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. 2023 Jun 19;14:107. doi: 10.1186/s13244-023-01451-4

Table 1.

Summary of all included studies

Author Study design Focus Subjects N Study objectives JBI quality score*
Tanaka et al. [8] Case series Pixel value analysis Healthy volunteer(s), COPD 18 Assess correlation between diaphragm motion parameters and lung vital capacity. Describe methods for visualising change in pixel value and compare to clinical/radiological data 5
Tanaka et al. [4] Observational Technical report NA 37 Compare four different automatic processes with accuracy of manual selection by radiologist to determine max inspiration/expiration 2
Tanaka et al. [9] Observational Ventilation Healthy volunteer(s) 6 Assess average pixel value change during respiratory cycle and regional differences in pixel value change in standing and decubitus position 6
Tanaka et al. [10] Observational Ventilation, perfusion Healthy volunteer(s) 7 Assess feasibility of using DCR to map blood distribution for future clinical use 3
Kawashima et al. [11] Observational Reproducibility Healthy volunteer(s) 5 Assess reproducibility of changes in pixel value between repeated DCR 2
Tanaka et al. [12] Observational Perfusion Various 14 To compare quantitative pulmonary blood flow using DCR and perfusion scanning 5
Tanaka et al. [5] Case control Perfusion Various 20 To assess the validity of DCR for evaluating pulmonary blood flow distribution, with normal controls 3
Tsuchiya et al. [13] Observational Nodule motion analysis Healthy volunteer(s) 8 To detect lung nodules (simulated) 4
Tanaka et al. [14] Case control Ventilation Various 20 To assess the ability of DCR to detect ventilatory impairment using pixel value change, compared with scintigraphy 5
Tanaka et al. [15] Case control Rib motion Various 16 To assess the ability of DCR to detect rib motion in normal controls and individuals with scoliosis 3
Yamada et al. [16] Observational Diaphragm motion Healthy volunteer(s) 172 To evaluate the average diaphragmatic excursions in healthy volunteers, and assess the relationships between DCR metrics and anthropometrics/spirometry 5
Tanaka et al. [17] Observational Ventilation Various 30 To assess ventilatory defects using change in lung texture 4
Yamada et al. [18] Case control Diaphragm motion Healthy volunteer(s), COPD 86 Evaluate the difference in tidal breathing diaphragm motion between COPD and healthy controls using DCR 5
Yamada et al. [19] Case control Craniocaudal gradient analysis Healthy volunteer(s), COPD 90 Evaluate the difference in craniocaudal gradient of maximum pixel value change rate between COPD and healthy controls 5
Hida et al. [20] Observational Diaphragm motion Healthy volunteer(s) 174 To assess diaphragm motion in standing positions during forced breathing, and evaluate its associations with demographics and pulmonary function tests 7
Hida et al. [21] Case control Diaphragm motion COPD, healthy 62 To assess differences in diaphragmatic motion (speed and excursion) between COPD and control. To assess correlation between pulmonary function tests and diaphragmatic motion 7
Kitahara et al. [22] Observational Segmentation Various 214 To develop a lung segmentation for dynamic chest radiography, and to assess the clinical utility of this measure for pulmonary function assessment 3
Hanaoka et al. [23] Diagnostic cohort study Pulmonary function Lung cancer resection 52 To assess the use of DCR to calculate post-operative pulmonary function compared to pulmonary perfusion scintigraphy 7
Hino et al. [24] Observational Lung areas Healthy volunteer(s) 162 To investigate correlation of projected lung areas with pulmonary function 7
Ohkura et al. [25] Observational Ventilation COPD 118 Assess relationship between lung area (max and min) and rate of change with pulmonary function tests 5
Tanaka et al. [26] Case control Ventilation, perfusion Various 53 To assess the ability of DCR to detect ventilatory impairment using pixel value change, compared with ventilation/perfusion imaging 6
Watase et al. [27] Case control Tracheal diameter analysis COPD 40 To assess the ability of DCR to detect intrathoracic tracheal narrowing between normal and abnormal cases 4
Yamamoto et al. [28] Observational Perfusion Various 42 Assess the success rate of deep-breath-holding and breath-holding DCR in assessment of pulmonary perfusion; correlation between diaphragm motion and anthropometrics 6
FitzMaurice et al. [29] Observational Diaphragm motion, lung areas Cystic fibrosis bronchiectasis 24 To describe changes in diaphragm motion and lung areas before and after modulator therapy in adults with cystic fibrosis bronchiectasis using DCR 7
FitzMaurice et al. [30] Case series Diaphragm motion Diaphragm palsy 21 To describe diaphragm motion in individuals with a paralysed hemidiaphragm using DCR 6
Ohkura et al. [31] Case control Diaphragm motion, lung areas, tracheal diameter COPD, restrictive lung disease 273 Identify relationship between lung disease (restrictive and obstructive) and parameters on DCR 4
Tanaka et al. [32] Observational Ventilation, perfusion Lung cancer 42 To assess the ability of DCR to detect ventilatory impairment using pixel value change, compared with ventilation/perfusion imaging 5
Ueyama et al. [33] Case control Lung volume measurement Interstitial lung disease 97 To evaluate the ability of DCR to predict forced vital capacity 7
FitzMaurice et al. [34] Observational Diaphragm motion, lung areas Cystic fibrosis bronchiectasis 20 To describe diaphragm motion in individuals undergoing treatment for a pulmonary exacerbation of cystic fibrosis bronchiectasis 7

DCR dynamic chest radiography, COPD chronic obstructive pulmonary disease

*Point-by-point score is listed in the Additional file 1