Radiography of pacemakers and implantable cardioverter defibrillators

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.

Malignant Neoplasms Arising in the Cardiac Pacemaker Cavity: A Systematic Review

Cancer is the abnormal proliferation of physiologically inadequate cells. Studies have identified the cardiac pacemaker pocket as a site of rare neoplasms. To evaluate the clinical outcomes, treatment, prognosis, and individualized management of tumors originating in the cardiac pacemaker pocket, a systematic review was conducted using case reports and case series available in the PubMed/Medline, Science Direct, Cochrane Central, LILACS, and Scientific Electronic Library Online (Scielo) databases. Pacemaker pocket tumors affected patients with a mean age of 72.9 years, with a higher incidence in males (76.9%, n = 10). The average time for neoplasm development was 4.4 years (54.07 months). The most prevalent model was Medtronic (38.4%, n = 5), with titanium (83.3%) being the most common metal composition. Chemotherapy was the most performed procedure among patients (38.4%), followed by radiation therapy (38.4%) and surgical tumor resection (30.7%). Six analyzed cases (46.1%) resulted in death, and four patients (30.7%) achieved a cure. Patients with pacemakers should be routinely evaluated for the occurrence of malignant tumors at the site of device implantation.

Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs

Objective

With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed.

Materials and Methods

We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn).

Results

The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001).

Conclusion

The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection

We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural network performance. We validated the publicly available Pacemaker-ID web server and mobile app on 43 local hospital emergency department (ED) cases of patients presenting with a cardiac rhythm device on anterior-posterior (AP) chest radiograph and assessed performance using Cohen's kappa coefficient for inter-rater reliability. To illustrate adversarial performance concerns, we then produced example CNN models using the 65,379 patient MIMIC-CXR chest radiograph retrospective database and evaluated performance with area under the receiver operating characteristic (AUROC). In retrospective review of 43 patients with cardiac rhythm devices on AP chest radiographs during our study period (January 1, 2020 to March 1, 2020), 74.4% (32/43) had device manufacturer information readily available within the electronic medical record. A total of 25.6% of patients (11/43) did not have this information documented in the patient chart and could ostensibly benefit from CNN-based identification of device manufacturer. For patients with known device manufacturer, the Pacemaker-ID prediction was accurate in 87.5% of cases (28/32). Mobile app accuracy varied from 62.5 to 93.75% depending on image capture settings and presentation. Cohen's kappa coefficient varied from 0.448 to 0.897 depending on mobile image capture conditions. For our additional analysis of medically adversarial performance failures with a DenseNet121 trained on MIMIC-CXR images, we showed that an AUROC of 0.9807 ± 0.0051 could be achieved on an example testing dataset while masking a 30% false positive rate in identification of cardiac rhythm devices versus clinically distinct entities such as vagal nerve stimulators. Despite the promise of CNN approaches for cardiac rhythm device analysis on chest radiographs, further study is warranted to assess potential for errors driven by user misuse when deploying these models to mobile devices as well as for cases when performance can be impaired by the presence of other support apparatuses.

Uncommon Iatrogenic Devices Seen on Chest Radiographs

Chest radiograph (CXR) is the most common imaging performed for both inpatients and outpatients. With advances in medicine and technology, newer devices/prosthesis are being used in the treatment of cardiothoracic conditions. Some of these are common while others are seen only in a handful of cases, especially in patients being treated or referred from tertiary care centers. It is important to know about these devices, their functionality, and radiographic appearances. Many of these devices also help us in understanding the clinical condition of the patient, as some are only used in unstable patients. Newer methods of life support are now available in intensive care units and these also can be seen on CXRs. In this review, we present various iatrogenic devices that we come across on a CXR and highlight important features to determine their correct placement and potential complications. The review looks at cardiac temporary and permanent pacing devices, cardiac interventional devices used to treat congenital heart disease, newer cardiac monitoring devices, and unusual surgical devices that one may come across on a CXR. We also suggest a stepwise algorithm to assess these devices on a CXR.

Back to Fundamentals: Radiographic Evaluation of Thoracic Lines and Tubes in Children

OBJECTIVE. The purpose of this article is to provide an up-to-date review of the radiographic appearance of the most commonly used thoracic lines and tubes in pediatric patients in daily clinical practice. CONCLUSION. Thoracic support lines and tubes are frequently used in children receiving hospital care. Evaluation of these devices is a fundamental skill in radiology. Many different devices are currently used, and new devices are regularly introduced. It is essential for radiologists to maintain a clear understanding of all devices currently in use.

Cardiovascular implantable electronic devices: a review of the dangers and difficulties in MR scanning and attempts to improve safety

Abstract

An increasing number of patients are being treated with cardiovascular implantable electronic devices (CIEDs), many of which are MR conditional. There is a lack of literature on the safe scanning of MR conditional CIEDs. This review article discusses MR imaging safety in patients with implanted CIEDs. Guidelines on safe use and indications of imaging patients with MR conditional CIEDs are described, followed by a pictorial essay of the radiographic features of these devices. We also discuss the challenges of monitoring the patient in the MR environment, advances in MRI conditional imaging of devices, availability, limitations and workflow including vendor-specific and other collaborative efforts to simplify the scanning process. Radiologists must be able to facilitate the safe utilization of MR imaging in patients who have CIEDs. A thorough knowledge of the hazards of imaging non-MR compatible devices is required as well as knowing how to correctly identify and manage the imaging of patients with MR conditional CIEDs. Finally, we propose steps required to facilitate the safe scanning of patients with MR conditional CIEDs adopted in our institution and a contingency plan in the event that an inadvertent MR scan of a patient with a MRI unsafe CIED should occur.

Main Messages

• Risks of MR imaging in patients who have CIEDs have been worked around.

• There are many technical limitations in enabling safe MR scanning of CIEDs.

• Radiological identification of MRI-conditional status of CIEDs is useful.

• Standardizing conditions for safe MRI scanning is important.

• We offer example algorithms for facilitating safe MRI scanning of CIEDs.

Evaluation of medical devices in thoracic radiograms in intensive care unit - time to pay attention!

Objective

To identify and evaluate the correct positioning of the most commonly used medical devices as visualized in thoracic radiograms of patients in the intensive care unit of our center.

Methods

A literature search was conducted for the criteria used to evaluate the correct positioning of medical devices on thoracic radiograms. All the thoracic radiograms performed in the intensive care unit of our center over an 18-month period were analyzed. All admissions in which at least one thoracic radiogram was performed in the intensive care unit and in which at least one medical device was identifiable in the thoracic radiogram were included. One radiogram per admission was selected for analysis. The radiograms were evaluated by an independent observer.

Results

Out of the 2,312 thoracic radiograms analyzed, 568 were included in this study. Several medical devices were identified, including monitoring leads, endotracheal and tracheostomy tubes, central venous catheters, pacemakers and prosthetic cardiac valves. Of the central venous catheters that were identified, 33.6% of the subclavian and 23.8% of the jugular were malpositioned. Of the endotracheal tubes, 19.9% were malpositioned, while all the tracheostomy tubes were correctly positioned.

Conclusion

Malpositioning of central venous catheters and endotracheal tubes is frequently identified in radiograms of patients in an intensive care unit. This is relevant because malpositioned devices may be related to adverse events. In future studies, an association between malpositioning and adverse events should be investigated.

Radiography of cardiac conduction devices: a pictorial review of pacemakers and implantable cardioverter defibrillators

Cardiac conduction devices (CCDs) depend on correct anatomic positioning to function properly. Chest radiography is the preferred imaging modality to evaluate CCD's anatomic location, lead wire integrity, and help in identifying several complications. In this pictorial review, our goal is to familiarize radiologists with CCD implantation techniques, appropriate positioning of the device, common causes of malfunction, methods to improve report accuracy, and assure maximal therapeutic benefit.

Imaging of iatrogenic conditions of the thorax

Common medical interventions performed by cardiologists, radiologists, surgeons, dentists, and alternative practitioners can result in complications within the thorax that lead to significant patient morbidity. Prompt radiologic identification of iatrogenic complications of medical procedures in the thorax is essential to guide patient triage and treatment. Understanding the approach to common thoracic interventions and the placement of thoracic medical devices can aid radiologists in the evaluation of iatrogenic complications.

Pacemaker lead malposition in the left atrial roof is masked by normal pacing thresholds

Pacemaker lead malpositioning with subsequent cardiac tamponade is a rare, but serious adverse event. We herein report a case of pacemaker lead malpositioning in a 76-year old female caucasian patient. The lead was malpositioned into the roof of the left atrium after perforation of the superior vena cava, resulting in cardiac tamponade. After fast surgical revision and an uneventful post-operative period, the patient was discharged in excellent condition.