Dave, Jaydev K.

Abstract Objective . The use of Feraheme (ferumoxytol) as a contrast agent in MR-guided focused ultrasound (MRgFUS) tumor ablations could enhance treatment safety and efficacy by improving vascular visualization through strong T1 and T2 shortening effects. However, Feraheme impact on MR thermometry and tissue heating is unclear. This study evaluates the feasibility of incorporating Feraheme into MRgFUS by assessing its impact on MR imaging contrast, MR thermometry, and thermal response in tissue-mimicking phantoms. Approach . Tissue-mimicking phantoms with 0 (baseline), 0.25, and 0.5 mM Feraheme concentrations were prepared using a validated recipe. Acoustic attenuation was measured with a commercial diagnostic ultrasound scanner. MR imaging at 1.5 T was performed to assess gradient-recalled-echo (GRE)-based thermometry signal-to-noise-ratio (SNR), relaxation rates, and relaxivities. MRgFUS system integrated with 1.5 T MRI was used to perform multiple sonications in the phantoms. Temperature elevations and focal heating areas were quantified from proton resonance frequency (PRF) shift thermometry. Experiments were repeated three times over the period of eight-weeks. Separately, temperature-dependent nuclear magnetic resonance (NMR) spectroscopy was performed at 3.0 T in saline-diluted Feraheme phantoms (0, 0.25, 0.5, and 1.0 mM) to assess the influence of Feraheme concentration on PRF coefficients used in MR thermometry. Main results . Phantoms showed nuclear relaxation times ( T 1: 1178.1 ± 77.8, T 2: 103.21 ± 2.45 ms) and attenuation coefficients (0.34–0.36 dB cm −1 MHz −1 ) within expected biological tissue ranges. Relaxivities demonstrated a strong linear dependence on Feraheme concentration ( r 1: 17.5 ± 0.2, r 2: 209.2 ± 2.6 s −1 mM −1 ). Relative to baseline (0 mM) phantom, the SNR of GRE-based thermometry increased by 12.6% at 0.25 mM and decreased by 27.1% at 0.5 mM. Sonications in Feraheme-containing phantoms resulted in statistically higher temperature elevations ( p < 0.001 ) although no statistically significant difference was observed between 0.25 and 0.5 mM. All NMR measurements demonstrated stable, linear PRF temperature dependence ( R 2 = 0.996–0.999), with PRF coefficients (between −0.0074 and −0.0082 ppm °C −1 ). The resulting thermometry bias was small and predictable (+0.67 and +1.73 per 20 °C at 3.0 T for 0.25 and 0.5 mM). Significance . These findings demonstrate that MR thermometry remains feasible at moderate Feraheme concentrations and support its potential use to enhance visualization of critical vasculature near targeted tissues during MRgFUS.

Objectives Uniformity artifacts caused by defective transducer elements or scanner malfunctions degrade diagnostic image quality. Traditional quality control (QC) methods, such as phantom testing and visual or quantitative image analysis, can be labor‐intensive and limited in test frequency. This study aims to develop a deep learning framework to detect uniformity artifacts and complement traditional QC. Methods Clinical median images were generated by aggregating co‐registered grayscale ultrasound images acquired by each transducer and computing median intensity values across the image stack. A pretrained ResNet‐18 model was fine‐tuned on a dataset consisting of clinical median images from linear and curvilinear transducers. The dataset was divided into training, validation, and testing subsets, ensuring no overlap between training and test transducers. To assess generalizability, the model was also evaluated on an independent test set of 396 phantom‐validated images from linear and curvilinear transducers. Results The model achieved 100% accuracy on the first dataset's test set. On the independent test set, it attained 87.4% accuracy with high sensitivity (0.84) and specificity (0.88), demonstrating robust generalization. Occlusion sensitivity maps confirmed the model's attention to uniformity artifact regions. Conclusion The deep learning framework using clinical median images demonstrated robust performance across several linear and curvilinear transducer models. It could be integrated into the clinical QC workflow by automating artifact detection in an effective and timely manner. In our practice, it can flag median images classified as artifact‐present, reducing human review time by approximately 80% while preserving detection accuracy.

Introduction Reverse shoulder arthroplasty (RSA) is performed for various shoulder pathologies. Inadequate tensioning of the deltoid muscle can lead to complications, highlighting the need for optimization. Preoperative quantification of deltoid health may improve operative execution and outcomes. Shear wave elastography (SWE) quantifies stiffness as a marker for muscle health and tensioning. This study establishes normal reference values for the deltoid in healthy individuals. Methods 40 participants were enrolled, 10 in each 18–30, 31–40, 41–55, and 55 + age groups. Two operators performed SWE in 3 deltoid regions, and the intra- and inter-operator agreement was assessed. Results Mean values were 23.2 ± 4.6, 26.4 ± 5.6, and 17.9 ± 5.2 kPa for the anterior, lateral, and posterior regions. The posterior region did not show any age dependency. However, there were significant differences between the age groups of 18–30 and 55 + years in the anterior (19.7 ± 3.7 vs. 26.7 ± 5.3 kPa) and lateral (22.0 ± 5.4 vs. 30.2 ± 8.8 kPa). Intra-operator reliability ranged from 0.45 to 0.60, and inter-reader agreement from 0.57 to 0.85. Conclusion This study provides reference values of deltoid stiffness which could serve as a comparison with clinical measurements for RSA operative planning and execution and prediction of surgical outcomes.

Subharmonic aided pressure estimation (SHAPE) is a technique that utilizes subharmonic signals from microbubble contrast agents for pressure estimation. Validation of the SHAPE technique relies on synchronous measurements of in vivo pressures using contrast microbubbles and a pressure catheter (reference standard). For the guidance and placement of pressure catheter in vivo, iodinated contrast is used with fluoroscopy. Therefore, during data acquisition for validation studies of the SHAPE technique, both contrast microbubbles and iodinated contrast are present simultaneously within the vasculature. This study aims to elucidate the effects of iodinated contrast (Visipaque, GE HealthCare) on subharmonic signal amplitude from contrast microbubbles (Definity, Lantheus Medical Imaging, Inc.). In an acrylic water tank, 0.06 mL of Definity and varied amounts of Visipaque (0.14, 0.43, 0.85, and 1.70 mL) were added to 425 mL of deionized water. Ultrasound scanning was performed with a SonixTablet scanner (BK Medical Systems) using optimized parameters for SHAPE with Definity ( f transmit/receive = 3.0/1.5 MHz; chirp down pulse). Subharmonic data was acquired and analyzed at 9 different incident acoustic outputs ( n = 3). Results showed an increase in subharmonic signal amplitude from Definity microbubbles in the presence of 0.14 mL Visipaque by 2.8 ± 1.3 dB ( p < .001), no change with 0.85 mL Visipaque (0.7 ± 1.2 dB; p = .09) and a decrease in subharmonic amplitude in the presence of 1.70 mL Visipaque by 1.9 ± 0.7 dB ( p < .001). While statistically significant effect on subharmonic signal amplitude of Definity microbubbles was noted due to the mixture, the magnitude of the effect was minimal (~2.8 dB) and unlikely to impact in vivo SHAPE measurements.

Purpose: To compare left-ventricular and aortic pressures obtained using fluid-filled and high-fidelity solid-state pressure catheters in subjects undergoing left heart catheterization. Methods: Twenty subjects scheduled for a left heart catheterization were enrolled and 18 subjects completed this IRB-approved study. During catheterization, left-ventricular (LV) and aortic (AO) pressures were obtained using a fluid-filled pressure catheter (standard-of-care) and a high-fidelity solid-state pressure catheter synchronously. The pressure tracings were analyzed by two independent readers to measure LV systolic (LVSP), LV minimum diastolic (LVMDP), LV end diastolic (LVEDP), AO systolic (AOSP), and AO diastolic (AODP) pressures. Derivatives of the pressure waveforms were computed to estimate the isovolumic contraction and relaxation rates (peak ± dp/dt). The measurements were made over multiple cardiac cycles and averaged results were used for comparisons using repeated measures analysis of variance, post-hoc tests with Bonferroni corrections and Bland-Altman plots. Results: A significant main effect of the pressure catheter was noted for LVSP, LVMDP and AOSP (p < 0.025). The LVSP and AOSP measured with fluid-filled pressure catheters were higher by 6.6 ± 6.9 mmHg and 4.6 ± 5.2 mmHg in comparison to the high-fidelity solid-state pressure catheter assessments. In contrast, the LVMDP measurements were 3.5 ± 5.7 mmHg lower than the solid-state pressure catheter measurements. When evaluating the differences between readers, the maximum difference was noted for LVEDP measurements (3.1 ± 2.4 mmHg; p ≤ 0.001). The differences in isovolumic contraction rate (66.4 ± 116.0 mmHg/s) and relaxation rate (60.5 ± 113.5 mmHg/s) were not significantly different between the two catheter systems after Bonferroni corrections for multiple comparisons (p > 0.06). Bland-Altman analysis revealed a bias ranging from 0.8 to 6.6 mmHg for all the pressure measurements. Conclusions: There are differences in LVSP, LVMDP, and AOSP measured using fluid-filled and high-fidelity solid-state pressure catheters. Clinicians should be aware that the fluid-filled catheters commonly overestimate the true systolic pressures in the left ventricle and aorta.

AbstractReject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms. Due to the lack of standardization, reject data often cannot be easily compared between radiography systems from different vendors. The purpose of this report is to provide guidance to help standardize data elements that are required for comprehensive reject analysis and to propose data reporting and workflows to enable an effective and comprehensive reject rate monitoring program. Essential data elements, a proposed schema for classifying reject reasons, and workflow implementation options are recommended in this task group report.

PurposeWe summarize the AAPM TG248 Task Group report on interoperability assessment for the commissioning of medical imaging acquisition systems in order to bring needed attention to the value and role of quality assurance testing throughout the imaging chain.MethodsTo guide the clinical physicist involved in commissioning of imaging systems, we describe a framework and tools for incorporating interoperability assessment into imaging equipment commissioning.ResultsWhile equipment commissioning may coincide with equipment acceptance testing, its scope may extend beyond validation of product or purchase specifications. Equipment commissioning is meant to provide assurance that a system is ready for clinical use, and system interoperability plays an essential role in the clinical use of an imaging system.ConclusionThe functionality of a diagnostic imaging system extends beyond the acquisition console and depends on interoperability with a host of other systems such as the Radiology Information System, a Picture Archive and Communication System, post‐processing software, treatment planning software, and clinical viewers.