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The diagnosis of interstitial lung disease (ILD) involves multidisciplinary collaboration among radiology, pulmonary medicine, rheumatology, and anatomic pathology disciplines. Imaging findings play a major role in the diagnosis of a variety of diffuse lung diseases, and the radiologist’s input into the ultimate diagnosis is often substantial [1]. Imaging is of critical importance in the diagnosis of ILD, although the accurate interpretation of characteristic high-resolution CT (HRCT) findings can be challenging. This InPractice article will review common pitfalls for those tasked with interpretation of CT in the diagnosis of ILD with a focus on avoiding common errors, identifying distinguishing features of specific diagnoses, and recognizing entities with which CT has limited sensitivity.

Overdiagnosis of Usual Interstitial Pneumonia Pattern 

The goals of the radiologist in the evaluation of a patient with suspected pulmonary fibrosis are to determine whether a diffuse lung disease is present, determine the pattern of fibrosis, and provide an appropriate differential diagnosis. Usual interstitial pneumonia (UIP) pattern of pulmonary fibrosis is the most common ILD. UIP is most frequently idiopathic, but can also be secondary to connective tissue disease, medications, or exposure to asbestos [2]. Given the pervasiveness of this diagnosis, radiologists participating in the multidisciplinary diagnosis of patients with suspected ILD are frequently asked whether CT findings support a UIP diagnosis.Fortunately, guidelines can increase the confidence of radiologists in correctly identifying patients with UIP. The American Thoracic Society guidelines for the diagnosis of UIP pattern break down CT findings into four categories: UIP, probable UIP, indeterminate for UIP, and alternative diagnosis. The CT findings indicative of UIP pattern include subpleural and basal predominant fibrosis in addition to honeycombing, with or without traction bronchiectasis (Fig. 1).

Fig. 1—73-year-old man with idiopathic pulmonary fibrosis. HRCT scan shows usual interstitial pneumonia pattern of fibrosis characterized by subpleural and basal distribution of fibrosis with honeycombing.

This is to be distinguished from the probable UIP pattern, which is characterized by the same distribution of fibrosis including reticulation and traction bronchiectasis, but the absence of honeycombing [3].

The PPV of UIP pattern on CT for histologic UIP at surgical lung biopsy exceeds 90%, and as such, surgical lung biopsy is rarely performed when a confident diagnosis of UIP pattern can be made from imaging [3, 4]. For this reason, a diagnosis of UIP should only be made when the radiologist is confident that the imaging findings are consistent with this pattern, because often further diagnostic testing will not be pursued, potentially depriving the patient of the opportunity to receive the correct diagnosis. This distinction is not trivial; those diagnosed with UIP may be treated with antifibrotic medications and thus be subject to the side effects thereof. Not surprisingly, patients treated with antifibrotics for UIP will not be given immunosuppressive therapy, which could be a more appropriate treatment in the setting of another histologic diagnosis (e.g., nonspecific interstitial pneumonia) nor will an extensive search for exposures be pursued (e.g., as is done with patients with hypersensitivity pneumonitis).

Given the importance of correctly making a diagnosis of UIP and avoiding overdiagnosis of this entity, radiologists interpreting HRCT should be mindful of the potential pitfalls described in the following sections.

Correctly Distinguish Honeycombing From Mimics

Honeycombing can be confidently diagnosed when there is a group of round clustered air-filled cysts in a row or cluster in the subpleural lung [5]. The subpleural involvement in honeycombing is critical in distinguishing it from other abnormalities. Multiple layers of cysts increase the reader’s confidence in honeycombing but are not required for diagnosis. Honeycomb cysts usually range in size from 3 to 10 mm and have relatively thick, well-defined walls [6]. In general, there is moderate agreement among radiologists for the presence of honeycombing, with kappa values ranging from 0.4 to 0.6 in one series comparing 43 different observers. There was disagreement on the presence of honeycombing in 29% of these cases [7]. Use of the above general rules for the features of honeycombing is helpful when distinguishing from common mimics. The most frequent findings mistaken for honeycombing include traction bronchiectasis, cystic lung disease, emphysema, and subpleural reticulation [8].To distinguish traction bronchiectasis from honeycombing, the shape of the air-filled structure should be noted. Airways in traction bronchiectasis are tubular in shape, which may be best seen on multiplanar reformatted images. Additionally, air-filled structures in the central or peribronchovascular lung are not consistent with honeycombing and are very likely a result of dilated airways (Fig. 2).

Fig. 2—Patient with scleroderma and fibrotic nonspecific interstitial pneumonia. Left, HRCT scan shows traction bronchiectasis mimicking honeycombing. Right, HRCT scan shows that air-filled structures spare subpleural lung.

Destruction of airspaces in patients with emphysema can lead to the presence of air-filled structures in the subpleural lung; however, these structures can be distinguished from honeycombing by the overall size of emphysematous spaces that in general are larger than honeycombing cysts, the presence of paper-thin walls in emphysema in contrast to thicker walls of honeycombing, and the absence of other findings of fibrosis such as reticulation and traction bronchiectasis in patients with emphysema [9] (Fig. 3).

Fig. 3—HRCT scan shows patient with paraseptal emphysema with extensive involvement of subpleural lung, but without well-defined walls or other findings of fibrosis.

Cystic lung disease can be distinguished from honeycombing given that the cysts are often larger, scattered throughout the lung rather than clustered, and not subpleural in distribution. Shape can also be helpful in distinguishing cystic lung disease from honeycombing in that honeycomb cysts are round, whereas several cystic lung diseases are characterized by either oblong or elliptical cysts (Birt-Hogg-Dubé syndrome) or irregularly shaped cysts (Langerhans cell histiocytosis) [10].

Reticulation or fine lines in the subpleural lung can also be mistakenly identified as honeycombing. To avoid this pitfall, radiologists should ensure that the subpleural abnormality is air density rather than lung density (Fig. 4).

Fig. 4—HRCT scan shows thin lines in subpleural lung in patient with pulmonary fibrosis characterized by diffuse reticulation. Abnormality in subpleural lung is lung density (same as more central lung parenchyma) rather than air density (for example in trachea), which is helpful in confirming that these findings do not represent honeycombing.

Identify Whether the Distribution of Fibrosis Is Subpleural and Basal

Fig. 5—Fibrosis with honeycombing in atypical distribution. Left, Axial HRCT scan shows diffuse fibrosis in association with ground-glass opacity. Diagnosis was hypersensitivity pneumonitis. Right, Coronal HRCT scan shows upper lobe–predominant fibrosis. Diagnosis was sarcoidosis.

Fibrosis that is diffuse in the axial plane or predominately in an upper lung, central, or peribronchovascular distribution may indeed be associated with honeycombing but nonetheless be caused by other entities such as nonspecific interstitial pneumonia, sarcoidosis, or hypersensitivity pneumonitis [11, 12]. Subpleural and basal distribution of fibrosis is essential to describing a pattern of fibrosis consistent with UIP at imaging. A percentage of cases with atypical distributions of fibrosis and honeycombing may be subsequently identified as UIP after biopsy; however, these cases are exactly those that benefit from surgical lung biopsy because there is a relatively high chance (70%) that another diagnosis will be found [12, 13].

Identify Inconsistent Findings

Numerous CT findings are of a diagnosis other than UIP pattern including the presence of significant ground-glass opacity, marked mosaic attenuation, nodules, and consolidation [13]. Each of these findings points the radiologist toward a diagnosis other than UIP. Patients with nonspecific interstitial pneumonia (i.e., ground-glass opacities), hypersensitivity pneumonitis (i.e., mosaic attenuation), sarcoidosis (i.e., nodules), and organizing pneumonia (i.e., consolidation) can all be identified by the presence of these features, and the presence of honeycombing should not detract from the CT findings that indicate these alternative diagnoses.

Overdiagnosis of Cystic Lung Disease

Many of the pitfalls in correctly identifying honeycombing and distinguishing honeycombing from mimics can also be applied to the correct diagnosis of cystic lung disease. When considering a potential diagnosis of cystic lung disease, it is important to again identify mimics: honeycombing, dilated airways and bronchiectasis, and emphysema. The extent of abnormality, from mild to severe, is also important to consider in this context. A few scattered pulmonary cysts may be considered in the spectrum of normal, particularly for older patients, and are most likely postinfectious rather than indicative of a cystic lung disease [14].Whereas the primary features of bronchiectasis (i.e., tubular shape) and honey- combing (i.e., thick walls, clustered, subpleural) make distinguishing these entities from cystic lung disease more straightforward, correctly distinguishing cystic lung disease from emphysema can be challenging. This challenge is in part because both entities can have very thin or imperceptible walls and can occur on a spectrum from mild to severe. The presence of the “central dot” sign in which the centrilobular artery is seen within an emphysematous space can be helpful in correctly distinguishing centrilobular emphysema from a cystic lung disease; however, this finding is not reliably seen in all regions of emphysema [15] (Fig. 6).

Fig. 6—Axial HRCT scan shows “central dot” sign in patient with centrilobular emphysema.

In general, pulmonary cysts are fewer in number, noncentrilobular in distribution, and have thicker or more perceptible walls compared with centrilobular emphysema [16]. Paraseptal emphysema and panlobular emphysema are less frequently mistaken for cystic lung disease because of their strongly subpleural distribution and overall extent respectively.

Distinguishing cystic lung diseases from one another can also be challenging; however, several key features including cyst shape, number, distribution, and classic demographic factors and associated findings can aid the radiologist in providing an appropriate differential diagnosis. Using these features allows the radiologist to narrow the differential diagnosis for a particular case to fit the specific CT features seen rather than including a long differential diagnosis consisting of all cystic lung diseases [17]:

Distinguishing Features of Cystic Lung Diseases

Disease	Cyst			Other Indicators
	Shape	Number	Distribution
Lymphangioleiomyomatosis Birt-Hogg-Dubé syndromeLight chain deposition disease Pulmonary Langerhanscell histiocytosisLymphocytic interstitial pneumonia	RoundElliptical or oval Round or oval Irregular Round	Few to many Few Few to many ManyFew	RandomJuxtapleural, perifissural, lower lungLower lung Upper lungRandom	Female sex, renal angiomyolipoma Pneumothorax, renal mass Cysts and nodules Smoker Ground-glass opacity, connective tissue disease

The presence of associated features may also be helpful in correctly identifying the presence and cause of a cystic lung disease when the abnormalities are mild and nonspecific.

Pitfalls in the Interpretation of Mosaic Attenuation and Small Airways Disease

Small airways disease may present a significant challenge in HRCT interpretation and typically manifests on HRCT as two main categories of findings: nodules or mosaic attenuation. Nodules may correspond to any of the following histologic findings: inflammation within the lumen of the airways, alveolar disease centered on the airway, or peribronchiolar interstitial inflammation. Diseases categorized by nodules are generally detected on HRCT with high sensitivity and are typically straightforward to classify.

Small airways obstruction causes hypoxia distal to the area of obstruction, resulting in regional areas of reflex vasoconstriction. Given that approximately 50% of lung attenuation is due to blood flow, regional reductions in perfusion result in a decrease in lung attenuation. These regional areas of decreased lung attenuation are described as “mosaic attenuation” or “mosaic perfusion.” More precisely, mosaic attenuation is a more general term and describes the presence of geographic areas of different lung attenuation but does not make a determination as to which lung is abnormal, whether the opaque or lucent lung. Mosaic perfusion, on the other hand, implies specifically that the lucent lung is abnormal and is the finding that most precisely corresponds to airways obstruction with reflex vasoconstriction [18].

The differential diagnosis of mosaic perfusion is broad and encompasses a wide variety of both small airways diseases and pulmonary vascular diseases. It may be associated with other findings (e.g., nodules) or may be seen in isolation. The presence of mosaic perfusion is most helpful in formulating a differential diagnosis when seen in isolation, in which case it may be due to pulmonary vascular disease (mainly chronic thromboembolic disease), constrictive bronchiolitis, asthma, and hypersensitivity pneumonitis [19].Diseases characterized by isolated mosaic perfusion may present a significant challenge for several reasons. First, mosaic perfusion is a finding that is sometimes difficult to detect on HRCT. The subtle difference in attenuation frequently seen between the normal and more lucent lung is better observed when a narrow window is applied to the HRCT examination, accentuating the attenuation differences (Fig. 7).

Fig. 7—Mosaic perfusion and importance of windowing in high-resolution CT (HRCT). Left, Standard lung window in HRCT shows heterogeneous lung attenuation with subtle difference between opaque and lucent lung. Right, More narrow window accentuates difference between two lung attenuations and increases sensitivity for detection of mosaic perfusion.

Second, when small airways or vascular diseases are diffuse in nature they result in a global and uniform decrease in lung perfusion. A diffuse HRCT abnormality is difficult to identify because there is no normal lung with which to compare the abnormality. This is most commonly seen in severe constrictive bronchiolitis [20]. Additionally, diffuse air trapping on expiratory CT is difficult to distinguish from poor timing or an inadequate respiratory effort. In these cases the HRCT scan may appear normal despite profound dyspnea and marked obstruction on pulmonary function tests. The diffuse but subtle decrease in lung attenuation is often not detected given its homogeneous nature.Mosaic perfusion (i.e., abnormal lucent lung) should be distinguished from ground-glass opacity (i.e., abnormal opaque lung), however, this distinction also has several pitfalls. Features that favor mosaic perfusion include sharp borders between the two regions of lung, smaller vessels in the lucent lung, and air trapping on expiration in the areas that were lucent on inspiration that only present in small airways disease (Fig. 8).

Fig. 8—Features of mosaic perfusion on high-resolution CT (HRCT). First two images, Axial HRCT scans show typical features of mosaic perfusion including sharp borders between opaque and lucent lung (first), larger vessels in normal more opaque lung (second), and air trapping on dynamic expiratory images. Third and fourth image, Paired inspiratory (third) and expiratory (fourth) HRCT images show heterogeneous lung attenuation on inspiration and air trapping on expiration.

None of these features are perfect in making this distinction, however. For instance, diseases characterized by ground-glass opacity may occasionally be geographic with sharp borders (Fig. 9). 

Fig. 9—Axial high-resolution CT scan shows ground-glass opacity due to SARS-CoV-2 infection. Sharp borders between areas of opaque and lucent lung usually suggest that lucent lung is abnormal and pattern is mosaic perfusion. However, sharp borders may occasionally be seen in ground-glass opacity, such as in this case. Normal lung and areas of ground-glass opacity show marked difference in attenuation.

In these cases, the absolute difference in attenuation between the two regions of lung may be helpful. Mosaic perfusion typically results in a relatively subtle difference in attenuation between the diseased lucent lung and the normal opaque lung. Ground-glass opacity, on the other hand, typically shows a more marked difference in density between the two areas [21]. That being said, when mosaic perfusion results in significant shunting of blood away from the diseased areas, a greater difference in lung attenuation may be present. These cases are not infrequently misinterpreted as ground-glass opacity. Another challenge in the distinction between mosaic perfusion and ground-glass opacity is that many cases of mosaic perfusion will not show a significant difference in vessel size between the lucent and opaque lung. Last, pulmonary vascular diseases characterized by mosaic perfusion will not show air trapping on expiratory CT. Thus, expiratory CT is not helpful in the diagnosis of diseases such as chronic pulmonary embolism [22].

Pitfalls in the Interpretation of Diffuse Nodular Lung Disease

Formulating a differential diagnosis of diffuse nodular lung disease is done by identifying the distribution of nodules in relation to the pulmonary lobular anatomy. Three distributions have been described: perilymphatic, random, and centrilobular [23–25]. The perilymphatic distribution is characterized by patchy, clustered nodules that are concentrated most frequently in the peribronchovascular and subpleural interstitium. Random nodules will also be seen in the subpleural lung; however, they are not clustered but instead show diffuse homogeneous lung involvement. Centrilobular nodules are characterized by a distinct lack of nodules involving the subpleural interstitium.

The determination of the predominant pattern of diffuse nodular lung disease has several pitfalls. The perilymphatic pattern shows significant heterogeneity in the distribution of nodules. Although peribronchovascular and subpleural nodules are most typical, nodules in the interlobular septa, which also contain lymphatics, may predominate [26]. These cases may be confused for lymphangitic spread of tumor or pulmonary edema, although the thickening of the interlobular septa in pulmonary edema should be smooth, not nodular. The centrilobular interstitium is continuous with the peribronchovascular interstitium. Rarely, lymphatic diseases may have a predominance of centrilobular nodules overlapping with the centrilobular distribution (Fig. 10). 

Fig. 10—Axial high-resolution CT scan shows centrilobular nodules in perilymphatic disease. Many centrilobular nodules (arrows) are present in this patient with sarcoidosis. Subpleural nodules reflect perilymphatic distribution of disease.

Although many centrilobular nodules may be present in lymphatic diseases, nodules should also be seen in the peribronchovascular or sub- pleural interstitium. This is in distinction to the centrilobular pattern in which only centrilobular nodules are present and no subpleural nodules should be seen. Lastly, diseases typically associated with a perilymphatic distribution of nodules (such as sarcoidosis) may occasionally show a fairly homogeneous involvement of the lung, mimicking a random distribution [27] (Fig. 11). 

Fig. 11—Axial high-resolution CT scan shows perilymphatic distribution mimicking random nodules. Innumerable tiny nodules are present. Although pattern resembles random distribution, heterogeneous distribution in lung shows proportionally more nodules along fissures (arrows) than would be expected for random distribution.

A greater number of nodules in the subpleural or peribronchovascular interstitium may be the only clue that the distribution is perilymphatic.

Diseases for Which HRCT Has Limited Sensitivity

Certain categories of diseases may present with significant symptoms or pulmonary function test abnormalities but only manifest with mild HRCT abnormalities. Understanding the subtle imaging clues that may be present in these diseases is important in increasing the sensitivity of imaging for diagnosis. The two main categories of disease that show this discrepancy between symptoms and pulmonary function tests and HRCT manifestations of disease include small airways diseases and pulmonary vascular diseases. As discussed above, small airways diseases that manifest as isolated mosaic perfusion (e.g., constrictive bronchiolitis) may be difficult to detect on HRCT. The subtle increase in lung lucency associated with these diseases may be difficult to see, especially when the disease is diffuse in distribution [28]. Pulmonary vascular diseases such as pulmonary hypertension or chronic pulmonary embolism may also present with subtle findings. Centrilobular nodules or ground-glass attenuation or mosaic perfusion are often the only findings present and are typically much less severe than would be predicted by the patient’s advanced clinical symptoms. The lungs may appear completely normal in some patients with pulmonary vascular disease, in which case the only manifestation of pulmonary vascular disease may be extrapulmonary findings such as an enlarged pulmonary artery or right ventricular enlargement [29]. Lastly, pulmonary symptoms and pulmonary function test abnormalities might have one of several nonlung causes including pleural fibrosis, diaphragmatic dysfunction, and musculoskeletal abnormalities. All of these should be evaluated in patients with significant symptoms but no evidence of lung abnormalities on HRCT. 

Awareness of common pitfalls in the diagnosis of ILD including the UIP pattern of fibrosis, cystic lung disease, airways disease, diffuse nodular disease, and lung diseases with subtle HRCT findings will better equip the radiologist to contribute to the multidisciplinary diagnosis of patients with ILD.

References

1. Hovinga M, Sprengers R, Kauczor HU, Schaefer-Prokop C. CT imaging of interstitial lung diseases. In: Schoepf UJ, Meinel FG, eds. Multidetector-row CT of the thorax. Springer, 2016:105–130
2. Wuyts WA, Cavazza A, Rossi G, Bonella F, Sverzellati N, Spagnolo P. Differential diagnosis of usual interstitial pneumonia: when is it truly idiopathic? Eur Respir Rev 2014; 23:308–319
3. Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/ JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med 2022; 205:e18–e47
4. Brownell R, Moua T, Henry TS, et al. The use of pre- test probability increases the value of high-resolution CT in diagnosing usual interstitial pneumonia. Thorax 2017; 72:424–429
5. Hobbs S, Chung JH, Leb J, Kaproth-Joslin K, Lynch DA. Practical imaging interpretation in patients suspected of having idiopathic pulmonary fibrosis: official recommendations from the Radiology Working Group of the Pulmonary Fibrosis Foundation. Radiol Cardiothorac Imaging 2021; 3:e200279
6. Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology 2008; 246:697–722
7. Watadani T, Sakai F, Johkoh T, et al. Interobserver variability in the CT assessment of honeycombing in the lungs. Radiology 2013; 266:936–944
8. Arakawa H, Honma K. Honeycomb lung: history and current concepts. AJR 2011; 196:773–782
9. Devaraj A. Imaging: how to recognise idiopathic pulmonary fibrosis. Eur Respir Rev 2014; 23:215–219
10. Grant LA, Babar J, Griffin N. Cysts, cavities, and honeycombing in multisystem disorders: differential diagnosis and findings on thin-section CT. Clin Ra- diol 2009; 64:439–448
11. Abehsera M, Valeyre D, Grenier P, Jaillet H, Battesti JP, Braunerl MW. Sarcoidosis with pulmonary fibro- sis: CT patterns and correlation with pulmonary function. AJR 2000; 174:1751–1757
12. Silva CIS, Churg A, Müller NL. Hypersensitivity pneumonitis: spectrum of high-resolution CT and pathologic findings. AJR 2007; 188:334–344
13. Raghu G, Remy-Jardin M, Myers JL, et al. Diagnosis of idiopathic pulmonary fibrosis. an official ATS/ ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med 2018; 198:e44–e68
14. Araki T, Nishino M, Gao W, et al. Pulmonary cysts identified on chest CT: are they part of aging change or of clinical significance? Thorax 2015; 70:1156–1162
15. Friedman PJ. Imaging studies in emphysema. Proc Am Thorac Soc 2008; 5:494–500
16. Lee KC, Kang EY, Yong HS, et al. A stepwise diagnostic approach to cystic lung diseases for radiologists. Korean J Radiol 2019; 20:1368–1380
17. Ferreira Francisco FA, Soares Souza A, Zanetti G, Marchiori E. Multiple cystic lung disease. Eur Respir Rev 2015; 24:552–564
18. Stern EJ, Müller NL, Swensen SJ, Hartman TE. CT mosaic pattern of lung attenuation: etiologies and terminology. J Thorac Imaging 1995; 10:294–297
19. Parambil JG, Yi ES, Ryu JH. Obstructive bronchiolar disease identified by CT in the non-transplant population: analysis of 29 consecutive cases. Respirology 2009; 14:443–448
20. Gunn ML, Godwin JD, Kanne JP, Flowers ME, Chien JW. High-resolution CT findings of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation. J Thorac Imaging 2008; 23:244–250
21. Kligerman SJ, Henry T, Lin CT, et al. Mosaic attenuation: etiology, methods of differentiation, and pitfalls. RadioGraphics 2015; 35:1360–1380
22. Ridge CA, Bankier AA, Eisenberg RL. Mosaic attenuation. AJR 2011; 197:[web]W970–W977
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What would you do if your hospital was going to run out of iodinated contrast? Reduce the amount of IV contrast used for each CT scan? Administer multiple doses of IV contrast from a single-use vial? Defer non-urgent contrast-enhanced CT? Utilize alternative modalities, such as ultrasound, MRI, or PET/CT?

In March of last year, supply chain disruptions in China resulted in an unexpected 80% reduction in global supply of iohexol (Omnipaque, GE Healthcare). Hospitals needed to make immediate decisions about ways to conserve contrast. Otherwise, they may run out. The American College of Radiology [1], Radiological Society of North America [2], and American Hospital Association [3] released statements, and AJR continues to publish all of its research regarding the contrast media shortage as free and open access [4]. 

The situation was rapidly evolving, but getting more inventory wasn’t an option. As part of the response to the contrast shortage, our hospital system created and implemented an electronic health record (EHR) -based solution to help reduce iodinated contrast usage by targeting referring provider CT ordering patterns [5]. 

First, we added a sidebar to the ordering panel that presented an alert describing the shortage (Fig. 1), including the following strategies for imaging patients (Intervention 1; May 10, 2022): 

Fig. 1—Screenshot from electronic health record shows sidebar text displayed to referring clinicians after placing orders for body CT (defined as CT of neck, chest, or abdomen and pelvis) that describes iohexol shortage and provides appropriate strategies for iodinated contrast media conservation.

Oncologic Imaging

• Avoid contrast for chest CT done alone to assess metastatic disease, unless primary is thoracic malignancy
• For chest/abdomen/pelvis restaging exams, consider combining non-contrast CT chest with abdominal MRI
• Consider abdominal MRI for assessment of hepatic metastases

Non-Oncologic Imaging

• CT for pulmonary embolism (PE)—utilize risk scoring methodology, such as Wells criteria or pulmonary embolism rule-out criteria (PERC), before pursuing CT
• CT chest for lung parenchymal disease does not require IV contrast
• In case of suspected musculoskeletal infection, use MRI

Emergency Imaging

Neuro

• CTA head/neck—contrast needed to assess large vessel occlusion in patients within stroke treatment window. For subacute stroke outside window, please consider non-contrast head CT, followed by MRI, when appropriate
• Reconsider CTA utilization for low-yield indications, including headache and dizziness

Thoracic

• CT for PE—utilize risk scoring methodology (i.e., Wells or PERC)
• CT chest for lung parenchymal disease doesn’t require IV contrast

Abdomen/pelvis

• Pancreatitis and pyelonephritis—CT rarely indicated for these diagnoses
• For primary hepatobiliary concerns, right upper quadrant ultrasound remains an excellent choice, unless high likelihood that CT also needed to explain symptoms
• GI Bleeding—reserve CTA for patients with bright red blood per rectum or hemodynamic instability in whom acute intervention might be needed

Trauma

• CT torso with IV contrast is needed to assess for parenchymal or vascular injury.
• Consider non-contrast CT torso imaging (or radiography) in patients with low suspicion for parenchymal or vascular injury, such as elderly patients with ground-level fall and suspicion for rib fracture or thoracic/lumbar spine fracture

Next, we required referrers to enter additional clinical information into a free text field describing why iodinated contrast was needed for the CT (Intervention 2; May 16, 2022).

The number of patients undergoing contrast-enhanced CTs per day decreased from 726 prior to the interventions, to 689 after intervention 1, to 639 after intervention 2 (Fig. 2). 

Fig. 2—Box-and-whisker plots show changes during preintervention and postintervention periods in number of patients who underwent contrast-enhanced CT examinations per day. Centerlines represent medians, ends of boxes represent interquartile ranges, ends of whiskers represent interdecile ranges, and dots beyond ends of whiskers represent outliers.

The overall number of patients undergoing CT per day decreased, as did the percentage of CT exams performed with IV contrast. These decreases were seen for all CT, as well as body CT alone (neck/chest/abdomen/pelvis). As expected, there was a decrease in requests for contrast-enhanced CT and a corresponding increase in requests for non-contrast CT.

In summary, an EHR intervention was able to reduce the number of contrast-enhanced CTs per day by 12%, the total number of CTs performed per day decreased 2.7%, and the percentage of CTs performed with IV contrast per day decreased from 53.8% to 48.6%. This simple intervention was implemented within weeks of the onset of the shortage and led to rapid practice change. Along with other conservation strategies, our health system was able to avoid rationing and continue near normal operations.

References

1. Wang CL, Asch D Cavallo J. Statement from the ACR Committee on Drugs and Contrast Media on the Intravenous Iodinated Contrast Media Shortage. J Am Coll Radiol 2022; 19:834-835 
2. Grist TM, Canon CL, Fishman EK, Kohi MP, Mossa-Basha M. Short-, mid-, and long-term strategies to manage the shortage of iohexol. Radiol 2022; 304:2 
3. Shortage of Contrast Media for CT Imaging Affecting Hospitals and Health Systems. American Hospital Association website. www.aha.org/advisory/2022-05-12-shortage-contrast-media-ct-imaging-affecting-hospitals-and-health-systems. Published May 12, 2022. Accessed August 8, 2023
4. Contrast Media Shortage (Free). AJR website. www.AJRonline.org/topic/article-collection/cms1. Updated June 21, 2023. Accessed August 8, 2023
5. Glazer DI, Lucier DJ, Sisodia RC. Electronic health record order entry–based interventions in response to a global iodinated contrast media shortage: impact on contrast-enhanced CT utilization. AJR 2022; 220:1  

Over the last few years, we in radiology have faced incredible and unprecedented challenges in our day-to-day work, and this is true regardless of our specific work environments. Why? The pandemic, which has touched everyone, has had a profound impact on the workplace in general. It has changed how we work, approach work, and shaped our opinions of work. And it is not just the pandemic—it’s other phenomena: political polarization, social unrest, changes in home life and education, remote work. The pandemic and its effects led to a great resignation, and as a result, many of our sites are now understaffed. One in five doctors plan to leave their current practice in two years; two in five nurses plan to leave their practice in two years; one in three doctors expect to work less next year.

Health care workers have far greater demands now than in the pre-pandemic times. The delivery of health care has changed dramatically and quickly over the last few years. There is unprecedented “consumerism” in medicine now with a mandate to improve and rethink patient access, to provide more and better mental health services to our populations, and to have transparent pricing. 

In radiology, whether you work in a large or small private practice, remotely by yourself, an academic department in a medical center, or part of a mega radiology practice, there has been a palpable shortage of radiologists. This shortage is fueled by a trend toward exclusive subspecialization with declining numbers of radiologists who can handle general work, ever-increasing expectations for service to our patients, referring doctors, hospitals, and health care systems. We have been stretched thinner. There is a desire by radiologists to have more flexible work hours or, simply stated, to work less hours overall compared to years past. There is a concern about what role artificial intelligence and machine learning will play; will we be displaced? Reimbursement has been decreasing relative to inflation and compared with other specialties. As a result of these realities and others, there is clear evidence of burnout among radiologists, similar to health care workers in other specialties. On top of that, sometimes, we find that the leaders in our organizations may be distant, or too corporate, or suffer from “toxic positivity,” which may be worse than “toxic negativity.”

There has been a steady headwind for years, but it now feels like a gale force wind. And a lot of this feels out of our control. So, goodness, how do we manage all of this?

Hold on, let’s take a breath. One strategy that we can embrace and control is to develop a culture of teams within our workplaces. In fact, I have titled this series “The Teamwork Imperative” because we must establish teamwork as a core value within the radiology workforce. I believe that if we foster a culture of teams, we can mitigate and shield ourselves from some of these headwinds.

Obviously, I’m not the first to suggest the importance of teams. It’s all over the blogs and press and our literature. In last year’s presidential address, Dr. Gary Whitman alluded to the importance of a culture of resiliency and teamwork [1]. I will also shout out to the 2021 president of our society, Dr. Jonathan Kruskal. Dr. Kruskal, along with colleagues and ARRS staff, launched RadTeams.org—an open-access website that helps radiologists establish, grow, and sustain high-functioning radiology teams, along with other aspects of imaging wellness and wellbeing [2]. Check it out. And you have to revisit the exciting ARRS Radiology Wellness Summit from the Annual Meeting that addressed, among many things, the importance of teams [3, 4].

Let me be clear. Here, when I say teams, I am specifically not referring to the “macro teams” that many of us find ourselves in. For example, at Duke Health, it is said that the 30,000-plus employees are my “teammates.” That very well may be true. But no, I am referring to your local and focal team. I am referring to the individuals that you rely on daily or weekly to deliver your work product. It’s the folks you huddle with. And the teams develop where you huddle. If you are in training, I am referring to your team of co-residents, your chief residents, maybe your program director or program coordinator who you lean on. If you are in a private practice, I am referring to those that you share physical space with, or perhaps switch call with, or the individuals you show difficult cases to, or the referring docs you have developed close relationships with, and who rely on you to deliver care. In an academic environment like mine, it might be the members of your subspecialty division. If done well, the division pulls together as a team to deliver care, service, teaching, and research.

Those divisions that have a culture of team are far more effective than those who are unable to act as a team. If you are lucky enough to have these local and focal teams (and these often form and evolve organically), many challenges at work open up and become more manageable and attainable. The clouds begin to lift. Specifically, your deliverables, whatever they may be, are far more easily and effectively achieved if you have your team and approach your work from the perspective of that team.

Work becomes more efficient and fulfilling and, frankly, more fun. The work becomes more manageable, with more aspects under your control. You become more engaged. And that then becomes an antidote to burnout. Teams, therefore, contribute to retention.

Coaches discuss this all the time. Just as Dr. Whitman was fond of quoting UCLA basketball coach John Wooden in his InPractice columns [5, 6], I’ll borrow here from Mike Krzyzewski, the legendary Duke basketball coach. “Coach K” famously talked about the five keys to an effective team and likened the keys to the fingers on a hand. Each finger is individual and can stand alone, but when the fingers come together into a fist, the fist proves to be much stronger than the sum of the individual fingers. 

Communication, trust, responsibility, caring, productivity—my next installments of “The Teamwork Imperative” will discuss all five of these fingers and more, so please do stay tuned!

References

1. Whitman GJ. Resilience in Radiology. ARRS InPractice website. www.radfyi.org/resilience-in-radiology. Published May 13, 2022. Accessed July 26, 2023
2. ARRS RadTeams website. About page. RadTeams.org/About. Accessed July 26, 2023
3. Kruskal J, Azour L, Goldin J. Introducing the ARRS Radiology Wellness Summit in Hawaii—Time to Get Serious! ARRS InPractice website. www.radfyi.org/radiology-wellness-summit-arrs-2023-hawaii. Published August 1, 2022. Accessed July 26, 2023
4. Kruskal J, Azour L, Goldin J. A Lighthouse for Radiology Wellness. ARRS InPractice website. www.radfyi.org/a-lighthouse-for-radiologists. Published November 14, 2022. Accessed July 26, 2023
5. Whitman GJ. Be a Primary Radiologist. ARRS InPractice website. www.radfyi.org/primary-radiologist. Published August 5, 2022. Accessed July 26, 2023
6. Whitman GJ. Repairing the World. ARRS InPractice website. www.radfyi.org/repairing-the-world. Published November 4, 2022. Accessed July 26, 2023

Prompt and early diagnosis is vital for timely treatment of traumatic cardiac emergencies. Myocardial rupture is a rare cause of immediate death after blunt cardiac trauma, with only 0.3–1.1% of patients with trauma reaching the emergency department (ED) [1]. Pericardial tears caused by deceleration forces or rib cage fractures are uncommon after blunt chest trauma, with a frequency of 0.3–0.5%. Rarely, valvular dysfunction can be seen due to an abrupt raised intracardiac pressure against a closed valve resulting from sudden rise in intraabdominal pressure translating into the heart causing valve cusp avulsion or tear [2]. Penetrating trauma can result in pericardial injuries further complicated by life-threatening conditions including partial or complete transdefect cardiac herniation or luxation with a mortality rate as high as 67%.

Plain radiographs will show pneumopericardium, hydrothorax or hemothorax, and mediastinal hematoma. Echocardiography will show abnormal valve function; wall motion abnormalities with decreased left ventricular ejection fraction; and pericardial effusion, signs of cardiac tamponade, or both. CT will show pneumopericardium, pericardial effusion, pericardial or myocardial laceration or rupture, cardiac herniation or luxation with associated SVC obstruction or right heart strain, valvular cusp avulsion or tears, coronary artery dissection or rupture, and associated rib cage fractures, retained foreign bodies, bullet fragments, and wound tracks [3] (Fig. 1).

Fig. 1—29-year-old man with gunshot wound. Left, Axial four-chamber cardiac CT image shows bullet fragment (arrow) abutting left ventricular side wall at mid cardiac level without myocardial penetration. Right, Mid ventricle short-axis color-coded functional cardiac CT image depicts reduced perfusion (white arrow) consistent with myocardial contusion. Black arrow indicates bullet fragment.

Imaging Pearls and Pitfalls in Cardiac Trauma

Any pericardial effusion detected in the acute trauma setting is presumed to be hemopericardium until proven otherwise. CT provides valuable information about the possible nature of pericardial effusions on the basis of the attenuation measurements of the collection. Coronary artery injuries are rare (in less than 2% of chest trauma cases), with left anterior descending artery being most commonly injured [4]. Penetrating cardiac trauma can result in pericardial injuries, which can result in partial or complete transdefect cardiac herniation or luxation with mortality up to 70% [5]. Portable supine studies in ED are suboptimal with overlying artifacts, which limits evaluation. TEE is invasive and difficult to perform in patients with acute craniocervical injuries. Cardiac MRI in trauma is primarily useful as a problem-solving tool after patients are admitted, especially to delineate the extent of myocardial contusion, regional infarction, wall motion abnormality, and valvular dysfunction.

Limitations of MDCT

First, CT involves use of ionizing radiation which increase the radiation exposure in the population. Second, the quality of MDCT images suffers with fast heart rate and high calcium burden. Finally, the patients with arrhythmias, ectopy or misregistration ECG artifacts degrade the image quality and limit evaluation. Optimizing techniques should be incorporated to counter these limiting factors.

Reduction of Radiation Dose

Radiation dose can be reduced with a prospective ECG-gated technique with narrow window acquisition, ECG tube current modulation, and limited pulse windows; tube voltage reduction based on body mass index; automated tube voltage reduction based on topogram attenuation profile; adaptive collimation limiting helical over spiral scanning; and iterative reconstructive techniques to reduce noise and ultimately reduce dose.

Optimizing Quality of Cardiac CT

Several steps can be taken to optimize the quality of cardiac CT studies. A heart rate of less than 65 beats/min can be achieved by administering 5–20 mg of β-blocker (metoprolol) IV or 50–100 mg by mouth 1 hour before the CT. Lowering the heart rate widens diastole and decreases beat-to-beat variability. Coronary arterial dilatation for optimal visualization can be achieved by administering 0.4–0.8 mg of nitroglycerin sublingually 5 minutes before contrast injection. Reconstruction algorithms can be used to reduce beam-hardening artifacts from iodine that mimic ischemia (Fig. 2). 

Fig. 2—CT images show beam-hardening correction (left) and utility of B23 kernel (right) as it reduces beam-hardening artifact from iodine in left ventricle and thoracic aorta, affecting posterior inferior aspect of left ventricle wall mimicking infarct.

Edge-enhancing reconstruction algorithms can be used to reduce noise caused by extensive coronary calcifications or coronary stents.

Emerging Applications and Outlook

Coronary Atherosclerotic Plaque Characterization

The rationale behind growing efforts to accurately characterize a vulnerable, predominantly lipid-rich, plaque is its grave association with ACS and SCD. Novel attenuation-based application of dual-energy CT (DECT) has shown promising results when correlated with histologic findings. Spectral attenuation curves for material characterization are generated using attenuation values of a specific material for each and every monochromatic energy ranging from 40 to 140 keV [6]. Lipid-rich atherosclerotic plaques share the known attenuation curve of fat, in which attenuation decreases with lower monochromatic energy, thus differentiating lipid-rich plaques from fibrous plaques [7].

CT-Derived Fractional Flow Reserve

Coronary blood-flow volume effectively provides an estimation of lesion-specific ischemia. Recent vigorous advancements in digital analysis of fluid dynamics allow noninvasive assessment of coronary flow on the basis of mathematic models. CT-derived fractional flow reserve (FFR) calculates lesion-specific FFR using static coronary CT data without additional radiation or modification in image acquisition protocols. Studies have found that CT FFR shows 90% sensitivity and nearly 83% specificity for lesions with moderate stenosis causing ischemia [38]. A multicenter prospective trial showed 73% specificity and 90% sensitivity for CT FFR in diagnosing obstructive CAD compared with conventional angiographic FFR [8].

CT Myocardial Perfusion and Viability

Myocardial perfusion is one of the most important prognostic indicators for patient outcome and management of CAD. CT myocardial blood pool analysis using myocardial iodine content is a promising dynamic technology. DECT color-coded iodine maps permit sensitive detection of myocardial perfusion by depicting myocardial blood pooling [9]. The perfused myocardium takes up iodine, but no iodine uptake is seen in the infarcted myocardium (Fig. 3). Assessment of myocardial viability predicts successful revascularization therapy.

Fig. 3—Myocardial ischemia in 51-year-old man. Left and right, Mid ventricle short-axis cardiac CT image (left) and iodine perfusion map (right) show decreased subendocardial iodine uptake (arrows) in inferior basal ventricle, suggesting perfusion defect consistent with myocardial ischemia.

MDCT is a viable, reliable, and potentially effective imaging modality in evaluation of coronary and noncoronary cardiac emergencies. Cardiac CT efficiently rules out CAD in patients with low to intermediate risk who present with acute chest pain in the ED and accurately predicts midterm adverse outcome. With integration of innovative applications like morphologic plaque characterization, coronary FFR and myocardial perfusion, cardiac CT will be able to offer unprecedented benefits, ranging from triage to treatment decisions in the ED.

References

1. Mirvis SE. Imaging of acute thoracic injury: the advent of MDCT screening. Semin Ultrasound CT MR 2005; 26:305–331
2. Farhataziz N, Landay MJ. Pericardial rupture after blunt chest trauma. J Thorac Imaging 2005; 20:50–52
3. Sohn JH, Song JW, Seo JB, et al. Pericardial rupture and cardiac herniation after blunt trauma: a case diagnosed using cardiac MRI. Br J Radiol 2005; 78:447–449
4. Bruschi G, Agati S, Iorio F, Vitali E. Papillary muscle rupture and pericardial injuries after blunt chest trauma. Eur J Cardiothorac Surg 2001; 20:200–202
5. Prêtre R, Chilcott M. Blunt trauma to the heart and great vessels. N Engl J Med 1997; 336:626–632
6. Beckman JA, Ganz J, Creager MA, Ganz P, Kinlay S. Relationship of clinical presentation and calcification of culprit coronary artery stenoses. Arterioscler Thromb Vasc Biol 2001; 21:1618–1622
7. Min JK, Leipsic J, Pencina MJ, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 2012; 308:1237–1245
8. Yoon YE, Choi JH, Kim JH, et al. Noninvasive diagnosis of ischemia-causing coronary stenosis using CT angiography: diagnostic value of transluminal attenuation gradient and fractional flow reserve computed from coronary CT angiography compared to invasively measured fractional flow reserve. JACC Cardiovasc Imaging 2012; 5:1088–1096
9. Han R, Sun K, Lu B, Zhao R, Li K, Yang X. Diagnostic accuracy of coronary CT angiography combined with dual-energy myocardial perfusion imaging for detection of myocardial infarction. Exp Ther Med 2017; 14:207–213