Aim and objectives: Pulmonary function tests (PFTs) for COPD diagnosis and clinical stage classification have for a long time been based on lung spirometry indices especially FEV1, FVC, FEV1%, FEV1/FVC%, and DLCO/VA. However, the accuracy of these spirometric indices is affected by the heterogeneous nature of COPD characteristics, some of which also overlap with asthma. Chest CT and MRI studies could perhaps provide a better understanding of morphological and functional changes in patients with mild-to-severe COPD compared to the routine spirometry testing. However, emerging evidence suggests that MRI has a better diagnostic accuracy than quantitative CT (qCT)-based functional lung parameters. The aim of this systematic review is to evaluate the correlation between qCT and PFTs and between three MRI modalities (Oxygen-Enhanced-MRI, Hyperpolarised 3He- MRI and perfusion-MRI) and PFTs in COPD diagnosis and clinical staging. Methodology: MEDLINE, PubMed, ENBASE and CENTRAL electronic bibliographic databases were searched in English from January, 2008 to July, 2015. Six diagnostic cohort studies evaluating the diagnostic accuracy between qCT and MRI modalities in COPD met the inclusion criteria. The methodological consistency and quality of the included studies was appraised based on the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) quality criteria. All six studies contributed data for correlation meta-analysis of qCT and MRI functional lung parameters. Main results: Based on QUADAS-2 quality criteria, the included studies were generally of good methodological quality with moderate risk of bias and low applicability concerns. The pooled absolute correlation coefficients between MRI parameters and PFTs (r = 0.626, 95% CI 0.585 to 0.663) was significantly stronger than that between qCT and PFT (0.533, 95% CI 0.471 to 0.590). Surprisingly, OE-MRI exhibited the strongest correlation (r = 0.665, 95% CI 0.621 to 0.706), followed by HP 3He MRI and PFTs (r = 0.59) and lastly perfusion-MRI and PFT (r = 0.563, 95% CI 0.483 to 0.634). Conclusions: There is substantial evidence that chest MRI parameters are more accurate compared to the routine qCT in COPD diagnosis and clinical staging. This strongly suggests that MRI parameters for emphysema measurements could correlate strongly with exercise limitation in COPD. This further suggests that MRI measurements of emphysema and airway disease (bronchitis) could be valuable in explaining symptoms in patients with mild-to-moderate COPD exhibiting modestly abnormal FEV1. Diagnostic accuracy evidence presented in this review warrants an update of various national and international clinical guidelines for COPD diagnosis, which have for a long time recommended the conventional PFTs and qCT. I would like to express my sincere gratitude to my project supervisor, Jan Little for her excellent supervision and intellectual support along with a sense of caring and patience. She provided a remarkable intellectual atmosphere that helped me through this systematic review project. I would also like to thank the library staff for the serene learning environment they provided while working this project. I would like to thank Mohsen a good friend, who acted as my second reviewer and proof-reader and was always willing to help and give his best suggestions for this project. Lastly, I would never have been able to complete this dissertation without the support from my family. Table of Contents Abstract i Acknowledgements. ii Table of Contents. iii List of figures. vi List of tables. viii List of abbreviations. ix 1 Introduction. 10 1.1 Aetiology and epidemiology of COPD.. 10 1.2 Diagnosis of COPD.. 12 1.3 Aims of the review.. 14 2 Literature review.. 15 2.1 Pulmonary function testing. 15 2.2 Computed tomographic scan. 19 2.3 MRI in COPD diagnosis. 21 2.3.1 Oxygen-enhanced MRI in COPD diagnosis. 22 2.3.2 Hyperpolarized noble gas MRI in COPD diagnosis. 23 2.4 Quantitative CT versus MRI modalities in COPD diagnosis. 25 2.5 Aims and objectives. 26 3 Methodology. 28 3.1 Methodological approach and justifications. 28 3.2 Criteria for considering studies for the review.. 29 3.2.1 Types of studies. 29 3.2.2 Participants and target conditions. 30 3.2.3 Index and comparator tests. 30 3.2.4 Reference standards. 30 3.3 Search methods for identification of studies. 31 3.3.1 Electronic databases searched. 31 3.3.2 Electronic search strategy. 31 3.3.3 Searching other resources. 32 3.3.4 Study selection. 33 3.4 Exclusion criteria. 33 3.5 Data extraction and management 34 3.6 Appraisal of methodological quality. 34 3.7 Meta-analysis. 35 4 Results. 37 4.1 Search results. 37 4.2 Methodological quality of the included studies. 43 4.3 Risk of bias and applicability concern. 47 4.4 Diagnostic accuracy test findings. 49 5 Discussion. 52 5.1 Diagnostic test accuracy evidence. 53 5.2 Limitations of this review.. 58 6 Conclusions, implications for clinical practice and recommendations. 59 6.1 Conclusions. 59 6.2 Implications for clinical practice and guidelines for COPD diagnosis. 60 6.3 Future recommendations. 61 References. 62 Appendices. 71 Appendix 1. QUADAS-2 tool: Risk of bias and applicability judgments. 71 Appendix 2. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies (adapted from Whiting et al, 2011). 72 Appendix 3. Suggested tabular presentation for QUADAS-2 results (adapted from Whiting et al, 2011). 72 Appendix 4. Bar charts to present the percentage of results from QUADAS-2 (adapted from Whiting et al, 2011). 73 Figure 2.1. Quantitative chest CT images of different cases of emphysema patterns and bronchitis; (A) Shows signs of para-septal emphysema, (B) an emphysema with a centrilobular pattern, (C) Signs of severe bullous disease of the lungs predominant at the right lung, and (D) a diffuse ill-defined centrilobular nodules indicative of respiratory bronchitis. 20 Figure 2.2. A CT versus OE-MRI images of a 56-year old healthy subject (A-C) and a 72-year-old subject with emphysema (DF): (D) axial (E) coronal thin-section CT showing areas of low attenuation in both lungs; (F) Shows OE- MR image (left panel) and the corresponding relative enhancement map in COPD showing heterogeneous and reduced oxygen-enhancement maps in both lungs (right panel) (Coxson et al, 2009). 22 Figure 2.3. Different phenotypes of COPD as visualized on 3He MRI in 2 COPD subjects with similar PFTs; (Ai-ii) 3He MR images, (Bi-ii) ADC maps on 3He; (i) A 52-year old subject with severe COPD, FEV1= 49% predicted, FEV1/FVC = 42%, residual volume (RV) = 4.3 L, total lung capacity (TLC) = 8 L, inspiratory volume (IC) 2.9 L, mean ADC value = 0.58 cm2/s, mean VDV=102 cm3. (ii) 72-year-old male with stage-3 COPD, FEV1=49% predicted, FEV1/FVC=54%, RV=4.1 L, TLC=7.7 L, IC=3.0 L, mean ADC value=0.30 cm2/s, mean VDV=360 cm3 (Coxson et al, 2009). 24 Figure 4.1. PRISMA flow diagram showing search results and eligibility screening of the potential studies 38 Figure 4.2. Methodological quality chart of each of the 14 QUADUS??2 criteria presented as percentages across all included studies item presented as percentages across all included studies 45 Figure 4.3. Bar charts showing the percentage proportion of studies with high, low or unclear risk of bias based on four broad methodological quality criteria of QUADAS??2 48 Figure 4.4. Bar charts showing the percentage proportion of studies with high, low or unclear applicability concerns based on four broad methodological quality criteria of QUADAS??2 48 Figure 4.5. A correlation meta-analysis between all MRI modalities and PFTs 49 Figure 4.6. A correlation meta-analysis between all qCT and PFTs 50 Figure 4.7. A correlation meta-analysis between all OE-MRI and PFTs 50 Figure 4.8. A correlation meta-analysis between all perfusion MRI and PFTs 51 Table 2.1. Post-bronchodilator FEV1 classification of the degree of airflow obstruction in COPD patients with FEV1/FVC < 0.70. 16 Table 2.2. Measurements of emphysema and airways disease (bronchitis) using quantitative thin-section CT and 3He MRI (Coxson et al, 2009) 25 Table 3.1. PICRO search framework. 32 Table 4.1. Characteristics, diagnostic test endpoints & performance of the included studies. 39 Table 4.2. Methodological quality summary of each of the included studies based on 14 QUADUS??2 criteria 46 Table 4.3. QUADAS??2 risk of bias and applicability concerns for the selected diagnostic test accuracy studies 47 CT: Computed Tomography qCT: Quantitative CT MRI: Magnetic Resonance Imaging OE-MRI: Oxygen-Enhanced MRI MDCT: Multi-Detector Computed Tomography MeSH: Medical Subject Headings PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies-2 RCT: Randomized Controlled Trial COPD: Chronic Obstructive Pulmonary Disease CDC: Centres for Disease Control and Prevention FVC: Forced Vital Capacity FEV1: Forced Expiratory Volume at 1 Second ACOS: Asthma-COPD Overlap Syndrome HP-3He MRI: Hyperpolarized Helium-3 (HP 3He) MRI DLCO: Diffusing Capacity of the Lungs for Carbon Monoxide DLCO/VA: DLCO Corrected for Alveolar Volume PFTs: Pulmonary Function Tests GOLD: Global Initiative for Chronic Obstructive Lung Disease ATS: American Thoracic Society ERS: European Respiratory Society MSCT: Multislice Computed Tomography MDCT: Multi-Detector-Row Computed Tomography HU: Hounsfield Units VDV%: Percentage Ventilation Defect Volume ADC: Apparent Diffusion Coefficient PICRO: Population, Index Test, Comparator Test, Reference Standard and Outcomes PICO; Population, Intervention, Comparator and Outcome A1ATD: Alpha-1-Antitrypsin Deficiency Chronic obstructive pulmonary disease (COPD) is generalised term for a group of chronic lung diseases, particularly chronic bronchitis, emphysema or both, which are independently characterized by persistent and progressive decline in lung function, airway hyper-reactivity and limited exercise capacity (Belfer & Reardon, 2009; Rycroft et al, 2012). COPD is clinically characterized by episodes of acute airflow limitations, especially difficulties in breathing (dyspnea), chest tightness, shortness of breath, coughing, wheezing and increased sputum production. Airflow inflammations and obstructions in COPD are generally partially reversible upon treatment and do not improve markedly even after several months of treatment (Decramer et al, 2012). Therefore, COPD remains a global health problem resulting in poor quality of life and significant economic burden due to disability, work absenteeism and increased direct/indirect health-care costs (Rabe et al, 2007; Zamzam et al, 2012). Tobacco smoking is the primary aetiological risk factor for COPD worldwide. Data from epidemiological studies indicate that more than 50% of older smokers (>40 years) and ex-smokers develop COPD and more than 80-90% of COPD-related morbidity and mortality is due to a history of tobacco smoking (Andreas et al, 2009). Previous studies have shown that smoking cessation is a cost-effective intervention for slowing-down the progression of smoking-related COPD (Tashkin & Murray, 2009; Hoogendoorn et al, 2010). Frequent cases of COPD occur in low- and middle-income countries where tobacco control and industrial safety regulations are not as stringent as those in upper middle-income countries. However, according to The Tobacco Atlas (2012), high- and middle-income countries have generally higher smoking rates at 34% and 30%, respectively, than in low-income countries (21%) attributable to the low cost of smoking. With regard to industrial safety regulations, about 20-30% of COPD cases occur among industrial workers exposed to occupational dust and fumes. This was well observed in a recent study, which investigated COPD incidence rates among coal-miners exposed to occupational coal dust (Santo-Tomas, 2011). A recent population-based study has unexpectedly revealed a high COPD mortality rate among female textile workers in China who were exposed to textile dust from silk and cotton products (Cui et al, 2011; Lai & Christiani, 2013). Furthermore, women in low-income countries, especially in some African and Asian countries are also at increased risk of developing COPD later in their lives due to prolonged exposure to indoor smoke while cooking and heating using biomass fuel (World Health Organization (WHO), 2015). While COPD is a major cause of chronic morbidity and mortality globally (Rabe et al, 2007), its epidemiology with respect to incidence and prevalence estimates differ significantly between epidemiological and population-based studies. According to the most recent COPD surveillance, the age-adjusted prevalence of COPD is 6.0% (an estimated 15 million adults) in the United States. COPD prevalence rate is significantly high (>11.6%) among older adults (aged ?65 years) compared to 3.2% among young adults (aged 1844 years) (Centres for Disease Control and Prevention CDC, 2012). In the year 2014, COPD was recorded as being the third leading cause of death in the US with an age-adjusted death rate of 40.8% (males 47.6%; female 36.4%) per 100,000 U.S. general population (CDC, 2014). A study by Simpson et al, (2010) showed that in England, the prevalence of COPD was estimated at 13.3% per 1000 of the population aged over 35 years. In the general population of Saudi Arabia, the prevalence rate of COPD is about 2.4%, which is significantly lower than those reported in heavily industrialized countries (Wali et al, 2014). However, due to high prevalence rates of cigarette smoking among Saudi men (Al Ghobain, 2011) and rapid industrialization, COPD prevalence is steadily rising in Saudi Arabia. The diagnostic criteria for COPD are based on quantitative spirometric indices, particularly forced expiratory volume at 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) (Hansen et al, 2007). Thus, COPD diagnosis is defined by a FEV1/FVC ratio of <0.7 (70%). These spirometric indices have inadequate diagnostic accuracy of the degree of airway obstruction in COPD as it tend to underestimate and overestimate this in young and older adults, respectively (Hansen et al, 2007; Gruffyfid-Jones & Loveridge, 2011). Even though this spirometric criterion was slightly reviewed in the National Institute for Health and Care Excellence (NICE) COPD guidelines to specify post-bronchodilator FEV1/FVC%, this still correlates uncertainly with the histological changes of the lungs, clinical features, physiological/exercise limitations and treatment outcomes in COPD patients (Sin et al, 2011 ). The observed variability in most of the reported COPD prevalence rates can be attributed to diagnostic accuracy concerns surrounding various diagnostic procedures or tests for clinical stage classification of COPD (Rycroft et al, 2012). The accuracy of COPD diagnosis based on spirometric indices is complicated by the overlapping clinical features with asthma and possibility of COPD-asthma comorbidity (Yawn, 2009; Ambrosino & Paggiaro, 2012). A recent study has revealed that asthma-COPD overlap syndrome (ACOS) accounts for about 15-25% of chronic obstructive airway diseases (Louie et al, 2013). Patients with ACOS who are generally younger than COPD patients, exhibit worsened clinical outcomes compared with asthma or COPD alone (Louie et al, 2013). Thus, differential diagnosis of COPD is important as the treatment strategies and clinical pathways differ between COPD and asthma and ACOS (Yawn, 2009; Miravitlles et al, 2012). The accuracy of COPD diagnosis is further complicated by its heterogeneous nature of the disease presenting abnormalities in both small airway and parenchyma. Therefore, COPD may exhibit different clinical symptoms and diagnostic endpoints as well as prognostic profile following therapeutic responses to medications. Differential phenotyping of COPD patients is clinically important because clinical manifestations and the severity of bronchitis-predominant COPD may differ from those of emphysema-predominant COPD (Coxson et al, 2009). A differential diagnosis of the two major morphological phenotypes of COPD is now possible using imaging modalities, particularly computed tomography (CT) and magnetic resonance imaging (MRI). X-ray, CT and lung ventilation/perfusion (V/Q) scans of the chest are routine radiologic imaging modalities for evaluating morphological changes in COPD (Ohno et al, 2008). Quantitative CT (qCT) is the standard radiologic modality for COPD diagnosis and staging as it is not only valuable for visualisation detailed morphologic changes in the lungs but also provides quantitative parameters for assessing physiological/functional limitations in COPD (Ohno et al, 2012; Guan et al, 2014). However, there is a growing body of diagnostic accuracy evidence that suggests some chest MRI modalities correlate positively with the spirometric FEV1 measurement the carbon monoxide diffusion capacity in the lungs (DLco) and DLco corrected for alveolar volume (%DLCO/VA ) (Hur et al, 2007; Kirby et al, 2014). Two recent diagnostic accuracy studies have demonstrated that the dynamic oxygen-enhanced MRI (OE-MRI) (Ohno et al, 2012) and hyperpolarized helium-3 (HP 3He) MRI (van Beek et al, 2009) are diagnostically more accurate than the quantitative chest CT in the assessment of lung functional loss in smoking-related COPD. Thus, chest MRI is increasingly becoming the radiologic modality of choice for diagnosing and staging smoking-related COPD. Although two recent diagnostic test accuracy studies have demonstrated that MRI is as accurate as qCT in clinical stage classification of COPD (van Beek et al, 2009; Ohno et al, 2012), the evidence-base supporting the diagnostic superiority of MRI over qCT in COPD diagnosis is still weak. The observed mixed evidences can be attributed to varied diagnostic setting and methodological consistency of the studies with respect to appropriateness of patient selection (spectrum of COPD patient), selection of reference (or gold) standard tests, and inadequate, validation of diagnostic tests (Bossuyt et al, 2003; BMJ Clinical Evidence, 2014). Therefore, further appraisal of the level and quality of evidence of diagnostic test accuracy between chest MRI and quantitative chest CT in the evaluation smoking-related COPD is warranted. The primary aim of this review is to evaluate the diagnostic accuracy between the routine quantitative chest CT and chest MRI in the diagnosis of adult patients with COPD. first, however, a preliminary literature overview of the current clinical guidelines for COPD diagnosis and inadequacies is presented. Importantly, the diagnostic value of lung imaging modalities (qCT and MRI) in the context of COPD is discussed with special emphasis on their strengths as well as limitations in the diagnosis and clinical stage classification of COPD. A clinical diagnosis of COPD takes a systematic approach involving careful recording of patient history, assessment of airflow limitation and chest imaging to determine anatomic/morphological changes. This diagnostic process is often complicated by the overlapping clinical features of asthma and COPD phenotypes (Yawn, 2009; Ambrosino & Paggiaro, 2012; Louie et al, 2013). As pulmonary lung function testing and chest imaging (CT and MRI) are key to COPD diagnosis, these diagnostic techniques have strengths as well as limitations worth reviewing. The routine diagnosis of COPD is based primarily on pulmonary function tests (PFTs), which include several breathing tests that assess how well the lungs function (Behr & Furst, 2008). PFTs are generally indicated for patients presenting with coughing, wheezing, breathlessness or abnormal chest x-ray as well as patients with known pulmonary diseases, in order to evaluate the severity, progression and response to treatment (Ranu et al, 2007). The goals of preforming PFTs are to assess lung volume, gaseous exchange capacity, bronchial obstruction and ventilation capacity including the diffusion capacity of molecular oxygen from the atmosphere through the lungs into the entire body??s circulation (Ranu et al, 2007; Behr & Furst, 2008). The presence of partially reversible airflow obstruction is the hallmark of COPD diagnosis and is most commonly assessed in the primary care setting based on lung spirometry tests (Joo et al, 2011). Spirometry scores assess airflow and the degree of airway obstruction by measuring both static and dynamic lung volumes. Static lung volumes are measured as slow (unforced) inspiratory or expiratory vital capacity and forced vital capacity (FVC) while dynamic lung volumes are measured as forced expiratory volume (FEV) and/or flow-volume loops (Ranu et al, 2007; Behr & Furst, 2008). However, given that there are no standardised criteria to define an unforced inspiratory or expiratory manoeuvre, forced spirometry is frequently used for assessing lung function (Garc??a-R??o et al, 2013). Forced spirometry tests require that patients inhale a maximal volume of air under a forced inspiratory manoeuvre, then forcefully exhale the air for as long and rapidly as possible (Miller et al, 2005; Ranu et al, 2007). FEV is a measure of the excess air volume a person can exhale during a forced breath, which can be determined at the first (FEV1), second (FEV2) or third (FEV3) sec. of the forced breath. FVC, on the other hand, is a measure of the total volume of air exhaled forcefully and quickly after a maximal inspiratory manoeuvre during the FEV tests (Ranu et al, 2007; Garc??a-R??o et al, 2013). FEV1 and the calculated ratio of FEV1 to FVC (FEV1/FVC) comprise the quantitative forced spirometry indices useful for the diagnosis of chronic obstructive lung diseases, particularly asthma and COPD (Hansen et al, 2007). Patients with asthma or COPD have a lower FEV1 and higher FVC scores than healthy subjects. A FEV1/FVC ratio of <0.7 (<70%) is a strong spirometric indication of COPD diagnosis and, therefore, a further decrease in FEV1 or FEV1/FVC score is also a reliable indicator of a worsening airflow obstruction (Behr & Furst, 2008). Three international bodies, the Global Initiative for Chronic Obstructive Lung Disease (GOLD), the American Thoracic Society (ATS) and the European Respiratory Society (ERS) have worked collaboratively to promote the global use of FEV1 as a key spirometric measure for diagnosing and staging of COPD including asthma (Rabe et al, 2007; Cazzola et al, 2008). However, while forced spirometry appears simple and quick due to its straightforward instrumentation (spirometer), its diagnostic value in COPD evaluation can be significantly compromised by incorrect breathing manoeuvres or poor patient compliance with the spirometry procedures (Miller et al, 2005; Behr & Furst, 2008). The diagnostic accuracy of forced spirometry tests for COPD evaluation can be also influenced by clinician experience or training in breathing manoeuvres (Behr & Furst, 2008). Furthermore, there are diagnostic concerns that FEV1 and FEV1/FVC spirometric indices have inadequacies in COPD evaluation. In general, the two spirometric indices are inadequate for COPD monitoring since they correlate poorly with symptom severity or health-related quality of life. This has complicated the efforts by clinical researchers who are trying to develop criteria for COPD diagnosis and clinical staging (Cazzola et al, 2008). The definition of airway obstruction in COPD diagnosis based on the primary criterion of FEV1/FVC <70% as recommended in the GOLD guidelines is loosely accurate (Cazzola et al, 2008; Sin et al, 2011). Two studies have highlighted that this spirometric criterion lacks statistical justifications, when defining airway obstruction in COPD diagnosis. In this case, the incidence of airway obstruction in young and older adults are underestimated and overestimated, respectively (Hansen et al, 2007; Gruffyfid-Jones & Loveridge, 2011). In a previous study by Hansen et al, (2007), 5,906 non-smoking adults with no respiratory or musculoskeletal disease and 3,497 active-smoking adults were enrolled from the Third National Health and Nutrition Examination Survey (NHANES-III) database. The age range of all the participants enrolled was 20.0 -79.9 years and all had undergone repeated spirometric measures that met the American Thoracic Society (ATS) spirometry standards. All participants (N=9,403) regardless of smoking status were classified as being above or below the NHANES-III 5th percentile of normal; those above and below the 5th percentile were considered as having normal and abnormal lung spirometry, respectively. The participants were then classified into three ethnic groups (white, black and Hispanic) where the non- and active-smoking groups with spirometric FEV1/FVC% <70% were identified and compared after adjusting for decade of life, sex, and ethnicity. It was revealed that about 50% of young adults initially identified as having abnormal lung spirometry were misdiagnosed as normal since their FEV1/FVC% was > 70%. Correspondingly, approximately 20% of older adults initially identified as having normal lung spirometry were found to have FEV1/FVC% of < 70%, which indicates abnormal lung spirometry. By this account, the GOLD??s FEV1/FVC <70% spirometric criterion was not completely accurate warranting an update as shown in Table 2.1 below. Table 2.1. Post-bronchodilator FEV1 classification of the degree of airflow obstruction in COPD patients with FEV1/FVC < 0.70 Gold criterion COPD severity FEV1 of predicted values GOLD 1 Mild FEV1? 80% predicted GOLD 2 Moderate 50% ?? FEV1< 80% predicted GOLD 3 Severe 30% ?? FEV1< 50% predicted GOLD 4 Very Severe FEV1< 30% predicted However, even after this spirometric criterion was slightly modified in the 2010 COPD guidelines of the NICE specifying post-bronchodilator of FEV1 < 80% of the predicted value together with a FEV1/ FVC < 70% to confirm COPD the diagnosis (Gruffyfld-Jones & Loveridge, 2011; Joo et al, 2011), it has only 70% accuracy to diagnose COPD and cannot certainly distinguish between COPD and asthma (Richter et al, 2008; Schneider et al, 2009). Furthermore, the updated spirometric criterion still correlates uncertainly with lung histological changes, clinical symptoms and outcomes in COPD (Sin et al, 2011). In addition, FEV1 is poorly responsive to clinical interventions and cannot offer a differential diagnosis of the major morphological phenotypes of COPD, particularly emphysema- and bronchitis-predominant COPD (Cazzola et al, 2008; Coxson et al, 2009). This is because both emphysema- and bronchitis-predominant COPD can independently deteriorate lung function and may exhibit different clinical symptom, prognostic profile and unpredictable therapeutic responses to medications (Coxson et al, 2009). A differential diagnosis of the two morphological phenotypes of COPD is now possible in the advent of lung imaging modalities particularly CT and MRI. Chest CT has remained the routine imagining modality for evaluating COPD patients owing to its technical capabilities in visualising overlapping lung structures with a relatively high spatial resolution and high signal-to-noise ratio (Coxson et al, 2009; Choroma?ska & Macura, 2012). Furthermore, chest CT has undergone significant technical advancements over the years from thick-slice to thin-slice axial scans and from the contiguous spiral/helical to multislice CT (MSCT) or multi-detector-row CT (MDCT) scanners. These advances in CT have made it possible to quantify lung volumes by summing up the isotropic lung CT voxel dimensions (Katada, 2002; Fain et al, 2010). An isotropic CT voxel is the minimum unit of a CT image obtained by a MSCT scanner in thin slices of ??1.0 mm (usually 0.5 mm) thickness, giving an image resolution equally distributed in three vector (X, Y, and Z axis) directions (Katada, 2002). The thin-slice MSCT also permits transformation of axial lung CT images into transverse, coronal or sagittal planes and therefore, allows accurate estimation of the lung volumes of individual lobes (Coxson et al, 2009). In addition, the X-ray attenuation values of the lung CT estimated in Hounsfield units (HU) provide quantitative data on lung density, which when combined with data on the lung volumes of individual lobes can aid in the estimation of lung mass, tissue volume and airspace volume (Coxson et al, 2009; Choroma?ska & Macura, 2012). By this account, CT scan is useful for differential diagnosis of the morphological phenotypes of COPD (bronchitis and emphysema) owing to its capability of elucidating the relative contributions of individual phenotypes to the deterioration of lung function. CT can also aid in the discrimination of morphological sub-phenotypes of emphysema patterns and bronchitis (Fig. 2.1) Figure 2.1. Quantitative chest CT images of different cases of emphysema patterns and bronchitis (A) signs of para-septal emphysema (B) an emphysema with a centrilobular pattern (C) signs of severe bullous disease of the lungs predominant at the right lung and (D) a diffuse ill-defined centrilobular nodules indicative of respiratory bronchitis. In addition, the threshold analysis of the X-ray attenuation can discriminate emphysematous from normal lung tissue based on predetermined cut-off HU values (Coxson et al, 2009; Choroma?ska & Macura, 2012). Furthermore, quantitative chest CT can help investigators and clinicians to map the spatial predominance of emphysema in emphysematous COPD to guide therapy (Coxson et al, 2009). However, while quantitative chest CT scan has for many years remained a routine imaging technique for quantifying the degree of emphysema in COPD, its accuracy in emphysema estimation is highly dependent on lung volume at the time of analysis, which has a relatively low reproducibility. It is therefore recommended that the extent of breath-hold during the CT scan be factored in during emphysema estimation (Coxson et al, 2009; Choroma?ska & Macura, 2012). Magnetic resonance imaging (MRI) is increasingly becoming a promising diagnostic and research tool for COPD evaluation due to its superior tissue-contrast enhancements with high spatial-temporal resolution (Coxson et al, 2009). Furthermore, MRI multimodality provides options for better visualization of different anatomical regions of the lung (Coxson et al, 2009; Choroma?ska & Macura, 2012). In the conventional proton (1H) MRI (1H-MRI) perfusion tissue contrast depends on both tissue proton density and magnetic environment within the target tissue. The magnetic environment of proton density in the target tissue is defined by both longitudinal (T1) and transverse (T2) relaxation times, which form the technical hallmark for tissue contrast in most MR images. However, chest MRI has been hindered by a short T1 relaxation time in lung tissue. In lung tissue where there is low water and fat protons density, MRI often results in low 1H signal (Coxson et al, 2009; Fain et al, 2010; Choroma?ska & Macura, 2012). In such anatomical regions, the low proton signal is further attenuated by the distortional effects of the magnetic field created by air-tissue interfaces in air pockets of the lung tissue (Fain et al, 2010). Furthermore, during image acquisition, respiratory and cardiac motions are observed to degrade pulmonary perfusion parameters and, therefore, degrade the quality of pulmonary MR image (Sergiacomi et al, 2014). Thus, the low 1H density in the lungs, coupled with susceptibility artefacts has for a long-time degraded the diagnostic value of perfusion MRI in COPD diagnosis leaving MDCT the imagi
Use the order calculator below and get started! Contact our live support team for any assistance or inquiry.
[order_calculator]