Table 1 Summary of all included studies

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