Background: When an ischemic stroke happens, it triggers a complex signalling cascade that may eventually lead to neuronal cell death if no reperfusion. Recently, the relayed nuclear Overhauser enhancement effect at-1.6 ppm [NOE(-1.6 ppm)] has been postulated may allow for a more in-depth analysis of the ischemic injury. This study assessed the potential utility of NOE(-1.6 ppm) in an ischemic stroke model.Methods: Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion. Results: The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information.Conclusions: Using CEST coupled with ADC and CBF, the ischemic tissue may thus potentially be separated into four zones to better understand the pathophysiology after stroke and improve ischemic tissue fate definition. Further verification of the potential utility of NOE(-1.6 ppm) may therefore lead to a more precise diagnosis.

Rationale Acquiring high-quality spirometry data in clinical trials is important, particularly when using forced expiratory volume in 1 s or forced vital capacity as primary end-points. In addition to quantitative criteria, the American Thoracic Society (ATS)/European Respiratory Society (ERS) standards include subjective evaluation which introduces inter-rater variability and potential mistakes. We explored the value of artificial intelligence (AI)-based software (ArtiQ.QC) to assess spirometry quality and compared it to traditional over-reading control. Methods A random sample of 2000 sessions (8258 curves) was selected from Chiesi COPD and asthma trials (n=1000 per disease). Acceptability using the 2005 ATS/ERS standards was determined by over-reader review and by ArtiQ.QC. Additionally, three respiratory physicians jointly reviewed a subset of curves (n=150). Results The majority of curves (n=7267, 88%) were of good quality. The AI agreed with over-readers in 91% of cases, with 97% sensitivity and 93% positive predictive value. Performance was significantly better in the asthma group. In the revised subset, n=50 curves were repeated to assess intra-rater reliability (κ=0.83, 0.86 and 0.80 for each of the three reviewers). All reviewers agreed on 63% of 100 unique tests (κ=0.5). When reviewers set the consensus (gold standard), individual agreement with it was 88%, 94% and 70%. The agreement between AI and “gold-standard” was 73%; over-reader agreement was 46%. Conclusion AI-based software can be used to measure spirometry data quality with comparable accuracy as experts. The assessment is a subjective exercise, with intra- and inter-rater variability even when the criteria are defined very precisely and objectively. By providing consistent results and immediate feedback to the sites, AI may benefit clinical trial conduct and variability reduction.

Supplementary Material, including Supplementary Methods describing the Random Effects Model. Supplementary Results showing example quantitative T1/T2 maps from the rat tumour model (Supplementary Figure S1), the standard curve for making quantitative protein concentration measurements using Coomassie staining (Supplementary Figure S2), the result of a conventional MTRasym APT analysis approach in the rat tumour model (Supplementary Figure S3), and the histological validation that areas of high protein concentration correlate with areas of increased hypoxia, increased vessel area and higher cellularity in the rat tumour model (Supplementary Figure S4). Supplementary Table S1 shows the mean {plus minus} standard deviation of T1 and T2 relaxation times measured in different tissue ROIs from both the rat tumour and mouse tumour models.

PurposeIn chemical exchange saturation transfer imaging, saturation effects between 2 to 5 ppm (nuclear Overhauser effects, NOEs) have been shown to exhibit contrast in preclinical stroke models. Our previous work on NOEs in human stroke used an analysis model that combined NOEs and semisolid MT; however their combination might feasibly have reduced sensitivity to changes in NOEs. The aim of this study was to explore the information a 4‐pool Bloch–McConnell model provides about the NOE contribution in ischemic stroke, contrasting that with an intentionally approximate 3‐pool model.MethodsMRI data from 12 patients presenting with ischemic stroke were retrospectively analyzed, as well as from six animals induced with an ischemic lesion. Two Bloch–McConnell models (4 pools, and a 3‐pool approximation) were compared for their ability to distinguish pathological tissue in acute stroke. The association of NOEs with pH was also explored, using pH phantoms that mimic the intracellular environment of naïve mouse brain.ResultsThe 4‐pool measure of NOEs exhibited a different association with tissue outcome compared to 3‐pool approximation in the ischemic core and in tissue that underwent delayed infarction. In the ischemic core, the 4‐pool measure was elevated in patient white matter () and in animals (). In the naïve brain pH phantoms, significant positive correlation between the NOE and pH was observed.ConclusionAssociations of NOEs with tissue pathology were found using the 4‐pool metric that were not observed using the 3‐pool approximation. The 4‐pool model more adequately captured in vivo changes in NOEs and revealed trends depending on tissue pathology in stroke.

Purpose To assess the correlation and differences between common amide proton transfer (APT) quantification methods in the diagnosis of ischemic stroke. Methods Five APT quantification methods, including asymmetry analysis and its variants as well as two Lorentzian model-based methods, were applied to data acquired from six rats that underwent middle cerebral artery occlusion scanned at 9.4T. Diffusion and perfusion-weighted images, and water relaxation time maps were also acquired to study the relationship of these conventional imaging modalities with the different APT quantification methods. Results The APT ischemic area estimates had varying sizes (Jaccard index: 0.544 <= J <= 0.971) and had varying correlations in their distributions (Pearson correlation coefficient: 0.104 <= r <= 0.995), revealing discrepancies in the quantified ischemic areas. The Lorentzian methods produced the highest contrast-to-noise ratios (CNRs; 1.427 <= CNR <= 2.002), but generated APT ischemic areas that were comparable in size to the cerebral blood flow (CBF) deficit areas; asymmetry analysis and its variants produced APT ischemic areas that were smaller than the CBF deficit areas but larger than the apparent diffusion coefficient deficit areas, though having lower CNRs (0.561 <= CNR <= 1.083). Conclusion There is a need to further investigate the accuracy and correlation of each quantification method with the pathophysiology using a larger scale multi-imaging modality and multi-time-point clinical study. Future studies should include the magnetization transfer ratio asymmetry results alongside the findings of the study to facilitate the comparison of results between different centers and also the published literature.

Introduction: The ATS/ERS 2005 guidelines for standardization of spirometry aimed to guide consistent assessment of all spirograms, to reduce inter-rater variability, and secure reliable measurements. The ATS/ERS guidelines were updated in 2019, which has the potential to impact spirometry acceptability in clinical trials. Objective: This study aims to explore the impact of 2019 guidelines on spirometry curve quality control by using the latest guidelines. Methods: A random sample of 400 spirometry sessions (1659 curves) was selected from legacy Chiesi COPD and Asthma clinical trials database (200 per disease). AI software (ArtiQ.QC) determined the acceptability of each curve using a deep-learning model (Das et al. ERJ 2020) using 2005 and 2019 guidelines. Since acceptability (acceptable/usable/unacceptable) for FEV1 and FVC are assessed separately in 2019 guidelines, but are combined in 2005, the comparison was made separately. Results: When considering FEV1 acceptability, 98% of curves were equivalent using either guideline. The remaining curves were deemed acceptable by 2005 guidelines, but not by 2019 guidelines, because the back-extrapolated volume (BEV) exceeded the new threshold (0.1L). When considering FVC acceptability, there was agreement in 91% of curves. 2% were rejected by 2019 guidelines because of the more stringent BEV criteria. With less strict guidance on artefacts for FVC in 2019 guidelines, 7% of curves were accepted by 2019 guidelines, but not by 2005. Conclusion: From this retrospective comparison, the impact on data quality by applying the 2019 guidelines should be acceptable. Improvements in data quality may be achieved by appropriate operator training according to 2019 standards.

Introduction: Accurate differential diagnosis of respiratory diseases requires interpreting the complete set of pulmonary function test (PFT) measurements including spirometry, body-plethysmography, and diffusion capacity. However, plethysmography may not be available or routinely performed in many clinical settings. Here, we explore the diagnostic potential of AI in interpreting PFTs with missing plethysmography. Methods: We compared the diagnostic performance of 2 machine learning models; one leveraging complete PFT data (Topalovic et al. 2019 ERJ), and one using only spirometry and diffusion capacity as input. Both models were developed on the same training dataset (N=1400). A previously reported sample of 50 subjects with complete PFT and a gold standard diagnosis was used for the comparison. Results: Overall diagnostic accuracy did not differ significantly (p=0.25, N=50) between the models (82% with plethysmography vs. 74% without). Sensitivity to COPD (100%, N=11) and asthma (75%, N=8) remained unchanged, as well as to interstitial lung disease (90%, N=10) and neuromuscular diseases (67%, N=3) that require lung volumes for interpretation. Sensitivity to thoracic deformity reduced (60% to 20%, N=5). Finally, we observed that spirometry and diffusion capacity measurements explained a significant (p<0.01) variation in plethysmographic parameters: RV (R2=0.45), TLC (R2=0.82), FRC (R2=0.72) and Raw (R2=0.49). Conclusion: AI may detect respiratory diseases even when PFTs do not include plethysmography data, as they were significantly correlated with spirometry and diffusion capacity. These results should be replicated with a large sample study in the future.

Introduction: Acquiring high quality spirometry data in clinical trials is important, particularly when using FEV1 or FVC as primary endpoints. In addition to quantitative criteria, the ATS/ERS quality control standards include subjective evaluation which introduces inter-rater variability. Within clinical trials, over-readers usually review spirometry curves to ensure data quality. Objectives: This study explores the value of artificial intelligence-based quality control software (ArtiQ.QC) to determine spirometry quality in clinical trials. Methods: A total of 2000 spirometry sessions (8258 curves) were randomly selected from Chiesi COPD and Asthma clinical trials (1000 sessions per disease). Acceptability using the 2005 ATS/ERS guidelines was determined by over-reader review and compared with acceptability defined by ArtiQ.QC (Das et al. ERJ 2020). In addition, two respiratory physicians jointly reviewed a subset of curves. Results: ArtiQ.QC agreed with over-readers in 87% of cases, with 93% sensitivity and 93% positive predictive value (PPV). In a subset of data, when ArtiQ.QC and over-readers agreed, the independent physician review agreed in 47/50 (94%) curves. When ArtiQ.QC and over-reader labels disagreed, the independent physicians agreed with ArtiQ.QC in 103/156 (66%) curves. Inter-rater variability in quality control assessment likely impacts the sensitivity and specificity of the software. Conclusion: ArtiQ.QC software results are comparable to the over-reader’s and could assist in the quality assessment of spirometry in clinical trials. By providing immediate and consistent results, using ArtiQ.QC may benefit clinical trial conduct and reduce the variability in outcomes.

Background: A deep-learning based algorithm to standardize quality control review has recently been developed (Das et al., ERJ 2020). Here we externally validate this software. Methods: Spirometry data collected in preschool children (Hospital for Sick Children, Canada) and healthy adults (Healthy Lungs for Life event; Madrid, Spain) were evaluated by ArtiQ.QC software using the 2005 ATS/ERS standards. Manoeuvre acceptability (acceptable/usable/unacceptable) by ArtiQ.QC was compared to quality control decisions made by trained operators at the time of test. Results: A total of 246 curves from 30 test occasions in children (healthy and cystic fibrosis) between the ages of 2.5 and 6 years of age were analyzed. Overall quality control decisions agreed for 88% (214/246) of the curves; ArtiQ.QC accepted 72% of curves compared with 67% by the operator. In a subset that was re-evaluated, there was 95% agreement with ArtiQ.QC. A second dataset with 1231 curves from 247 test occasions in healthy adults between the ages of 18 and 60 years was also analyzed. The agreement between ArtiQ.QC and the technicians was 81%; ArtiQC accepted 58% of curves compared with 60% by the operators. Disagreement between ArtiQ.QC and technician mostly occurred when ArtiQ.QC detected a cough, sub-maximal effort or hesitation in the curve effort. Conclusions: ArtiQ.QC had good overall agreement with technician review for both paediatric and adult data. Using ArtiQ.QC to review spirometry curves in real-time or retrospectively can save time and evaluate data quality.

PurposeChemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low‐concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements.MethodsCEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a two‐pool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates.ResultsWhen all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis.ConclusionsThese results indicate that current practices of simultaneously fitting both CEST and MT effects in model‐based analyses can lead to significant bias in all parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases.

Cerebral blood flow is an important parameter in many diseases and functional studies that can be accurately measured in humans using arterial spin labelling (ASL) MRI. However, although rat models are frequently used for preclinical studies of both human disease and brain function, rat CBF measurements show poor consistency between studies. This lack of reproducibility is due, partly, to the smaller size and differing head geometry of rats compared to humans, as well as the differing analysis methodologies employed and higher field strengths used for preclinical MRI. To address these issues, we have implemented, optimised and validated a multiphase pseudo-continuous ASL technique, which overcomes many of the limitations of rat CBF measurement. Three rat strains (Wistar, Sprague Dawley and Berlin Druckrey IX) were used, and CBF values validated against gold-standard autoradiography measurements. Label positioning was found to be optimal at 45°, while post-label delay was optimised to 0.55 s. Whole brain CBF measures were 109 ± 22, 111 ± 18 and 100 ± 15 mL/100 g/min by multiphase pCASL, and 108 ± 12, 116 ± 14 and 122 ± 16 mL/100 g/min by autoradiography in Wistar, SD and BDIX cohorts, respectively. Tumour model analysis shows that the developed methods also apply in disease states. Thus, optimised multiphase pCASL provides robust, reproducible and non-invasive measurement of CBF in rats.

Abstract Abnormal pH is a common feature of malignant tumors and has been associated clinically with suboptimal outcomes. Amide proton transfer magnetic resonance imaging (APT MRI) holds promise as a means to noninvasively measure tumor pH, yet multiple factors collectively make quantification of tumor pH from APT MRI data challenging. The purpose of this study was to improve our understanding of the biophysical sources of altered APT MRI signals in tumors. Combining in vivo APT MRI measurements with ex vivo histological measurements of protein concentration in a rat model of brain metastasis, we determined that the proportion of APT MRI signal originating from changes in protein concentration was approximately 66%, with the remaining 34% originating from changes in tumor pH. In a mouse model of hypopharyngeal squamous cell carcinoma (FaDu), APT MRI showed that a reduction in tumor hypoxia was associated with a shift in tumor pH. The results of this study extend our understanding of APT MRI data and may enable the use of APT MRI to infer the pH of individual patients' tumors as either a biomarker for therapy stratification or as a measure of therapeutic response in clinical settings. Significance: These findings advance our understanding of amide proton transfer magnetic resonance imaging (APT MRI) of tumors and may improve the interpretation of APT MRI in clinical settings.

Hyperpolarised MRI with Dynamic Nuclear Polarisation overcomes the fundamental thermodynamic limitations of conventional magnetic resonance, and is translating to human studies with several early-phase clinical trials in progress including early reports that demonstrate the utility of the technique to observe lactate production in human brain cancer patients. Owing to the fundamental coupling of metabolism and tissue function, metabolic neuroimaging with hyperpolarised [1- 13 C]pyruvate has the potential to be revolutionary in numerous neurological disorders (e.g. brain tumour, ischemic stroke, and multiple sclerosis). Through the use of [1- 13 C]pyruvate and ethyl-[1- 13 C]pyruvate in naïve brain, a rodent model of metastasis to the brain, or porcine brain subjected to mannitol osmotic shock, we show that pyruvate transport across the blood-brain barrier of anaesthetised animals is rate-limiting. We show through use of a well-characterised rat model of brain metastasis that the appearance of hyperpolarized [1- 13 C]lactate production corresponds to the point of blood-brain barrier breakdown in the disease. With the more lipophilic ethyl-[1- 13 C]pyruvate, we observe pyruvate production endogenously throughout the entire brain and lactate production only in the region of disease. In the in vivo porcine brain we show that mannitol shock permeabilises the blood-brain barrier sufficiently for a dramatic 90-fold increase in pyruvate transport and conversion to lactate in the brain, which is otherwise not resolvable. This suggests that earlier reports of whole-brain metabolism in anaesthetised animals may be confounded by partial volume effects and not informative enough for translational studies. Issues relating to pyruvate transport and partial volume effects must therefore be considered in pre-clinical studies investigating neuro-metabolism in anaesthetised animals, and we additionally note that these same techniques may provide a distinct biomarker of blood-brain barrier permeability in future studies.

Brain perfusion imaging can contribute valuable information in diseases such as cancer and stroke. Arterial spin labelling (ASL) is a non-invasive MRI technique enabling quantification of perfusion by imaging tissue following exchanges of water with magnetically tagged blood. Although ASL has recently been standardised in the clinic1, pre-clinical usage of ASL remains inconsistent between studies. Our aim here was to identify optimal parameters for pre-clinical ASL, taking into account the anatomical and MRI system differences between humans and rodents, for reproducible and accurate perfusion imaging of metastatic brain tumours in rats.

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.

Breast cancer and prostate cancer are the most common cancers diagnosed in women and men, respectively, in the UK, and radiotherapy is used extensively in the treatment of both. In vitro data suggest that tumours in the breast and prostate have unique properties that make a hypofractionated radiotherapy treatment schedule advantageous in terms of therapeutic index. Many clinical trials of hypofractionated radiotherapy treatment schedules have been completed to establish the extent to which hypofractionation can improve patient outcome. Here we present a concise description of hypofractionation, the mathematical description of converting between conventional and hypofractionated schedules, and the motivation for using hypofractionation in the treatment of breast and prostate cancer. Furthermore, we summarise the results of important recent hypofractionation trials and highlight the limitations of a hypofractionated treatment regimen.