MRI in Adult Patients with Active and Inactive Implanted MR-conditional, MR-nonconditional, and Other Devices.

Active implanted medical devices (AIMDs) enable therapy and patient monitoring by way of electrical activity and typically have a battery and electrical leads. The most common types of AIMDs include cardiac implantable electronic devices (CIEDs), spinal cord stimulators, deep brain stimulators, bone growth or fusion stimulators, other neurostimulators, and drug infusion pumps. As more patients with AIMDs undergo MRI, it is important to consider the safety of patients who have these implanted devices during MRI. The authors review the physics concepts related to MRI safety, such as peak spatial gradient magnetic field, specific absorption rate, root mean square value of the effective magnetic component of the transmitted RF pulse, and gradient slew rate, as well as the parameters necessary to remain within safety limits. The roles of MRI safety personnel, as set forth by the International Society of Magnetic Resonance in Medicine, are emphasized. In addition, the relevant information provided in vendor manuals is reviewed, with a focus on how to obtain relevant up-to-date information. The radiologist should be able to modify protocols to meet safety requirements, address possible alternatives to MRI, and weigh the potential benefits of MRI against the potential risks. A few more advanced topics, such as fractured or abandoned device leads and patients with multiple implanted medical devices, also are addressed. Recommended workflows for MRI in patients with implanted medical devices are outlined. It is important to implement an algorithmic MRI safety process, including a review of the MRI safety information; patient screening; optimal imaging; and monitoring patients before, during, and after the examination. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Shetty et al in this issue.

Time-Dependent Change in Carpal Tunnel Cross-Sectional Area and Transverse Carpal Ligament Thickness Using Serial Magnetic Resonance Imaging Studies-An Anatomical Study.

PURPOSE: Idiopathic carpal tunnel syndrome (CTS) is a common compressive neuropathy. Aging and female sex are risk factors, but the reasons are unclear. The purpose of this study was to evaluate whether identifiable radiographic changes resulting in a decrease in carpal tunnel area (CTA) over time exist. METHODS: A database search of a multicenter, academic, tertiary institution from 1998 to 2021 identified 433 patients with serial wrist magnetic resonance images (MRI) at least 5 years apart. Fifty-six met the inclusion criteria with adequate films to measure CTA and transverse carpal ligament (TCL) thickness at the same slice location-the carpal tunnel inlet, hook of the hamate, and carpal tunnel outlet-independently by two observers who were blinded to each other's measurements. Rates for the change in CTA and TCL thickness were calculated at all three locations. RESULTS: Thickness of the TCL increased, whereas that of the CTA decreased over time. Inlet CTA decreased by 0.9 mm2 per year (95% CI: 0.34-1.5), outlet CTA decreased by 1.8 mm2 per year (95% CI: 1.2-2.5), and CTA at the hook of the hamate decreased by 1.6 mm2 per year (95% CI: 1.0-2.0 per year). The TCL thickened by 0.02 mm per year at all three sections. Taller patients had a decreased rate of CTA loss. CONCLUSIONS: In this select cohort, TCL thickened and CTA decreased with time. TCL thickening accounted for about half of the variation in CTA, suggesting that this is a possible contributor to this change. Hypertrophy of the carpal tunnel floor may account for the remaining variation in CTA. The question of whether these results are reliable and generalizable to the general population, or a major influence in the pathophysiology of CTS, is unknown. CLINICAL RELEVANCE: Small decreases in CTA and thickening of the TCL occur with aging. Whether this is a contributing factor in the development of CTS requires further study.

Opportunistic screening for low bone density using abdominopelvic computed tomography scans.

BACKGROUND: While low bone density is a major burden on US health system, current osteoporosis screening guidelines by the US Preventive Services Task Force are limited to women aged ≥65 and all postmenopausal women with certain risk factors. Even within recommended screening groups, actual screening rates are low (<26%) and vary across socioeconomic groups. The proposed model can opportunistically screen patients using abdominal CT studies for low bone density who may otherwise go undiagnosed. PURPOSE: To develop an artificial intelligence (AI) model for opportunistic screening of low bone density using both contrast and non-contrast abdominopelvic computed tomography (CT) exams, for the purpose of referral to traditional bone health management, which typically begins with dual energy X-ray absorptiometry (DXA). METHODS: We collected 6083 contrast-enhanced CT imaging exams paired with DXA exams within ±6 months documented between May 2015 and August 2021 in a single institution with four major healthcare practice regions. Our fusion AI pipeline receives the coronal and axial plane images of a contrast enhanced abdominopelvic CT exam and basic patient demographics (age, gender, body cross section lengths) to predict risk of low bone mass. The models were trained on lumbar spine T-scores from DXA exams and tested on multi-site imaging exams. The model was again tested in a prospective group (N = 344) contrast-enhanced and non-contrast-enhanced studies. RESULTS: The models were evaluated on the same test set (1208 exams)-(1) Baseline model using demographic factors from electronic medical records (EMR) - 0.7 area under the curve of receiver operator characteristic (AUROC); Imaging based models: (2) axial view - 0.83 AUROC; (3) coronal view- 0.83 AUROC; (4) Fusion model-Imaging + demographic factors - 0.86 AUROC. The prospective test yielded one missed positive DXA case with a hip prosthesis among 23 positive contrast-enhanced CT exams and 0% false positive rate for non-contrast studies. Both positive cases among non-contrast enhanced CT exams were successfully detected. While only about 8% patients from prospective study received a DXA exam within 2 years, about 30% were detected with low bone mass by the fusion model, highlighting the need for opportunistic screening. CONCLUSIONS: The fusion model, which combines two planes of CT images and EMRs data, outperformed individual models and provided a high, robust diagnostic performance for opportunistic screening of low bone density using contrast and non-contrast CT exams. This model could potentially improve bone health risk assessment with no additional cost. The model's handling of metal implants is an ongoing effort.

Quantifying misdiagnosis rates from cross-calibration biases and precision errors in dual-energy X-ray absorptiometry of the femoral neck.

BACKGROUND: Dual-energy X-ray absorptiometry (DXA) is an exam that measures areal bone mineral density (aBMD) and is regularly used to diagnose and monitor osteoporosis. Except for exam quality issues such as operator error, the quantitative results of an exam are not modified by a radiologist or other physician. DXA cross-calibration errors can shift diagnoses, conceivably leading to alternate intervention decisions and patient outcomes. PURPOSE: After identifying and correcting a cross-calibration bias of 3.8% in our two DXA scanners' aBMD measurements, we investigated misdiagnosis rates for given cross-calibration errors in a single patient cohort to determine the impact on patient care and the value of cross-calibration quality control. METHODS: The studied cohort was 8012 patients of all ages and sexes with femoral neck exams that were scanned on a single DXA unit from October 1, 2018 to March 31, 2021. There were six subcohorts delineated by age and sex, three female groups and three male groups. Data reporting focused on the highest risk subcohort of 2840 females aged 65 or older. The DXA unit had no calibration changes during that time. Only one femoral neck-left or right-was randomly chosen for analysis. Patients with multiple qualifying exams within the time interval had one exam randomly chosen. The proof-of-principle simulation shifted the aBMD values within a range of ±10%, ±8%, ±6%, ±4%, ±3.5%, ±3%, ±2.5%, ±2%, ±1.5%, ±1%, ±0.5%, and 0 (no shift); the cross-calibration shifts were informed by published results and institutional experience. Measurement precision was modeled by randomly sampling a Gaussian distribution characterized by the worst acceptable least significant change (LSC) of 6.9%, with 100 000 samplings for each patient. T-scores were recalculated from the shifted aBMD values, followed by reassigned diagnoses from the World Health Organization's T-score-based scheme. RESULTS: The unshifted original subcohort of women aged 65 and older had 599 normal diagnoses (21.1% of the cohort), 1784 osteopenia diagnoses (62.8%), and 455 osteoporosis diagnoses (16.1%). Osteoporosis diagnosis rates were highly sensitive to aBMD shifts. At the extrema, a -10% aBMD shift led to +161% osteoporosis cases, and a +10% aBMD shift led to -64.5% osteoporosis cases. Within the more plausible ±4% aBMD error range, the osteoporosis diagnosis rate changed -10.5% per +1% aBMD shift as indicated by linear regression (R2  = 0.98). Except for the men aged 49 years and younger subcohort, the total cohort and five subcohorts had fit line slopes ranging between -9.7% and -12.1% with R2 ≥ 0.98. Cross-calibration bias had greater influence for diagnosis count rates compared to measurement precision, that is, LSC. CONCLUSIONS: These results quantify the degree of misdiagnosis that can occur in a clinically relevant cohort due to cross-calibration bias. In medical practices where patients may be scanned on more than one DXA unit, ensuring cross-calibration quality is a critical and high-value quality control task with direct impact on patient diagnosis and treatment course. The clinical impact and incidence of poor DXA quality control practices, and cross-calibration in particular, should be studied further.

The Magnet Is Sometimes "Off"-Practical Strategies for Optimizing Challenging Musculoskeletal MR Imaging.

To describe practical solutions to the unique technical challenges of musculoskeletal magnetic resonance imaging, including off-isocenter imaging, artifacts from motion and metal prostheses, small field-of-view imaging, and non-conventional scan angles and slice positioning. Unique challenges of musculoskeletal magnetic resonance imaging require a collaborative approach involving radiologists, physicists, and technologists utilizing optimized magnetic resonance protocols, specialized coils, and unique patient positioning, in order to reliably diagnose critical musculoskeletal MR image findings.

Mathematical modeling of mass and energy transport for thermoembolization.

Background: Thermoembolization presents a unique treatment alternative for patients diagnosed with hepatocellular carcinoma. The approach delivers a reagent that undergoes an exothermic chemical reaction and combines the benefits of embolic as well as thermal- and chemical-ablative therapy modalities. The target tissue and vascular bed are subjected to simultaneous hyperthermia, ischemia, and chemical denaturation in a single procedure. To guide optimal delivery, we developed a mathematical model for understanding the competing diffusive and convective effects observed in thermoembolization delivery protocols.Methods: A mixture theory formulation was used to mathematically model thermoembolization as chemically reacting transport of an electrophile, dichloroacetyl chloride (DCACl), within porous living tissue. Mass and energy transport of each relevant constituent are considered. Specifically, DCACl is injected into the vessels and exothermically reacts with water in the blood or tissue to form dichloroacetic acid and hydrochloric acid. Neutralization reactions are assumed instantaneous in this approach. We validated the mathematical model predictions of temperature using MR thermometry of the thermoembolization procedure performed in ex vivo kidney.Results: Mathematical modeling predictions of tissue death were highly dependent on the vascular geometry, injection pressure, and intrinsic amount of exothermic energy released from the chemical species, and were able to recapitulate the temperature distributions observed in MR thermometry.Conclusion: These efforts present a first step toward formalizing a mathematical model for thermoembolization and are promising for providing insight for delivery protocol optimization. While our approach captured the observed experimental temperature measurements, larger-scale experimental validation is needed to prioritize additional model complexity and fidelity.

A measurement of the attenuation of radiation from F-18 by a PET/MR scanner.

The attenuation of 511 keV photons by the structure of a PET/MR scanner was measured prior to energizing the magnet. The exposure rate from a source of fluorine-18 was measured in air and, with the source placed at the isocenter of the instrument, at various points outside of the scanner. In an arc from 45 to 135 degrees relative to the long axis of the scanner and at a distance of 1.5 m from the isocenter, the attenuation by the scanner is at least 5.6 half-value layers from the MR component alone and at least 6.6 half-value layers with the PET insert installed. This information could inform better design of the radiation shielding for PET/MR scanners.

A model evaluation study for treatment planning of laser-induced thermal therapy.

A cross-validation analysis evaluating computer model prediction accuracy for a priori planning magnetic resonance-guided laser-induced thermal therapy (MRgLITT) procedures in treating focal diseased brain tissue is presented. Two mathematical models are considered. (1) A spectral element discretisation of the transient Pennes bioheat transfer equation is implemented to predict the laser-induced heating in perfused tissue. (2) A closed-form algorithm for predicting the steady-state heat transfer from a linear superposition of analytic point source heating functions is also considered. Prediction accuracy is retrospectively evaluated via leave-one-out cross-validation (LOOCV). Modelling predictions are quantitatively evaluated in terms of a Dice similarity coefficient (DSC) between the simulated thermal dose and thermal dose information contained within N = 22 MR thermometry datasets. During LOOCV analysis, the transient model's DSC mean and median are 0.7323 and 0.8001 respectively, with 15 of 22 DSC values exceeding the success criterion of DSC ≥ 0.7. The steady-state model's DSC mean and median are 0.6431 and 0.6770 respectively, with 10 of 22 passing. A one-sample, one-sided Wilcoxon signed-rank test indicates that the transient finite element method model achieves the prediction success criteria, DSC ≥ 0.7, at a statistically significant level.

Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures.

PURPOSE: A generalised polynomial chaos (gPC) method is used to incorporate constitutive parameter uncertainties within the Pennes representation of bioheat transfer phenomena. The stochastic temperature predictions of the mathematical model are critically evaluated against MR thermometry data for planning MR-guided laser-induced thermal therapies (MRgLITT). METHODS: The Pennes bioheat transfer model coupled with a diffusion theory approximation of laser tissue interaction was implemented as the underlying deterministic kernel. A probabilistic sensitivity study was used to identify parameters that provide the most variance in temperature output. Confidence intervals of the temperature predictions are compared to MR temperature imaging (MRTI) obtained during phantom and in vivo canine (n = 4) MRgLITT experiments. The gPC predictions were quantitatively compared to MRTI data using probabilistic linear and temporal profiles as well as 2-D 60 °C isotherms. RESULTS: Optical parameters provided the highest variance in the model output (peak standard deviation: anisotropy 3.51 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.43 °C, and perfusion 0.94 °C). Further, within the statistical sense considered, a non-linear model of the temperature and damage-dependent perfusion, absorption, and scattering is captured within the confidence intervals of the linear gPC method. Multivariate stochastic model predictions using parameters with the dominant sensitivities show good agreement with experimental MRTI data. CONCLUSIONS: Given parameter uncertainties and mathematical modelling approximations of the Pennes bioheat model, the statistical framework demonstrates conservative estimates of the therapeutic heating and has potential for use as a computational prediction tool for thermal therapy planning.