and mean diffusivity within 16 white matter tracts or globally with tract-based spatial statistics. DISCUSSION: Strengthening of functional connectivity with concomitant improvement in executive function abilities with transfusion suggests that functional connectivity MRI could be used as a biomarker for acutely reversible neurocognitive injury as novel therapeutics are developed for people with SCD.

Analyzing the nexus between Chinese industrial policy and cross-border M&As.

In recent years, as China experiences economic expansion and its corporations become more global, it has notably become a central hub for cross-border mergers and acquisitions (M&A) on the world stage. The Chinese government, in tandem, leverages these international M&A operations to drive industrial transformation and progress in technology. This research investigates the role of China's industrial policies in shaping cross-border M&A activities by examining recent instances. Findings indicate that relaxing financial barriers and applying specific industrial tactics bolster companies' abilities to secure funding, consequently energizing cross-border M&A initiatives. Several firms in these international mergers and acquisitions are intricately connected to political strategies, markedly affecting the formulation of industrial policies. This assertion is corroborated through the analysis of relevant statistical evidence. The study methodically collects and scrutinizes data to quantitatively depict the current landscape and influencing elements of cross-border M&A, thus providing concrete evidence for policy and business strategy formulation.

Resting state MRI reveals spontaneous physiological fluctuations in the kidney and tracks diabetic nephropathy in rats.

The kidneys maintain fluid-electrolyte balance and excrete waste in the presence of constant fluctuations in plasma volume and systemic blood pressure. The kidneys perform these functions to control capillary perfusion and glomerular filtration by modulating the mechanisms of autoregulation. An effect of these modulations are spontaneous, natural fluctuations in nephron perfusion. Numerous other mechanisms can lead to fluctuations in perfusion and flow. The ability to monitor these spontaneous physiological fluctuations in vivo could facilitate the early detection of kidney disease. The goal of this work was to investigate the use of resting- state magnetic resonance imaging (rsMRI) to detect spontaneous physiological fluctuations in the kidney. We performed rsMRI of rat kidneys in vivo over 10 minutes, applying motion correction to resolve time series in each voxel. We observed spatially variable, spontaneous fluctuations in rsMRI signal between 0-0.3 Hz, in frequency bands also associated with autoregulatory mechanisms. We further applied rsMRI to investigate changes in these fluctuations in a rat model of diabetic nephropathy. Spectral analysis was performed on time series of rsMRI signal in kidney cortex and medulla. Power from spectra in specific frequency bands from kidney cortex correlated with severity of glomerular pathology caused by diabetic nephropathy. Finally, we investigated the feasibility of using rsMRI of the human kidney in two participants, observing the presence of similar, spatially variable fluctuations. This approach may enable a range of preclinical and clinical investigations of kidney function, and facilitate the development of new therapies to improve outcomes in patients with kidney disease.

PHF19 before and post induction treatment possess favorable potency of reflecting treatment response to protease inhibitors, event-free survival, and overall survival in multiple myeloma patients.

OBJECTIVE: Plant homeodomain finger protein 19 (PHF19) regulates hematopoietic stem cell differentiation and promotes multiple myeloma (MM) progression. This study intended to explore the potency of PHF19 at baseline and post induction treatment in estimating treatment response to protease inhibitors and survival in MM patients. METHODS: This retrospective study screened 69 MM patients who received protease inhibitors with bone marrow (BM) samples available at both baseline and post induction treatment. Twenty healthy BM donors were included as healthy controls (HCs). PHF19 in plasma cells from BM was quantified by reverse transcription-quantitative polymerase chain reaction. RESULTS: PHF19 at baseline and post induction treatment in MM patients were increased than in HCs. In MM patients, PHF19 was declined post induction treatment. Elevated PHF19 at baseline and post induction treatment were correlated with renal impairment, beta-2-microglobulin ≥5.5 mg/L, t (4; 14), higher international staging system (ISS) stage, and higher revised ISS (R-ISS) stage. Concerning treatment response, PHF19 at baseline and post induction treatment were negatively associated with complete response and overall response rate. Notably, abnormal PHF19 (above 95% quantile value of PHF19 in HCs) at baseline and post induction treatment were linked with shortened event-free survival (EFS) and overall survival (OS). After adjustment, abnormal PHF19 post induction treatment was independently related to shortened EFS (hazard ratio = 2.474) and OS (hazard ratio = 3.124). CONCLUSION: PHF19 is aberrantly high and declines post induction therapy, which simultaneously reflects unfavorable treatment response to protease inhibitors as well as shorter EFS and OS in MM patients.

Comparison of cerebral oxygen extraction fraction using ASE and TRUST methods in patients with sickle cell disease and healthy controls.

Abnormal oxygen extraction fraction (OEF), a putative biomarker of cerebral metabolic stress, may indicate compromised oxygen delivery and ischemic vulnerability in patients with sickle cell disease (SCD). Elevated OEF was observed at the tissue level across the brain using an asymmetric spin echo (ASE) MR method, while variable global OEFs were found from the superior sagittal sinus (SSS) using a T2-relaxation-under-spin-tagging (TRUST) MRI method with different calibration models. In this study, we aimed to compare the average ASE-OEF in the SSS drainage territory and TRUST-OEF in the SSS from the same SCD patients and healthy controls. 74 participants (SCD: N = 49; controls: N = 25) underwent brain MRI. TRUST-OEF was quantified using the Lu-bovine, Bush-HbA and Li-Bush-HbS models. ASE-OEF and TRUST-OEF were significantly associated in healthy controls after controlling for hematocrit using the Lu-bovine or the Bush-HbA model. However, no association was found between ASE-OEF and TRUST-OEF in patients with SCD using either the Bush-HbA or the Li-Bush-HbS model. Plausible explanations include a discordance between spatially volume-averaged oxygenation brain tissue and flow-weighted volume-averaged oxygenation in SSS or sub-optimal calibration in SCD. Further work is needed to refine and validate non-invasive MR OEF measurements in SCD.

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). PURPOSE: This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT. METHODS: Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI-Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave-One-Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole-heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), and multiscale structural similarity (MS-SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD. RESULTS: For the whole-heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877 mm; and 3) Mean 95% HD dropped from 10.236 to 7.700 mm. For the whole-body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS-SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart. CONCLUSION: CycleGAN-generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.

Effects of a brief HIIT intervention on cognitive performance in older women.

Cardiorespiratory fitness (CRF) mitigates age-related decline in cognition and brain volume. Little is known, however, about the effects of high-intensity interval training (HIIT) on cognitive aging and the relationship between HIIT, cognition, hippocampal subfield volumes, and cerebral oxygen extraction fraction (OEF). Older sedentary women participated in an 8-week HIIT intervention. We conducted cognitive assessments, fitness assessments (VO2max), MRI scans: asymmetric spin echo oxygen extraction fraction (ASE-OEF), high-resolution multiple image co-registration and averaging (HR-MICRA) imaging, and transcranial Doppler ultrasonography before and after the intervention. VO2max increased from baseline (M = 19.36, SD = 2.84) to follow-up (M = 23.25, SD = 3.61), Z = - 2.93, p < .001, r = 0.63. Composite cognitive (Z = - 2.05, p = 0.041), language (Z = - 2.19, p = 0.028), and visuospatial memory (Z = - 2.22, p = 0.026), z-scores increased significantly. Hippocampal subfield volumes CA1 and CA3 dentate gyrus and subiculum decreased non-significantly (all p > 0.05); whereas a significant decrease in CA2 (Z = - 2.045, p = 0.041, r = 0.436) from baseline (M = 29.51; SD = 24.50) to follow-up (M = 24.50; SD = 13.38) was observed. Right hemisphere gray matter was correlated with language z-scores (p = 0.025; r = 0.679). The subiculum was correlated with attention (p = 0.047; r = 0.618) and verbal memory (p = 0.020; r = 0.700). The OEF and CBF were unchanged at follow-up (all p > .05). Although we observed cognitive improvements following 8 weeks of our HIIT intervention, they were not explained by hippocampal, OEF, or CBF changes.

An automatic pipeline for PET/MRI attenuation correction validation in the brain.

PURPOSE: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity. METHODS: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images. RESULTS: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs. CONCLUSION: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

Tuning Intrinsic Spin Hall Effect in Platinum/Ferrimagnetic Insulator Heterostructure in Moderately Dirty Regime.

Studying the mechanisms of the spin Hall effect (SHE) is essential for the fundamental understanding of spintronic physics. By now, despite the intensive studies of SHE on heavy metal (HM)/metallic magnet heterostructures, the SHE on HM/ferrimagnetic insulator (FMI) heterostructures still remains elusive. Here, we study the mechanism of SHE in the Pt/Tm3Fe5O12 (TmIG) heterostructure. We first tune the crystallinity and resistivity of Pt by an annealing method, and then study the spin-orbit torque (SOT) in the tuned-Pt/TmIG devices. The SOT generation efficiency per unit electric field and spin Hall angle were obtained, which are insensitive to the annealing temperature. We further demonstrate that the intrinsic contribution in the moderately dirty regime is responsible for the SHE in our Pt/TmIG bilayer. Our study provides an important piece of information for the SHE in FMI-based spintronic physics.

The influence mechanism of industrial policies on Chinese companies' cross-border M&A decision-making.

This study explored the potential links between Chinese industrial policy and cross-border mergers and acquisitions by Chinese firms from 2009 to 2019. Based on describing China's industrial strategy and evaluating the then-current situation of Chinese enterprises' cross-border mergers and acquisitions, this empirical study constructed a two-way fixed-effect panel model and an intermediate effect model to assess the mechanism of industrial policy's influence on Chinese enterprises' cross-border mergers and acquisitions decisions. The findings were as follows: (1) Industrial policy could promote the implementation of cross-border mergers and acquisitions of Chinese enterprises; (2) By easing financial restrictions, industrial policy could improve firms' access to capital and encourage cross-border mergers and acquisitions. (3) Industrial policies could promote the high political relevance of state-owned enterprises, thus promoting the success of transnational mergers and acquisitions of enterprises. Therefore, it was significant to promote the transformation of industrial policy from subsidy-oriented to performance-oriented and rationally evaluate the risks and benefits of M&A for enterprises to complete cross-border M&A.

White matter hyperintensity longitudinal morphometric analysis in association with Alzheimer disease.

INTRODUCTION: Vascular damage in Alzheimer's disease (AD) has shown conflicting findings particularly when analyzing longitudinal data. We introduce white matter hyperintensity (WMH) longitudinal morphometric analysis (WLMA) that quantifies WMH expansion as the distance from lesion voxels to a region of interest boundary. METHODS: WMH segmentation maps were derived from 270 longitudinal fluid-attenuated inversion recovery (FLAIR) ADNI images. WLMA was performed on five data-driven WMH patterns with distinct spatial distributions. Amyloid accumulation was evaluated with WMH expansion across the five WMH patterns. RESULTS: The preclinical group had significantly greater expansion in the posterior ventricular WM compared to controls. Amyloid significantly associated with frontal WMH expansion primarily within AD individuals. WLMA outperformed WMH volume changes for classifying AD from controls primarily in periventricular and posterior WMH. DISCUSSION: These data support the concept that localized WMH expansion continues to proliferate with amyloid accumulation throughout the entirety of the disease in distinct spatial locations.

Impact of inherent energy barrier on spin-orbit torques in magnetic-metal/semimetal heterojunctions.

Spintronic devices are based on heterojunctions of two materials with different magnetic and electronic properties. Although an energy barrier is naturally formed even at the interface of metallic heterojunctions, its impact on spin transport has been overlooked. Here, using diffusive spin Hall currents, we provide evidence that the inherent energy barrier governs the spin transport even in metallic systems. We find a sizable field-like torque, much larger than the damping-like counterpart, in Ni81Fe19/Bi0.1Sb0.9 bilayers. This is a distinct signature of barrier-mediated spin-orbit torques, which is consistent with our theory that predicts a strong modification of the spin mixing conductance induced by the energy barrier. Our results suggest that the spin mixing conductance and the corresponding spin-orbit torques are strongly altered by minimizing the work function difference in the heterostructure. These findings provide a new mechanism to control spin transport and spin torque phenomena by interfacial engineering of metallic heterostructures.

The Role of Job Insecurity, Social Media Exposure, and Job Stress in Predicting Anxiety Among White-Collar Employees.

Purpose: The purpose of this study is to examine the combined effects and relationships between social media exposure, job insecurity, job stress, and anxiety among individuals and to propose an innovative model exploring how these factors contribute to increased anxiety. Patients and Methods: This empirical research paper focuses on understanding the role of job insecurity, social media exposure, and job stress in predicting anxiety levels. The study was conducted on a sample of 292 white-collar employees in various organizations and institutions across the United States amid the COVID-19 pandemic and the ongoing economic crisis, during the broader transition to a digital working environment. A self-report Likert-type questionnaire was administered to measure employees' job stress, uncertainty, anxiety levels and social media exposure. The present study employed theoretical background of Lazarus' Theory of Psychological Stress and the JDR Model. Structural equation modeling (SEM) was used to examine the relationships between these constructs, while confirmatory factor analysis (CFA) was conducted to assess the validity of the measurement model. Results: The study provides empirical support for the claim that employees with pervasive job stress will likely develop anxiety symptoms. It also highlights the mechanisms by which social media exposure increases employees' anxiety levels and how management and policymakers can buffer the stressors. Conclusion: The research emphasizes the importance of addressing occupational mental health problems, and the implications of the findings indicate the need for managerial interventions in securing effective measures for buffering stress and controlled social media usage. This study contributes to the body of knowledge by informing managers and policymakers on key aspects to consider in promoting psychological balance and a healthy organizational climate.

Synthetic PET via Domain Translation of 3-D MRI.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

Spatiotemporal Mapping of Local Heterogeneities during Electrochemical Carbon Dioxide Reduction.

The activity and selectivity of a copper electrocatalyst during the electrochemical CO2 reduction reaction (eCO2RR) are largely dominated by the interplay between local reaction environment, the catalyst surface, and the adsorbed intermediates. In situ characterization studies have revealed many aspects of this intimate relationship between surface reactivity and adsorbed species, but these investigations are often limited by the spatial and temporal resolution of the analytical technique of choice. Here, Raman spectroscopy with both space and time resolution was used to reveal the distribution of adsorbed species and potential reaction intermediates on a copper electrode during eCO2RR. Principal component analysis (PCA) of the in situ Raman spectra revealed that a working electrocatalyst exhibits spatial heterogeneities in adsorbed species, and that the electrode surface can be divided into CO-dominant (mainly located at dendrite structures) and C-C dominant regions (mainly located at the roughened electrode surface). Our spectral evaluation further showed that in the CO-dominant regions, linear CO was observed (as characterized by a band at ∼2090 cm-1), accompanied by the more classical Cu-CO bending and stretching vibrations located at ∼280 and ∼360 cm-1, respectively. In contrast, in the C-C directing region, these three Raman bands are suppressed, while at the same time a band at ∼495 cm-1 and a broad Cu-CO band at ∼2050 cm-1 dominate the Raman spectra. Furthermore, PCA revealed that anodization creates more C-C dominant regions, and labeling experiments confirmed that the 495 cm-1 band originates from the presence of a Cu-C intermediate. These results indicate that a copper electrode at work is very dynamic, thereby clearly displaying spatiotemporal heterogeneities, and that in situ micro-spectroscopic techniques are crucial for understanding the eCO2RR mechanism of working electrocatalyst materials.

Giant Orbital-to-Spin Conversion for Efficient Current-Induced Magnetization Switching of Ferrimagnetic Insulator.

It has long been believed that the attachment of two heavy metals such as Ta and Pt with opposite spin Hall angles results in a weakened net torque generation efficiency in magnetization switching devices. Here, we report a giant orbital-to-spin conversion in Ta/Pt/Tm3Fe5O12 (TmIG) heterostructures. We show that the torque generation efficiency is enhanced by an order of magnitude in the Ta/Pt/TmIG trilayer compared to that in the Pt/TmIG bilayer. This enhancement is further evidenced by the fact that the critical current density for the magnetization switching of the Ta/Pt/TmIG is an order of magnitude smaller than that of the Pt/TmIG. It is found that the orbital current generated from Ta through the orbital Hall effect (OHE) is converted to the spin current in the interior of Pt. Our discovery offers an extraordinary approach to enhance the torque generation for magnetization switching of insulators and provides an important piece of information for orbitronics.

An automatic pipeline for PET/MRI attenuation correction validation in the brain.

Purpose: PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches. Methods: The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework. The synthetic lesion insertion tool is used to insert simulated spherical, and brain regions of interest (ROI) into the PET projection space and reconstructed with four different PET MRAC techniques, while FreeSurfer is used to generate brain ROIs from T1 weighted MRI image. Using a cohort of 11 patients' brain PET dataset, the quantitative accuracy of four MRAC(s), which are: DIXON AC, DIXONbone AC, UTE AC, and Deep learning trained with DIXON AC, named DL-DIXON AC, were compared to the PET-based CT attenuation correction (PET CTAC). MRAC to CTAC activity bias in spherical lesions and brain ROIs were reconstructed with and without background activity and compared to the original PET images. Results: The proposed pipeline provides accurate and consistent results for inserted spherical lesions and brain ROIs inserted with and without considering the background activity and following the same MRAC to CTAC pattern as the original brain PET images. As expected, the DIXON AC showed the highest bias; the second was for the UTE, then the DIXONBone, and the DL-DIXON with the lowest bias. For simulated ROIs inserted in the background activity, DIXON showed a -4.65% MRAC to CTAC bias, 0.06% for the DIXONbone, -1.70% for the UTE, and - 0.23% for the DL-DIXON. For lesion ROIs inserted without background activity, DIXON showed a -5.21%, -1% for the DIXONbone, -2.55% for the UTE, and - 0.52 for the DL-DIXON. For MRAC to CTAC bias calculated using the same 16 FreeSurfer brain ROIs in the original brain PET reconstructed images, a 6.87% was observed for the DIXON, -1.83% for DIXON bone, -3.01% for the UTE, and - 0.17% for the DL-DIXON. Conclusion: The proposed pipeline provides accurate and consistent results for synthetic spherical lesions and brain ROIs inserted with and without considering the background activity; hence a new attenuation correction approach can be evaluated without using measured PET emission data.

Toward Automated Detection of Silent Cerebral Infarcts in Children and Young Adults With Sickle Cell Anemia.

BACKGROUND: Silent cerebral infarcts (SCI) in sickle cell anemia (SCA) are associated with future strokes and cognitive impairment, warranting early diagnosis and treatment. Detection of SCI, however, is limited by their small size, especially when neuroradiologists are unavailable. We hypothesized that deep learning may permit automated SCI detection in children and young adults with SCA as a tool to identify the presence and extent of SCI in clinical and research settings. METHODS: We utilized UNet-a deep learning model-for fully automated SCI segmentation. We trained and optimized UNet using brain magnetic resonance imaging from the SIT trial (Silent Infarct Transfusion). Neuroradiologists provided the ground truth for SCI diagnosis, while a vascular neurologist manually delineated SCI on fluid-attenuated inversion recovery and provided the ground truth for SCI segmentation. UNet was optimized for the highest spatial overlap between automatic and manual delineation (dice similarity coefficient). The optimized UNet was externally validated using an independent single-center prospective cohort of SCA participants. Model performance was evaluated through sensitivity and accuracy (%correct cases) for SCI diagnosis, dice similarity coefficient, intraclass correlation coefficient (metric of volumetric agreement), and Spearman correlation. RESULTS: The SIT trial (n=926; 31% with SCI; median age, 8.9 years) and external validation (n=80; 50% with SCI; age, 11.5 years) cohorts had small median lesion volumes of 0.40 and 0.25 mL, respectively. Compared with the neuroradiology diagnosis, UNet predicted SCI presence with 100% sensitivity and 74% accuracy. In magnetic resonance imaging with SCI, UNet reached a moderate spatial agreement (dice similarity coefficient, 0.48) and high volumetric agreement (intraclass correlation coefficient, 0.76; ρ=0.72; P<0.001) between automatic and manual segmentations. CONCLUSIONS: UNet, trained using a large pediatric SCA magnetic resonance imaging data set, sensitively detected small SCI in children and young adults with SCA. While additional training is needed, UNet may be integrated into the clinical workflow as a screening tool, aiding in SCI diagnosis.

Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system.

BACKGROUND: MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. PURPOSE: We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. METHODS: Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. RESULTS: Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p < 0.0001) and smaller RMSEs (p < 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring > 8 out of 10), followed by P2P200 (scoring > 5 out of 10), and then motion-uncorrected (scoring < 3 out of 10) in sharpness, contrast, and artifact-freeness. CONCLUSIONS: We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.