Citations

AbstractBackgroundThe imaging of patients with implanted electrically‐conductive devices via magnetic resonance imaging at ultra‐high fields is hampered by uncertainties relating to the potential for inducing tissue heating adjacent to the implant due to coupling of energy from the incident electromagnetic field into the implant. Existing data in the peer‐reviewed literature of comparisons across field strengths of tissue heating and its surrogate, the specific absorption rate (SAR), is scarce and contradictory, leading to further doubts pertaining to the safety of imaging patients with such devices.PurposeThe radiofrequency‐induced SAR adjacent to orthopedic screws of varying length and at frequencies of 64 to 498 MHz was investigated via full‐wave electromagnetic simulations, to provide an accurate comparison of SAR across MRI field strengths.MethodsDipole antennas were used for RF transmission to achieve a uniform electric field tangential to the screws located 120 mm above the antenna midpoints, embedded in a bone‐mimicking material. The input power to the antennas was constrained to achieve the following targets without the screw present: (i) E = 100 V/m, (ii) B1+ = 2 μT, and (iii) global‐average‐SAR = 3.2 W/kg. Simulations were performed with a spatial resolution of 0.2 mm in the volume surrounding the screws, resulting in 76–137 MCells, noting the maximum 1 g‐averaged SAR value in each case. Simulations were repeated at 128 and 297 MHz for screws embedded in muscle tissue.ResultsThe peak SAR, occurring at the resonant screw length, substantially increased as the frequency decreased when the input power to the dipole antenna was constrained to achieve constant electric field in background tissue at the screws’ locations. A similar pattern was observed when constraining input power to achieve constant B1+ and global‐average‐SAR. The dielectric properties of the tissue in which the screws were embedded dominated the SAR comparisons between 297 and 128 MHz.ConclusionsThe study design allowed for a direct comparison to be performed of SAR across frequencies and implant lengths without the confounding effect of variable incident electric field. Lower frequencies produced substantially larger SAR values for implants approaching the resonant length for the worst‐case uniform incident electric field along the screws’ length. The data may inform risk‐benefit assessments for imaging patients with orthopedic implants at the new clinical field strength of 7 Tesla.

Current management of patients with congenital heart disease has increased their survival into adulthood. This is accompanied by potential cardiac complications, including pulmonary hypertension associated with congenital heart disease (PAH-CHD). PAH-CHD constitutes a challenging subgroup of pulmonary hypertension and requires expert management to improve quality of life and prognosis. Novel agents have shown a significant improvement in morbidity and mortality in patients with pulmonary arterial hypertension. However, the long-term effects of these medications on PAH-CHD patients remain somewhat uncertain, necessitating treatment plans largely founded on the clinical experience of the healthcare providers. The aim of this review is to summarize the current evidence and future perspectives regarding treatment strategies for PAH-CHD to help better guide management of this complex disease.

Importance Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.

AbstractPurpose has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging–Dynamic Contrast‐Enhanced (OSIPI‐DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement.MethodsA framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components.ResultsAcross the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0–1 = lowest–highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility.ConclusionsThis study reports results from the OSIPI‐DCE challenge and highlights the high inter‐software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real‐world clinical setting, many of these tools may perform differently with different benchmarking methodology.

AbstractSenescent cells compromise tissue structure and function in older organisms. We recently identified senescent fibroadipogenic progenitors (FAPs) with activated chemokine signaling pathways in the skeletal muscle of old mice, and hypothesized these cells may contribute to the age‐associated accumulation of immune cells in skeletal muscle. In this study, through cell–cell communication analysis of skeletal muscle single‐cell RNA‐sequencing data, we identified unique interactions between senescent FAPs and macrophages, including those mediated by Ccl2 and Spp1. Using mouse primary FAPs in vitro, we verified increased expression of Ccl2 and Spp1 and secretion of their respective proteins in the context of both irradiation‐ and etoposide‐induced senescence. Compared to non‐senescent FAPs, the medium of senescent FAPs markedly increased the recruitment of macrophages in an in vitro migration assay, an effect that was mitigated by preincubation with antibodies to either CCL2 or osteopontin (encoded by Spp1). Further studies demonstrated that the secretome of senescent FAPs promotes polarization of macrophages toward an M2 subtype. These data suggest the unique secretome of senescent FAPs may compromise skeletal muscle homeostasis by recruiting and directing the behavior of macrophages.

AbstractPurposeThe inflow‐based vascular‐space‐occupancy (iVASO) MRI was originally developed in a single‐slice mode to measure arterial cerebral blood volume (CBVa). When vascular crushers are applied in iVASO, the signals can be sensitized predominantly to small pial arteries and arterioles. The purpose of this study is to perform a systematic optimization and evaluation of a 3D iVASO sequence on both 3 T and 7 T for the quantification of CBVa values in the human brain.MethodsThree sets of experiments were performed in three separate cohorts. (1) 3D iVASO MRI protocols were compared to single‐slice iVASO, and the reproducibility of whole‐brain 3D iVASO MRI was evaluated. (2) The effects from different vascular crushers in iVASO were assessed. (3) 3D iVASO MRI results were evaluated in arterial and venous blood vessels identified using ultrasmall‐superparamagnetic‐iron‐oxides–enhanced MRI to validate its arterial origin.Results3D iVASO scans showed signal‐to‐noise ratio (SNR) and CBVa measures consistent with single‐slice iVASO with reasonable intrasubject reproducibility. Among the iVASO scans performed with different vascular crushers, the whole‐brain 3D iVASO scan with a motion‐sensitized‐driven‐equilibrium preparation with two binomial refocusing pulses and an effective TE of 50 ms showed the best suppression of macrovascular signals, with a relatively low specific absorption rate. When no vascular crusher was applied, the CBVa maps from 3D iVASO scans showed large CBVa values in arterial vessels but well‐suppressed signals in venous vessels.ConclusionA whole‐brain 3D iVASO MRI scan was optimized for CBVa measurement in the human brain. When only microvascular signals are desired, a motion‐sensitized‐driven‐equilibrium–based vascular crusher with binomial refocusing pulses can be applied in 3D iVASO.

AbstractBackgroundRecently the US Food and Drug Administration has granted variances to select blood centers to supply cold‐stored platelet components (CSP). In hemorrhage resuscitation warming of blood components with approved fluid warming devices is common.Study Design and MethodsPathogen‐reduced apheresis platelet units were collected and stored in one of two ways: (1) CSP‐I, (2) CSP‐D. CSP‐I were collected and immediately stored at 1–6°C until used. CSP‐D were collected and stored at 20–24°C for 5 days and transferred to storage at 1–6°C until use. Aggregometry using arachidonic acid (AA), adenosine diphosphate (ADP) and collagen as agonists was performed on the unit samples before and after the units were infused through a Ranger blood‐warming device.ResultsCSP‐I, 23 units, had very high aggregation responses to all agonists (all ≥47.6 ± 20.7). There was a statistically significant reduction in ADP‐induced aggregometry results from 55.1 ± 23.2 before compared to 33.5 ± 14.6 following infusion of the PLT through the blood warmer (p < .001). There were no differences in AA and collagen aggregometry results before and after the infusion of the platelets through the blood warmer. CSP‐D had 5 of the 15 units with visible clotting in the bag. The 10 CSP‐Ds studied had lower aggregation than all agonists before and after infusion through the blood‐warming device (all ≤49.9 ± 35.9).ConclusionWe detected a statistically significant reduction in ADP‐induced aggregometry in CSP‐I run through a Ranger blood‐warming device with no change with AA or collagen agonist aggregometry.

Objective Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. Methods PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. Results Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655–0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882–0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684–0.723, I² = 97.5%, p < 0.01). Conclusions The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.