Chest radiography and computed tomography imaging in cystic fibrosis: current challenges and new perspectives

Abstract

Imaging plays a pivotal role in the noninvasive assessment of cystic fibrosis (CF)-related lung damage, which remains the main cause of morbidity and mortality in children with CF. The development of new imaging techniques has significantly changed clinical practice, and advances in therapies have posed diagnostic and monitoring challenges. The authors summarise these challenges and offer new perspectives in the use of imaging for children with CF for both clinicians and radiologists. This article focuses on chest radiography and CT, which are the two main radiologic techniques used in most cystic fibrosis centres. Advantages and disadvantages of radiography and CT for imaging in CF are described, with attention to new developments in these techniques, such as the use of artificial intelligence (AI) image analysis strategies to improve the sensitivity of radiography and CT and the introduction of the photon-counting detector CT scanner to increase spatial resolution at no dose expense.

Keywords: Chest; Children; Computed tomography; Cystic fibrosis; Lungs; Photon-counting computed tomography; Radiography.

Fig. 1

Chest radiography in children with cystic fibrosis and acute pulmonary exacerbation. a, b Anteroposterior (AP) chest radiograph in a 3-year-old boy (a) and posteroanterior (PA) chest radiograph in a 10-year-old boy (b). Note absence of clear abnormalities in (a). Conversely, (b) shows small peribronchial opacity in the right upper lung (thin arrow) and large consolidation in left upper lung (thick arrow)

Fig. 2

Acute pulmonary exacerbation in a 3-year-old boy. a, b Upper lobe (a) and lower lobe (b) sections of axial free-breathing CT without sedation show clear bronchial wall thickening (thin arrows) and focal parenchymal abnormalities in the lower lobes (thick arrows). The consolidation in the left lower lobe also shows a ground-glass halo (arrowhead), making it suspicious for acute respiratory infection

Fig. 3

End-inspiratory and end-expiratory spirometry-guided non-enhanced CT in a 13-year-old girl with cystic fibrosis at her biennial follow-up. a, b End-inspiratory (a) and end-expiratory (b) axial CT images show clear bronchial wall thickening and bronchiectasis in the upper segment of the right lower lobe (thin arrows) and low-attenuation regions on the same area and also in the ventral part of the right upper lobe (thick arrows), indicating chronic small airways disease with trapped air and perfusion impairment

Fig. 4

Current diagnostic algorithm for cystic fibrosis (CF) imaging. CR chest radiography, CT computed tomography, FB free breathing, FU follow-up, MRI magnetic resonance imaging, PEx pulmonary exacerbation, y.o. years old. The term “CT” in the flow chart is a general term, where dose should be set according to current dose reference level for paediatric chest CT imaging [24]. ^§ Non-cooperative children are generally those who are younger than 5 years or mentally impaired, * depending on cystic fibrosis centre expertise, ⊖ CT can be performed in cases of negative chest radiography findings and persistent symptoms despite therapy, † at our centre we use MRI for short-term follow-up of pulmonary exacerbation and as follow-up technique in stable CF subjects in alternation with CT (1 year CT and the year after MRI)

Fig. 5

Acute pulmonary exacerbation in a 10-year-old boy. a, b Posteroanterior (PA) chest radiograph (a) and coronal end-inspiratory CT (b) show large cavitating consolidation in the left upper lobe (thick arrow in both). More subtle changes such as peribronchial opacities and mucus plugs in the right upper lobe are less visible on chest radiograph (thin arrow in a) compared to CT (thin arrow in b)

Fig. 6

Illustration of photon-counting detector CT (PCD-CT) and conventional energy integrating detector CT (EID-CT) scanners. a, b The current EID (b) and PCD (a) CT scanners with improved detector efficiency and more detailed information thanks to direct conversion of the radiation signal, smaller detector elements and spectral information (intensity and energy) obtained by counting the number of incoming photons per energy bin. CdTe cadmium telluride, GOS gadolinium oxysulfide. Images adapted from [39]

Fig. 7

Photon-counting detector CT (PCD-CT) and conventional energy integrating detector CT (EID-CT) scanning in a 20-year-old man with cystic fibrosis. a–d Coronal detail of end-inspiratory CT using conventional EID-CT (a) and PCD-CT (b–d). Images (a) and (b) have slice thickness of 1 mm, and images (c) and (d), 0.6 mm and 0.2 mm, respectively. All four images have similar kernels including iterative reconstruction. Comparison of images (a) and (b) shows a clear reduction of image noise (reduced granularity in the axillary region, thick arrow) and sharper definition of bronchial wall and cystic bronchiectasis in the PCD-CT (thin arrow). In images (c) and (d), the increased resolution allows sharper details of the most peripheral structures (thin arrow)

Fig. 8

Advanced image analysis of CT in a 12-year-old boy with cystic fibrosis (CF). a, b PRAGMA (Perth-Rotterdam Annotated Grid Morphometric Analysis) CF score (a) and airway–artery (AA) ratio (b). The PRAGMA-CF score is a scoring system that uses a grid to quantify all typical CF lung disease components. In the example, green colour is used for normal lung tissue, red for bronchiectasis and yellow for bronchial wall thickening. The percentage of disease (%Dis) is the total score of these components expressed as a percentage (%) of the total lung volume. In the AA ratio, the entire bronchial tree is automatically segmented (b) along with the adjacent pulmonary arteries. Each AA pair is then segmented (perpendicular view of airway and artery measurements) to compute several parameters, such as percentage of bronchiectasis, bronchial wall thickening and lack of tapering. Both PRAGMA-CF and the AA method have been automated using artificial intelligence techniques and are validated