Category: Articles

^{_{Updated October 25, 2021}}

As MRI technology continues to improve, radiologists must maintain a mastery of complex sequences, evolving protocols, and advanced techniques. Likewise, disease-specific guidelines, protocols, and reporting continue to evolve. Radiologists in all practice types are often tasked with managing a busy practice at this intersection of advanced technology and state-of-the-art clinical care.  

From September 9–10, the ARRS Virtual Symposium, Abdominal MRI: Practical Applications and Advanced Imaging Techniques, delivered trusted perspectives on the most pressing issues in body MRI—ranging from protocol and acquisition optimization, incorporation of advanced techniques in routine clinical practice, and interpretation/reporting for the most important trends in liver, prostate, emergency, and gynecological MR imaging. What follows is our primer for those unable to attend the live sessions.

Emergency MR Imaging

Although CT is the workhorse imaging modality in the emergency department (ED), there are many concerns related to ionizing radiation exposure in populations that are vulnerable due to young age, pregnancy, or exposure to repeated imaging examinations. In patients with nontraumatic acute abdominal symptoms, non-contrast MRI offers similar diagnostic performance to CT. For instance, in a prospectively enrolled cohort of 48 patients with head-to-head comparison of MRI to CT, there was no significant difference in performance for diagnosing acute appendicitis in young adults and adolescents (Fig. 1).

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A key to harnessing the efficacy of MRI in the ED setting is through protocol optimization, focusing on efficiency and speed. Abbreviated protocols, comprising fewer and faster sequences, allow for improved adoption, decreased cost, and maintained sensitivity for answering directed clinical questions. For example, in the ED setting, compared to conventional MR cholangiopancreatography (MRCP) protocols, abbreviated MRCP provides significant time savings, while maintaining similar diagnostic accuracy for the detection of choledocholithiasis. ARRS Symposia course director Victoria Chernyak, ARRS Instructional Courses Committee chair Courtney Coursey Moreno, and Elena Korngold highlighted ED MR protocols and imaging findings for common genitourinary and gastrointestinal emergencies.  

Advanced MR Techniques

Beyond the ED setting, advanced MR imaging techniques have opened the door for quantitative imaging, and multiparametric assessment has become standard practice for many disease processes. Sequences for assessment of liver steatosis, iron deposition, and fibrosis are now available on all major MR vendor platforms, allowing accurate diagnosis and monitoring of patients with chronic liver diseases. A working knowledge of how to extract and report the quantitative data derived from MRI proton density fat fraction, R2* maps, and elastography is required to build a state-of-the-art radiology liver practice. Diffusion weighted imaging (DWI) is no longer an ancillary or optional sequence but is required for accurate multiparametric assessment of prostate cancer. DWI can be used as a biomarker of tumor response; for instance, DWI adds value in assessing response to neoadjuvant therapy in patients with rectal cancer. While important and useful, DWI can be challenging to optimize and interpret in practice. 2002 ARRS Scholar Claude B. Sirlin imparted his applied wisdom regarding DWI acquisition optimization and interpretation, Mustafa Rifaat Bashir offered insights into some of the new sequences and future directions, and Antonio Carlos A. Westphalen emphasized the utility of DWI in the current prostate imaging reporting and data system (PI-RADS) v2.1. 

Protocol Optimization

In practice, MRI provides both great potential and great challenges. Optimizing sequences across multiple scanners, technologists, and protocols can be laborious and frustrating for radiologists. Optimized MRI protocols achieve a delicate balance between acquisition times, image quality, and sequence comprehensiveness, and they are—to paraphrase Albert Einstein—made as simple as possible, but no simpler. Richard Kinh Gian Do, Steven S. Raman, Elizabeth A. Sadowski, and Korngold shared their expert insights into optimal protocols for hepatobiliary, prostate, female pelvis, and bowel imaging. Bashir presented real-world methods for recognizing and, most importantly, mitigating artifacts commonly encountered in abdominal MR imaging.

State-of-the-Art Reporting

Optimizing images is just one hurdle for delivering the best imaging care to patients. Over the last decade, there has been a major movement toward standardized reporting for many disease processes in the abdomen and pelvis. Standardized reporting allows for more precise communication of results and improves compliance with diagnostic criteria. Notably, the liver imaging reporting and data system (LI-RADS), PI-RADS, ovarian reporting and data system (O-RADS), and updated Bosniak criteria are increasingly recognized as the standard of care for interpretation and reporting. ARRS Symposia course codirector Kathryn J. Fowler, Westphalen, Raman, and Sadowski provided an overview of these important systems, a framework for applying them, as well as insights into creating templates to improve reporting efficiency (Fig. 2).

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Delivering state-of-the-art care with MRI requires comfort with the technical aspects of image acquisition, reporting standards and approaches to common disease processes, and advanced sequences as integrated into practice. Taught by world-renowned MR imaging experts, Abdominal MRI: Practical Applications and Advanced Imaging Techniques provided practicable tools to harness the power of these expanded procedures for improving patient care.

^{Published June 28, 2021}

In the past decade, interest in artificial intelligence (AI) research in radiology has increased dramatically, as reflected by the tenfold increase in papers published in the subject over this period. This development is accompanied by fears from radiologists that AI will eventually replace human readers for the diagnostic interpretation of imaging studies. This article will review the current methods AI uses for diagnostic imaging tasks, the challenges AI algorithms have to overcome for their more widespread implementation, and how radiologist roles may shift to better accommodate the strengths of machine learning.

Deep Learning: An Introduction

The term “artificial intelligence,” in the most general sense, refers to the ability of algorithms to mimic human cognitive abilities. AI algorithms used for image analysis are typically designed using neural networks, which involves layers of processing nodes organized into an input, output, and multiple hidden layers. The architecture is feedforward; outputs of the previous layer are used as the inputs of the next. Each layer is composed of nodes, each of which has a weighted function that associates the inputs to outputs. The function weights are determined by backpropagation during training, an algorithm which updates function weights by minimizing a loss function.

Convolutional neural networks (CNNs) are neural networks specifically designed to handle image data and are the most common, though by no means only, deep learning architecture used for medical image applications. In diagnostic imaging studies, CNNs are usually trained with supervised learning by radiologist annotated data. The layers of a CNN process input images into an image classification output decided by weighted probabilities. For example, one hypothetical CNN’s classification output of a chest radiograph may be an 80% probability of pneumonia diagnosis, 15% pleural effusion, and 5% pneumothorax. The architecture of a CNN can be summarized as follows: first, convolutional layers extract features from an input image. The resulting feature map then passes through pooling layers, which decrease the number of trainable parameters by reducing the dimensions of the convolutional layer input [2]. Finally, a fully connected layer uses the inputs from the feature map and applies weights to parameters to classify the image, typically using rectified linear unit (ReLU) as its activation function.

CNNs have been developed for a wide range of diagnostic tasks based on modality image input data. They have shown comparable results to radiologists for identification of lung nodules and quantification of coronary artery calcium volume during low-dose CT screening, comparable performance to radiologists on the diagnosis of multiple thoracic pathologies from chest radiographs, and better diagnostic performance than radiologists for diagnosis of pneumonia from chest radiographs. Aside from applications for chest imaging, CNNs have achieved high accuracy (AUC) for liver fibrosis staging, accurate segmentation of clinical target volume (CTV) and organs at risk (OARs) in colorectal cancer films, and improved prediction of overall survival in glioblastoma patients from MRI data. However, while the results of CNNs are often comparable to diagnostic radiologists, it is important to note their detection tasks are narrow in scope, and like other AI methods, they are dependent on high quantities of quality training and validation data.

Challenges

There are several challenges to AI implementation in diagnostic radiology not readily solved with mere improvements in technology. The opaque nature of an AI algorithm’s functionality is one such challenge. The nature of backpropagation is such that algorithm developers are inherently unable to explain why parameters end up at their trained values, only that they were the values the algorithm discovered as optimal. Likewise, the precise features of an image that a neural network used to arrive at its classification output are impossible to know. In this regard, a neural network resembles the brain it was loosely inspired by—scientists understand that they work, but not how or why. This “black box” quality of machine learning has not, however, prevented the FDA from approving commercial AI-based medical devices and algorithms, and the history of medicine shows the field has accepted other black boxes for patient care. For example, the exact mechanisms by which many drugs work or produce side effects are poorly understood, but such drugs are used and trusted by physicians. Computer scientists are also attempting to make AI more transparent by developing processes to delineate the methods by which neural networks reach conclusions. This new field, called explainable artificial intelligence (XAI), is an active area of research.

Nevertheless, it is easy to imagine a future where AI becomes part of standard of care for diagnostic radiology, wherein health systems, and specifically the radiologist, would still be responsible for algorithm errors, even if an AI’s reasoning for the error is unexplainable. It is still unknown who would bear the liability for an algorithm misdiagnosis, nor whether the public and their primary physicians would accept machine diagnosis.

The training and validation of medical image algorithms is yet another challenge to their implementation. Deep learning algorithms such as CNNs often require huge amounts of data because of the high number of parameters that must be optimized during training. Large high-quality datasets, which must be annotated by radiologists, are expensive to procure and often several orders of magnitude smaller than the training datasets used for other deep learning applications. They usually number from the hundreds to the tens of thousands, compared to the 4 million labeled images Facebook’s DeepFace facial recognition neural network used as its training dataset. There has been recent progress on creating publicly available image datasets for algorithm development and on medical image analysis challenges that provide datasets for developers. Stanford’s CheXpert dataset is the largest of these, with 224,216 labeled chest radiographs available for public use. However, training datasets is still often proprietary due to privacy concerns or intended commercial use of the algorithms utilized. This dearth of training data and lack of transparency can create problems of algorithmic biases; homogenous training data can cause health care AI algorithms to weigh certain diagnoses unequally based on socioeconomic status, race, or gender, and properly diverse datasets are difficult to assemble.

AI and the Future of Radiology

However, the greatest challenge for AI in diagnostic radiology may be whether it replaces a diagnostic radiologist’s function entirely, and the resulting health care implications for patients. Medical experts have even raised the question if diagnostic radiologists should continue to be trained, given the fast-approaching integration of AI technologies as part of diagnostic algorithms, which will almost certainly include some form of initial automated machine-driven image analysis. The advantages of AI over human radiologists seem overwhelming at first. Computers can work 24/7 without signs of fatigue. While diagnostic radiologists typically read between 10,000–20,000 films annually, the number of images a neural network could read in the same time period is multiple orders of magnitudes greater, in the tens or hundreds of millions. As such, the economic reasoning for institutions to replace diagnostic radiologists seems obvious. However, currently developed algorithms only accomplish tasks narrow in scope (e.g., the binary classification of a lung nodule or the density analysis of a renal stone), whereas radiologists read studies holistically, looking for all possible abnormalities displayed in an imaging study. For a computer to read a diagnostic imaging data set in the same way a radiologist typically does would require the development, training, validation, and integration of thousands of discrete AI algorithms into one workflow. This is a difficult task, and though theoretically possible, a single generalized AI algorithm that can holistically analyze all features of an image for all possible diagnoses is even harder. For the foreseeable future, radiologists will still be required for accurately reading film, with AI packages offering decision support algorithms, and boosting reader efficiency by both providing second opinions on the primary detection tasks and simplifying workflow (e.g., prepopulating reports, automatically creating ROIs, etc.)

Even if AI technologies improve such that diagnostic AI application packages become capable of holistically reading modality studies, one must also remember that radiologists are not just image analysts, but fundamentally clinicians, trained to interpret and communicate imaging findings in the clinical context of the patient. Radiomics that uses AI generates complex data that must be interpreted by radiologists and linked to clinical uses; this is a need that will only grow in the future. AI-driven increased efficiency of image analysis may cause radiologists to pivot to more patient interaction and to become even more integrated in the clinical decision processes, in collaboration with a patient’s care team.

Given the uncertainties of the black box nature of AI, radiologists appear to be the best positioned medical professionals to elucidate a neural network’s probability outputs to a patient, along with the image features the machine is using to arrive at its diagnoses and their meaning. To best synergize with AI, radiologists of the future will have to improve care through knowledge of and empathy for their patients, the ability to identify which AI-produced data is relevant to meet diagnostic demands, and the interpretation of scans to elucidate the next steps following image findings to better advocate for their patients.

AI, and in particular convolutional neural networks, have seen success in narrow detection tasks for medical imaging diagnoses. While various challenges exist, such as their opaque nature and the cost and small scale of training datasets, they will likely be surmountable in time. AI will inevitably enter the diagnostic radiologists’ workflow. Although AI will not replace radiologists in the future, it is likely that radiologists who use AI will eventually replace radiologists who do not. The use of AI will lead to more efficient image diagnosis, in combination with optimized decision support algorithms, which will benefit patient care. In the not too distant future, AI could usher a potential realignment of radiologist duties towards a more interactive and patient-focused paradigm, in addition to traditional models centered around the reporting of imaging findings.

^{Published June 28, 2021}

For valiant service selflessly rendered on the frontlines of the fight against COVID-19, the American Roentgen Ray Society symbolically awarded each and every one of our members the 2021 ARRS Gold Medal. The ARRS Gold Medal Story Series shares perspectives of imaging professionals who conquered the day-to-day challenges of battling COVID-19.

The declaration by the World Health Organization of the coronavirus disease (COVID-19) pandemic in December 2019 led to an avalanche of information which did not manifest clearly, especially here in Nigeria. The signs, symptoms, presentation, and management of patients with COVID-19 slowly evolved for us.

The breast imaging sections of our hospitals went into an almost complete lull, as radiologists were exceedingly careful about exposing themselves to patients whose infection status was unknown. Testing patients for COVID-19 was not easy, due to lack of resources and consumables. More so, there was a dearth of personnel protective equipment (PPE), which was in extremely short supply from hospital management. Initially, breast clinics were shut down, and all activities were suspended. We had to set up an Infection, Prevention, and Control (IPC) Committee, which I chaired, while developing core guidelines for operations in the department.

All of the underlisted were considered crucial standard operating procedures:

• Undergo IPC training and tutorials
• Audit staff for protection
• Inventory, maintain, and decontaminate equipment
• Aerate all facilities
• Identify isolation opportunities for at-risk patients

Contamination Prevention

With temperature checks and masks mandatory for entry, our front desk served as patient registration only, not for triaging, so that foot traffic did not build up. To maintain social distancing of at least 2 meters apart in all waiting areas, the floors were marked in red to depict this distance. All persons entering the triage zone had to wash their hands with soap and running water. Posters showing proper modes of mask-wearing and hand-washing—as well as for gloves, goggles, and faces shields—were posted around the hospital. Only one relative was allowed to accompany a patient who could not stand on their own. To further reduce foot traffic, payment areas were augmented and fast-tracked.

Examination Rooms

Only one patient at a time was allowed in the examination room, and the radiographer/radiologist was well-kitted, depending upon that patient’s risk assessment. For COVID-19-postive patients, we had a dedicated mobile radiography unit, though all rooms were decontaminated after every procedure.

Decontamination

With hand sanitizers at each interactive point and zone, hands were properly washed before and after every procedure. All surfaces were decontaminated after each procedure, including cleaning and decontamination of reusable PPE and proper disposal of disposable ones. Our hospital’s standardized protocols for decontaminating an imaging room and equipment—especially CT after attending to a patient with COVID-19—featured downtime of about one hour in between procedures. Result retrieval points were set up in a cubicle outside the facility, and to reduce human occupancy, results were dispatched by e-mail. Remote reporting was encouraged, too.

Communication

Bold signage was displayed all over the hospital, including clear instructions and visible explanation notices posted on our doors. Relevant phone numbers (e.g., Central Preparedness Team, Hospital Infection Control Committee, Departmental IPC, National Center for Disease Control, etc.) were displayed on the front desk, triage zone, and notice boards.

Capacity Building

In addition to restricting their local and international travel, our entire staff received regular training (and retraining) in IPC protocols, especially how to don and doff PPE.

Some of our radiologists also contracted COVID-19. It got to a stage where all the staff in the pathology department tested positive, so ultrasound-guided biopsies were not possible. Even patients had restricted themselves from showing up for treatment in the hospitals, for fear of the unknown and restriction of movement. Breast cancer patients preferred to receive treatment through phone calls, since there were no teleradiology options.

However, musculoskeletal services were made available for patients who needed to be seen as emergency cases.

With the gradual improvement of the knowledge of the disease, availability of testing facilities, PPE, and now, vaccines, there has been much easing of restrictions. A gradual return to normal is envisaged. In our hospitals, there has been a return of influx for breast imaging patients in the last two months, especially for ultrasound and mammography, since these are the readily available investigations.  

_{Published March 16, 2021}

With our April 2020 AJR paper, “DICOM Images Have Been Hacked! Now What?,” well received by the ARRS membership, we were asked to record both an accompanying podcast and live webinar. We also presented our results at the Cybersecurity Refresher Course during the Radiological Society of North America 2020 Scientific Assembly, as well as at many other venues. Due to so many new developments in the worlds of cybersecurity and health care since the publication of our original AJR review, I have been invited to provide an overview summarizing a few of those latest developments.

Recent Hacks

The number of cyberattacks targeting medical institutions continue to increase. In 2020, 79% of all cyberbreaches affected the health care sector, so our sector has become a prime target. The breach portal of the U.S. Department of Health and Human Services Office for Civil Rights has reported a total of 620 new breaches of medical records in 2020. Trinity Health in Livonia, MI took the largest hit, when more than 3.3 million records with patient information were compromised as a result of a ransomware attack on Blackbaud, a cloud computing provider selling a fundraising database software.

Other recent breaches and attacks have received lots of media coverage, for example:

• In September 2020, Universal Health Services (UHS) was forced to shut down all computer systems at its facilities around the U.S. after a cyberattack by the Ryuk ransomware. This attack was likely triggered by a phishing email. UHS operates more than 400 health care facilities across the U.S. and U.K. After the shutdown, many of their facilities were left without access to computer and phone systems. Access to anything computer-based—from old labs and ECGs to radiology studies—was lost. UHS was forced to redirect ambulances and relocate patients in need of surgery to other hospitals nearby. Following the incident, four deaths were reportedly caused by delays in lab results arriving via courier. However, it is unclear whether or not these deaths were directly related to the cyberattack.
• In Düsseldorf, Germany, also in September 2020, a ransomware attack against Heinrich Heine University gravely affected its University Hospital. Cybercriminals encrypted about 30 hospital servers, preventing access to important medical information for patient care. Doctors had no access to this information, so patients had to be redirected to other hospitals. As a result, a female patient in transit to the emergency department in critical condition was rerouted to a hospital 20 miles away. With the detour causing a one-hour delay in her care, she died in transit—known to be one of the first cases of proven death from ransomware.
• A study by Greenbone Networks in September 2019 revealed that, worldwide, billions of confidential medical images on DICOM servers were freely accessible on the internet. This study headlined the news and even caught the attention of Congress, where Senator Mark Warner of Virginia became a strong supporter of improving the security of medical servers and images. In October 2020, New Net Technologies directed a follow-up study and discovered that, in the U.S. alone, millions of unprotected medical images were still exposed on the internet.
• In March 2020, Russian hackers (APT29), who were also responsible for the Democratic National Committee hack in 2016, inserted malicious code within an update of SolarWinds’ Orion software, which monitors the computer networks of governments and businesses to detect problems. Once the hacked update was downloaded by users, the perpetrators were secretly granted remote access to all the networks monitored by Orion, allowing for complete control and the ability to easily steal information. Hundreds of government institutions and private companies have been affected, including the Departments of Homeland Security, Treasury, Commerce, and Justice, as well as the Pentagon, Postal Service, and National Institutes of Health. As this article is being written, investigation continues to reveal the full extent of the damages. So far, at least 250 federal agencies and businesses have been compromised by the hack.

COVID-19 Vulnerabilities

The outbreak of coronavirus disease (COVID-19) created a new set of problems for cybersecurity, producing a triple threat for health care systems:

1. rapid expansion of networked devices and services, creating an expanded attack surface
2. increase in the different types of cyberattacks
3. fewer available resources to defend against cyberattacks

The use of telehealth has surged during the COVID-19 pandemic. At my institution, the Hospital of the University of Pennsylvania, consultations by telehealth skyrocketed from 50 to 7,000 per day. Many radiologists continue to work remotely from home, creating additional vulnerabilities in security.

A home radiology workstation connects to a home router, which connects via the internet to the hospital virtual private network device, which itself connects to the hospital servers. Each of these connection points are vulnerable. Radiologists reading remotely often forget to change the default administrator password on their home router. Hackers have performed domain name system hijacking on hundreds of thousands of home routers, redirecting links from legitimate institutions to hackers’ websites and intercepting data.

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As soon as COVID-19 became a pandemic, there was a massive surge in different kinds of cyberattacks. Whenever there is a social crisis, cybercriminals exploit the situation in full force, as people are more stressed out and, therefore, more prone to make mistakes. Phishing attacks pretending to originate from entities such as the World Health Organization spread across the globe like wildfire. These emails included fake links and malicious file attachments.

Real websites, such as the Johns Hopkins Coronavirus Resource Center’s COVID-19 map, were quickly duplicated, becoming major sources of worldwide malware distribution. Upon visiting the fake websites, computers became infected by Trojans stealing crucial information. Information about those fake websites was spread via phishing emails, malicious online advertisements, social engineering, and search engines. Since the beginning of the pandemic, over 60,000 COVID-19-related fake websites have been created.

Meanwhile, nation states, like Russia and China, have been attacking pharmaceutical companies and vaccine developers to steal intellectual property. They use phishing emails to obtain login credentials, and then use exploits to transmit files or execute code remotely. Two well-known groups of cybercriminals, APT29 from Russia and APT41 from China, have stolen COVID-19 research data by exploiting weaknesses in servers and routers.

Some Solutions

Late last year, the National Cybersecurity Center of Excellence, part of the National Institute of Standards and Technology (NIST), released its final practice guide on how to protect PACS technology and DICOM servers. NIST recommended a combination of strategies, including defense in depth, access control mechanisms, a holistic risk management approach, and the use of cloud storage. A defense in depth strategy involves multiple layers of defense at the level of the perimeter, the network, the workstation, the application, and the data; if one fails, data are still protected. NIST also recommended network segmentation into groups of devices sharing similar functionalities. This can be accomplished through virtual local area networks or by finer segmentation using software-defined networking—often used to secure legacy devices that lack inherent security features.

Also in December 2020, the Health Sector Cybersecurity Coordination Center issued a sector alert regarding vulnerabilities in DICOM image servers, while adding a series of recommendations on how to better protect them. These suggestions included general ones (use secure passwords, close unused computer ports, apply the most recent patches), as well as using encryption of data at-rest and in-motion, restrictions on network access, and network segmentation.

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The U.S. government enacted into law the Internet of Things (IoT) Cybersecurity Improvement Act of 2020 in December, too. Responding to the significant increase in control of IoT devices by cybercriminals during the year, this law establishes new mandatory minimum-security standards for IoT devices purchased using government funds, including supply chain security.

Early this year, the U.S. government enacted into law the HIPAA Safe Harbor Bill to amend HITECH (Health Information Technology for Economic and Clinical Health) to incentivize the use of cybersecurity best practices for meeting HIPAA requirements, especially those recommended by the NIST. The HITECH Act from 2009 was responsible for expanding the adoption of electronic health records (EHR) by health care providers— keeping one million U.S. physicians busy every evening, entering clinical data into the EHR.

What can we expect in 2021 for cybersecurity in health care? The new administration will start by repairing the massive damages to the cybersecurity infrastructure caused by the previous administration. Nation states will continue cyberattacks on COVID-19 vaccine developers to steal trade secrets and gain a competitive advantage. Smaller health care institutions, barely surviving this pandemic economically, will face increased attacks by cybercriminals. They do not have the same cyberdefense budget and manpower as larger institutions, although they face the same cyberthreats. As health care organizations transition more and more of their data to the cloud, we should see increasing numbers of data breaches from patient data on cloud infrastructures. Phishing with a health care theme will be more prevalent, given the focus on all health issues during COVID-19. And IoT and wearable medical devices will remain targets of cyberattacks for the foreseeable future, until the industry fully implements the new IoT security standards imposed by the latest legislation.

_{Published March 16, 2021}

In early 2020, as the coronavirus disease (COVID-19) epidemic spread, numerous articles appeared on imaging for the diagnosis of COVID-19. Descriptions were often discordant and confusing because of small sample sizes and selection biases. Those discrepancies have been largely resolved. As clinical experience and testing improved, imaging’s role has largely switched from diagnosis to aiding prognosis and clinical management.

Asymptomatic/mildly symptomatic patients: The Fleischner Society (Radiology: Cardiothoracic Imaging, Vol. 2, No. 3) and others see no role for chest x-ray (CXR) or CT for diagnosis, unless comorbidities exist or testing is otherwise limited.

Moderately/severely symptomatic patients: CXR is often negative early, only to turn positive subsequently. The most characteristic findings are basilar and peripheral ground-glass opacities (GGOs) (Fig. 1).

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Unfortunately, only a minority of patients have this typical COVID-19 pattern. In most patients, the disease is located more diffusely or elsewhere. Consolidation may be present initially with more severe illness or duration (Fig. 2).

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Early on, CT may be positive when the CXR is negative. The typical GGO distribution is similar, but usually more extensive than on CXR (Fig. 3).

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Adenopathy, cavitation, and effusion are uncommon early on. Compared to other viral pneumonias, peripheral lower-lobe GGOs are more common in COVID-19, whereas other pneumonias tend to have more diffuse disease. There is considerable overlap, however.

Reporting guidelines: Many templates have been proposed. Radiological Society of North America guidelines (Radiology: Cardiothoracic Imaging, Vol. 2, No. 2) define:

• CXR = negative, COVID-19-like; regular pneumonia, other disease
• CT = negative, typical of COVID-19; indeterminant, not COVID-19

Both show moderate interobserver reproducibility.

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Follow-up of established disease: On CXR and CT, GGOs and focal area of consolidation often progress and may evolve into a bilateral acute respiratory distress syndrome-like pattern when cytokine storm develops. Less common CT findings include dilated peripheral pulmonary vessels, adenopathy, rounded infiltrates, and signs of bronchial inflammation. Hospital-acquired bacterial pneumonia may complicate COVID-19 and vice versa. Preexisting lung disease (e.g., chronic obstructive pulmonary disease, interstitial lung disease, etc.) further complicate interpretation. Clearing usually starts after 2 weeks.

COVID-19 may cause hypercoagulability, leading to an increased incidence of both emboli and in situ thrombi and deep vein thrombosis.

Pleural effusions may appear late, and barotrauma causing pneumothorax appears to be more common than in other viral pneumonias.

Beyond the thorax: COVID-19 affects more than the lung. It is now clear that this is a systemic disorder affecting many organs.

As with lung involvement, patients with comorbidities (e.g., diabetes, cardiopulmonary disease, etc.) appear more likely to develop extrapulmonary diseases—summarized briefly below.

Cardiac disease: A significant minority of patients develop cardiac disease, evidenced by elevated troponins and other cardiac markers. Imaging may show evidence of heart failure, myocarditis, coronary vasculitis, mural thrombi, and pericardial effusions. Cardiac MRI has an important role, since it can identify myocardial inflammation. Myocarditis has been associated with poor outcomes, including cardiac dysfunction and mortality (either related to COVID-19 or other cause).

Neurological disease: Approximately 15–20% of hospitalized patients develop altered mental status or more focal symptoms. Imaging is positive in a minority, however. Ischemic infarcts are the most common imaging findings, probably related to coagulopathy. Reported infrequently are hemorrhagic stroke, cranial nerve inflammation, encephalopathy, and worsening of multiple sclerosis plaques.

Renal disease: Elevated renal function tests are not uncommon, and acute renal failure has been reported in some cytokine storm patients. Ultrasound may show increased renal echogenicity.

Gastrointestinal disease: Diarrhea and other gastrointestinal symptoms are not uncommon. Imaging may show ileus, dilatation, bowel loops (diffuse/focal), and CT may reveal contrast-enhanced bowel wall. Liver function tests are often abnormal, but failure is uncommon.

Long haulers: A significant minority of patients have residual vague or specific symptoms. Cough and dyspnea are frequent and often clear with supportive therapy within 30 days. Imaging is suggested for symptoms lasting beyond 30 days. Approximately a third of post-hospital patients will have residual signs of “fibrosis” and other residual ground-glass patches. Signs and symptoms in other organ systems may also linger. Clinical and imaging understanding are still evolving and may change as second- and third-wave infections hit, mutations occur, and vaccinations alter immunity. Stay tuned.

This piece first appeared in ARRS’ SRS Notes.

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_{Published in InPractice, Spring 2021: Volume 15, Issue 2}

Ancient Egyptians believed in an afterlife, that it was necessary to preserve the body after death. They perfected the process of mummifying the dead and buried them with their treasures in deep underground burial shafts, as in the Saqqara necropolis, or in expertly crafted mountain tombs, such as The Valley of Kings.

Paleoradiology: Imaging the Insides

In 1896, only a few months after the discovery of radiography, prior to its use on living humans, the first tests of the mysterious form of radiation were done on Egyptian mummies: one of a human and one of a cat. For the first time, x-ray film showed what was inside a mummy—without the need to dissect or even unwrap it. This event marked the birth of paleoradiology. The term refers to the use of non-invasive medical imaging modalities to study ancient human and animal remains or objects. Paleoradiology has evolved alongside medical imaging methods since its first uses. Today, a wide array of imaging modalities can be applied in archaeological work: conventional radiography, computed radiography, direct digital radiography, CT, micro-CT, endoscope, and MRI.

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Latest Findings in Saqqara

The Saqqara necropolis had been an ancient burial site since the Old Kingdom (ca. 2686–2181 B.C.) and was in use through much later periods for royalty, noblemen, priests, and even common people. Located about 30 kilometers south of present-day Cairo, Saqqara is a United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Site, hosting several archaeological excavations. Our recent mission at Saqqara, near the pyramid of King Teti (2323–2291 B.C.), unearthed great discoveries, including the temple of Queen Naeret, wife of King Teti. We also discovered 52 burial shafts (1522–1069 B.C.), containing coffi ns and a large number of rare artifacts: statues, board games, jewelry, amulets, stellas, as well as a papyrus 3 meters long by 1 meter wide with inscriptions from The Book of the Dead.

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Field Paleoradiology: Imaging With Archaeological Context

Paleoradiology can give us important information regarding the condition of a mummy or other ancient artifacts. Imaging mummies in a proper facility provides the optimum technical conditions. However, during excavations, it is often impractical to transport mummies to an imaging facility; it may even be harmful to vulnerable mummies. Instead, we use dedicated mobile imaging equipment to reach the mummies inside their tombs, a nondestructive option by which we can accomplish the same goals.

Traditional radiography has been used in field paleoradiology, but it is a cumbersome process to develop x-ray film in situ. Therefore, we use computed radiography and direct digital radiography technologies that have the advantage of being filmless systems, allowing for instant review and further manipulation of displayed images.

Conquering Technical Challenges

Careful site inspection is crucial preparation for the use of these field paleoradiology techniques. First, we determined the topography of the site—type, position, and relationship of the burial objects—and the data we intended to collect. We then identified any safety concerns, like applying radiation protection measures to conduct responsible field paleoradiology. The burial shaft in Saqqara was narrow, located 10 meters underground, leading to a room jammed with more than 50 coffins stacked atop one other. To better navigate this architecture, we designed detachable, lightweight radiography accessories that we could bring down and mount inside the burial shaft. These included a tube holder, a framed mummy bed with a plexiglass top, as well as a cassette holder for lateral viewing.

We x-rayed the closed coffins, altering radiographic exposure settings and positioning according to the mummification style and the structures we aimed to visualize: bony skeletons, dense amulets, or soft tissues.

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Interpretation Issues

Unlike patients, mummified remains do not have clinical history, and their historical context is often questionable. In fact, we identified a female mummy via radiograph that had been placed inside a coffin bearing inscriptions of a male’s name. The interpretation of mummy radiography also requires awareness of the ways in which the desiccation of human tissue affects its morphology, as well as the changes that resulted from different mummification practices. Additionally, some mummified bodies could be covered by artifacts that might be misinterpreted as pathology.

Thus far, our field paleoradiology study has been successful, resulting in valuable details regarding the sex, age at death, and possible cause of death for the mummies in the Saqqara burial shaft. We were also able to identify jewelry, amulets, and embalming materials inside many of the mummies that reflected their socioeconomic status. Moreover, we gained new perspectives in to the health status and diseases of the studied mummies, which can provide a better understanding of the natural history of disease, itself.

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Field paleoradiology allowed us to determine the mummies’ levels of preservation—to avoid moving vulnerable mummies unnecessarily. This enabled us to carry out a type of triage; mummies whose images suggested were in good condition and needed further investigations could be moved to a nearby medical facility for a CT scan, while those in more delicate condition were left in situ.

An invaluable tool to unlock the mysteries of mummification, paleoradiology puts us face-to-face with the men and women of ancient Egypt. Even thousands of years after their deaths, we’re still gaining remarkable insights into their lives simply from imaging their remains. Our work at the excavation site at Saqqara has not ended, of course, as what we have unearthed to date is merely a fraction of what we expect to exist.

This is my fourth and final column in an intense and unforgettable year. I have heard many opine that, somehow, we are going to be stronger and smarter and more evolved as a result of the many trials and tribulations we have faced. I don’t know so, but I do certainly hope so—hoping that something positive comes out of fear, pain, suffering, and uncertainty. In a certain way, I suppose we as physicians must possess such an optimistic mindset to survive and keep moving forward, despite the human misery and pain which we as radiologists literally see on a daily basis. As I think about the year, I can’t help but generate a list of questions for every challenging and engrossing topic, questions that I believe are intuitive, and questions that command my attention and test my optimism.

In this year, there has been no dearth of challenges, large and small, that affect and threaten to derail our daily lives and, therefore, the practice of our specialty. Our year opened with COVID-19, affecting our lives like an unpredictable and interrupted set of explosions. It affected the financial security of our department, our salaries, our personal sense of security (affecting ourselves and our families), and our ability to serve our patients, who were either sheltering at home or suffering from a disease which we incompletely understood. Just when we thought we had turned a corner, a larger wave comes crashing on top of us. We followed a handful of models telling us what to expect for a wave, insofar as magnitude and timing were concerned, realizing these predictions may as well have been science fiction. We congratulated ourselves for having several vaccinations, then realized that we could not effectively manage distributing and administering these vaccinations at anything faster than a trickle. As I write this column, we have administered 20 million vaccinations. By some estimates, we have to give 229 million doses to reach 70% of our population of 328 million for effective immunity, if conventional models hold. That means we are 8% of the way there, in the one year and five days since the first U.S. case was reported, on January 19, 2020. Are we committed to social distancing, as I drive up the Pacific coast and see numerous outdoor restaurants open, including some with maskless servers? Have we understood how we will ensure sufficient supply chain management to optimize vaccination production, delivery, and inoculation? Do we know what the longterm effects will be on our businesses and our children’s education, and have we established plans to address the fallout?

Before we could even gain our bearings in dealing with this new pandemic reality, we had to deal with the reality of race riots burning our streets, evidencing centuries of outrage and anger, threatening to tear us apart. As an occasional visitor who picks up fragmented and incomplete impressions of communities I have visited over the years, I never thought I would see Minneapolis in flames, or Seattle. I never imagined Portland as a city under siege. Did we divert to a better place after the Watts riots, or have we learned nothing? Did we resolve any of the contributory factors? Do we have a plan? Did we agree on a course of action that would address divergences and prevent future race riots?

Perhaps all of our problems are really only psychosocial, since we are clearly masters of the natural world, so we may have thought as we debated the validity or existence of global warming. Then, we blinked, and life and earth reminded us of our hubris and fallibility: massive, unprecedented forest fires choked our newly darkened ochre skies in California, we wiped ash off of our windshields in the mornings before going to work, burning flames took away every material possession our friends and families cherished. Homes and property disappeared with a snap of Thanos’ finger. What are we going to do during next year’s fire season? Have we done anything to better address those seemingly inevitable forest fires? Did we solve this problem? Are we ready to take on the next series of forest fires with greater effectiveness, confidence, and less loss of property and life?

And then, after a pandemic, race riots, and forest fires, the fourth horseman of the apocalypse turned out to be an angry mob, numbering in the tens of thousands, breaking into our seat of government while it was in session, intending to kill or physically harm our vice president, our senators, and our representatives. Despite the historic siege of the U.S. Capitol and all that it implied, the sun rose the following morning. In one fortnight, a peaceful transfer of power took place, and both the relevance and power of our systems and processes were reinforced. Are we now committed as a society to respectful disagreement? Will we need 20,000- plus troops in Washington, D.C., for inaugurations and legislative sessions to maintain law and order? Is the pendulum going to slow down and stop swinging way to the left, then way to the right?

So, I can’t help but ask these questions. I would presume you, too, have at least one question: what relevance does any of this have to the ARRS and this column? I would respond as follows: we do not have control over many external factors in our daily life, and we may not be able to easily address external questions; however, we do have the power to mobilize our societal membership to create a better future for our patients and society through the improved delivery of health care—the scope and scale at which we operate, that is—and we have to aspire to work as a committed group of colleagues to positively influence our professional future, which is in our control. Oddly enough, we also have the opportunity to address some of those external big questions through our society, particularly the aspects that overlap with our professional lives.

No matter what, we show up every day, and we do our best to help our fellow citizens. We are part of a group of compassionate, professional, and knowledgeable citizens. And even if the skies are on fire, if there is a literal plague among us, if there is revolution in the streets, if the future of our government is in question, we do our job to the best of our ability. Because we are hopeful, because we are committed, and because we have faith in our processes and our systems.

Our professional society reflects the same collective ethic. Our central belief hinges on having faith in the power of our collective. We hope to educate each other, support each other, and facilitate our collective progress, so that we may become the very best that we can be at what we do. It doesn’t matter if we have to convert the in-person ARRS Annual Meeting to an all-virtual convening, if we must distribute our educational materials in a more effective way, if we need to focus our practice communications on the realities of pandemic management, if we need to share fast-breaking scientific communications regarding COVID-19 to help you work better and smarter, or if we utilize our platforms and publications as a bridge to timely and essential topics, such as diversity in health care. This professional society tirelessly does what it has to do to inspire and empower you.

As I hand off this column to my dear successors, please allow me to ask three things of you, dear reader. I ask you to care, I ask you to engage, and I ask you to volunteer.

I ask you to care because that will fuel your efforts. Caring creates motivational reserves that are virtually limitless. Caring provides purpose, and purpose allows us to muscle through uncertainty in the quest to find solutions. Everything that happens should matter to you, be it race riots or forest fires. Whether or not they are in your backyard, you should care, and you should want to care. Once you care, you will think of solutions, you will share these solutions, and, ultimately, we will collectively iterate and address our problems. It starts with caring. Great things are only possible when we care about the world around us and the events that affect our lives.

I ask you to engage because not one of us is an island, and no individual one of us has the answers. Answers are generated by groups and teams. This means choosing to effectively establish dialogue, which is bidirectional information fl ow necessitating transmitting and receiving. If I’m only listening, then I’m not contributing to the conversation. If I’m only speaking, then I’m not listening suffi ciently to contribute to the conversation in a meaningful way. I have to train myself to listen, and I have to train how to express myself. When we connect with each other—and we translate our caring into effective communication—if we are prepared to both listen and speak, then we leverage the cooperative wisdom of the crowd into a powerful understanding. No matter how fascinating and experienced a single life may be, it cannot compare with the shared experiences and gained lessons and perspectives of a dozen lives. Great things are only possible when we communicate with each other.

I ask you to volunteer, so we can all be part of something bigger, so we can align our efforts and energies, so we can not only imagine positive change, but we can realize it. Part of the beauty of professional societies is the remarkable power evidenced in likeminded individuals standing side-by-side with their shoulders to the wheel, pushing hard, and doing the best they can to create a better future. Sometimes, this testbed demonstrates remarkable returns. As one small example, I can see a brief line of succession where our societal executive leadership is concerned, and I am inspired by the depth of character, thoughtfulness, and energy I see for the next several cycles and years. We have great leaders moving into their ranks, who will serve our society exquisitely well. Great things are only possible when we work together.

Please care, engage, and volunteer. You secure our brightest future, and you validate all that we do. And for that, I owe you all an unrepayable debt of gratitude.

_{Published March 16, 2020}

First used as a hashtag and codified more than a decade ago, altmetrics present a more comprehensive alternative to the traditional citation impact criteria of scientific publishing. Mainstream media, online-first publications, social and academic networks, public policy documents, post-publication forums, Wikipedia even—these complementary bibliometrics help to paint a truer picture of engagement with scholarly work, especially work that finds second, often third lives beyond the towers of academia.

Altmetric.com is a data science company uniquely leveraged to capture this full array of online sources and collate all the disparate activity accordingly. In fact, just to the right of the abstract for every AJR article published on AJRonline.org, you will now see a multicolored “donut” badge visualizing a real-time summary of the attention that article is receiving (red for news outlets, orange for blogs, blues for social media, etc.) For numerical context, you will notice the data output gets assigned an Altmetric Attention Score, a high-level assessment of both the quality and the quantity of attention said AJR article is receiving, weighted according to the relative reach of each attention source, itself.

Perhaps most importantly, if you click anywhere on the embedded donut, then you will be directed to a dedicated AJR Altmetric details page. Here, you, too, can follow the myriad conversations this specific AJR article is engendering—geographic and demographically—in one convenient, shareable URL.

Want to be notified whenever someone shares or discusses the details for a certain AJR article? You can sign up for e-mail alerts under the Summary Tab of any AJR Altmetric details page. While you are free to sign up for notifications regarding multiple AJR Altmetric research outputs, you will only receive a single email alert, sent via once-a-day digest.