Influenza season influence on outcome of new nodules in the NELSON study.

We evaluated the impact of the influenza season on outcome of new lung nodules in a LDCT lung cancer screening trial population. NELSON-trial participants with ≥ 1 new nodule detected in screening rounds two and three were included. Outcome (resolution or persistence) of new nodules detected per season was calculated and compared. Winter (influenza season) was defined as 1st October to 31st March, and compared to the summer (hay-fever season), 1st April to 30th September. Overall, 820 new nodules were reported in 529 participants. Of the total new nodules, 482 (59%) were reported during winter. When considering the outcome of all new nodules, there was no statistically significant association between summer and resolving nodules (OR 1.07 [CI 1.00-1.15], p = 0.066), also when looking at the largest nodule per participant (OR 1.37 [CI 0.95-1.98], p = 0.094). Similarly, there was no statistically significant association between season and screen detected cancers (OR 0.47 [CI 0.18-1.23], p = 0.123). To conclude, in this lung cancer screening population, there was no statistically significant association between influenza season and outcome of new lung nodules. Hence, we recommend new nodule management strategy is not influenced by the season in which the nodule is detected.

Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models.

Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on deep learning algorithms for small datasets. Fifty CT scans were retrospectively selected and the whole heart, ventricles and atria were contoured. A two stage deep learning pipeline was trained on 41 non contrast planning CTs, tuned with 3 CT scans and validated on 6 CT scans. In the first stage, An InceptionResNetV2 network was used to identify the slices that contained cardiac structures. The second stage consisted of three deep learning models trained on the images containing cardiac structures to segment the structures. The three deep learning models predicted the segmentations/contours on axial, coronal and sagittal images and are combined to create the final prediction. The final accuracy of the pipeline was quantified on 6 volumes by calculating the Dice similarity coefficient (DC), 95% Hausdorff distance (95% HD) and volume ratios between predicted and ground truth volumes. Median DC and 95% HD of 0.96, 0.88, 0.92, 0.80 and 0.82, and 1.86, 2.98, 2.02, 6.16 and 6.46 were achieved for the whole heart, right and left ventricle, and right and left atria respectively. The median differences in volume were -4, -1, + 5, -16 and -20% for the whole heart, right and left ventricle, and right and left atria respectively. The automatic contouring pipeline achieves good results for whole heart and ventricles. Robust automatic contouring with deep learning methods seems viable for local centers with small datasets.

Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation: a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms.

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance.

Diffusion weighted imaging of the breast: Performance of standardized breast tumor tissue selection methods in clinical decision making.

OBJECTIVES: In breast diffusion weighted imaging (DWI) protocol standardization, it is recently shown that no breast tumor tissue selection (BTTS) method outperformed the others. The purpose of this study is to analyze the feasibility of three fixed-size breast tumor tissue selection (BTTS) methods based on the reproducibility, accuracy and time-measurement in comparison to the largest oval and manual delineation in breast diffusion weighted imaging data. METHODS: This study is performed with a consecutive dataset of 116 breast lesions (98 malignant) of at least 1.0 cm, scanned in accordance with the EUSOBI breast DWI working group recommendations. Reproducibility of the maximum size manual (BTTS1) and of the maximal size round/oval (BTTS2) methods were compared with three smaller fixed-size circular BTTS methods in the middle of each lesion (BTTS3, 0.12 cm3 volume) and at lowest apparent diffusion coefficient (ADC) (BTTS4, 0.12 cm3; BTTS5, 0.24 cm3). Mean ADC values, intraclass-correlation-coefficients (ICCs), area under the curve (AUC) and measurement times (sec) of the 5 BTTS methods were assessed by two observers. RESULTS: Excellent inter- and intra-observer agreement was found for any BTTS (with ICC 0.88-0.92 and 0.92-0.94, respectively). Significant difference in ADCmean between any pair of BTTS methods was shown (p = <0.001-0.009), except for BTTS2 vs. BTTS3 for observer 1 (p = 0.10). AUCs were comparable between BTTS methods, with highest AUC for BTTS2 (0.89-0.91) and lowest for BTTS4 (0.76-0.85). However, as an indicator of clinical feasibility, BTTS2-3 showed shortest measurement times (10-15 sec) compared to BTTS1, 4-5 (19-39 sec). CONCLUSION: The performance of fixed-size BTTS methods, as a potential tool for clinical decision making, shows equal AUC but shorter ADC measurement time compared to manual or oval whole lesion measurements. The advantage of a fixed size BTTS method is the excellent reproducibility. A central fixed breast tumor tissue volume of 0.12 cm3 is the most feasible method for use in clinical practice.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis.

BACKGROUND: Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC). METHODS AND FINDINGS: In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95). CONCLUSIONS: None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease.

OBJECTIVES: The purpose of this study was to analyze the prognostic value of dynamic CT perfusion imaging (CTP) and CT derived fractional flow reserve (CT-FFR) for major adverse cardiac events (MACE). METHODS: 81 patients from 4 institutions underwent coronary computed tomography angiography (CCTA) with dynamic CTP imaging and CT-FFR analysis. Patients were followed-up at 6, 12, and 18 months after imaging. MACE were defined as cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, or revascularization. CT-FFR was computed for each major coronary artery using an artificial intelligence-based application. CTP studies were analyzed per vessel territory using an index myocardial blood flow, the ratio between territory and global MBF. The prognostic value of CCTA, CT-FFR, and CTP was investigated with a univariate and multivariate Cox proportional hazards regression model. RESULTS: 243 vessels in 81 patients were interrogated by CCTA with CT-FFR and 243 vessel territories (1296 segments) were evaluated with dynamic CTP imaging. Of the 81 patients, 25 (31%) experienced MACE during follow-up. In univariate analysis, a positive index-MBF resulted in the largest risk for MACE (HR 11.4) compared to CCTA (HR 2.6) and CT-FFR (HR 4.6). In multivariate analysis, including clinical factors, CCTA, CT-FFR, and index-MBF, only index-MBF significantly contributed to the risk of MACE (HR 10.1), unlike CCTA (HR 1.2) and CT-FFR (HR 2.2). CONCLUSION: Our study provides initial evidence that dynamic CTP alone has the highest prognostic value for MACE compared to CCTA and CT-FFR individually or a combination of the three, independent of clinical risk factors.

Respiratory level tracking with visual biofeedback for consistent breath-hold level with potential application in image-guided interventions.

BACKGROUND: To present and evaluate a new respiratory level biofeedback system that aids the patient to return to a consistent breath-hold level with potential application in image-guided interventions. METHODS: The study was approved by the local ethics committee and written informed consent was waived. Respiratory motion was recorded in eight healthy volunteers in the supine and prone positions, using a depth camera that measures the mean distance to thorax, abdomen and back. Volunteers were provided with real-time visual biofeedback on a screen, as a ball moving up and down with respiratory motion. For validation purposes, a conversion factor from mean distance (in mm) to relative lung volume (in mL) was determined using spirometry. Subsequently, without spirometry, volunteers were given breathing instructions and were asked to return to their initial breath-hold level at expiration ten times, in both positions, with and without visual biofeedback. For both positions, the median and interquartile range (IQR) of the absolute error in lung volume from initial breath-hold were determined with and without biofeedback and compared using Wilcoxon signed rank tests. RESULTS: Without visual biofeedback, the median difference from initial breath-hold was 124.6 mL (IQR 55.7-259.7 mL) for the supine position and 156.3 mL (IQR 90.9-334.7 mL) for the prone position. With the biofeedback, the difference was significantly decreased to 32.7 mL (IQR 12.8-59.6 mL) (p < 0.001) and 22.3 mL (IQR 7.7-47.0 mL) (p < 0.001), respectively. CONCLUSIONS: The use of a depth camera to provide visual biofeedback increased the reproducibility of breath-hold expiration level in healthy volunteers, with a potential to eliminate targeting errors caused by respiratory movement during lung image-guided procedures.

Non-invasive assessment of coronary artery geometry using coronary CTA.

AIM: To assess the association of coronary artery geometry with the severity of coronary artery disease (CAD). METHODS: 73 asymptomatic individuals at increased risk of CAD due to peripheral vascular disease (18 women, mean age 63.5 ±â€¯8.2 years) underwent coronary computed tomography angiography (coronary CTA) using first generation dual-source CT. Curvature and tortuosity of the coronary arteries were quantified using semi-automatically generated centerlines. Measurements were performed for individual segments and for the entire artery. Coronary segments were labeled according to the presence of significant stenosis, defined as >70% luminal narrowing, and the presence of plaque. Comparisons were made by segment and by artery, using linear mixed models. RESULTS: Overall, median curvature and tortuosity were, respectively, 0.094 [0.071; 0.120] and 1.080 [1.040; 1.120] on a per-segment level, and 0.096 [0.078; 0.118] and 1.175 [1.090; 1.420] on a per-artery level. Curvature was associated with significant stenosis at a per-segment (p < 0.001) and per-artery level (p = 0.002). Curvature was 16.7% higher for segments with stenosis, and 13.8% higher for arteries with stenosis. Tortuosity was associated with significant stenosis only at the per-segment level (p = 0.002). Curvature was related to the presence of plaque at the per-segment (p < 0.001) and per-artery level (p < 0.001), tortuosity was only related to plaque at the per-segment level (p < 0.001). CONCLUSION: Coronary artery geometry as derived from coronary CTA is related to the presence of plaque and significant stenosis.

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons.

AIM: To evaluate progressive changes in quantitative CT features of the non-solid component of ground-glass nodules (GGNs) from baseline to follow-up to differentiate invasive (minimally invasive adenocarcinoma [MIA] and invasive adenocarcinoma [IA]) GGNs from benign or pre-invasive (adenocarcinoma in situ [AIS]) lesions. MATERIALS AND METHODS: Patients with a GGN detected at baseline and follow-up computed tomography (CT), examined by tissue sampling were included in the study. The diameter and mean, maximum, minimum CT density and density deviation from the non-solid component of whole GGNs were measured. Progression of these features over time was analysed by linear regression analysis. Multivariate receiver operating characteristics analyses of combined measures created by a logistic regression model were performed to evaluate diagnostic performance for invasive GGNs. RESULTS: Sixty-one patients (24 males) with 70 GGNs were included. Fifteen GGNs were benign, six AIS, 38 MIA, and 11 IA. The mean diameter of all histological subtypes increased from baseline to follow-up, the largest increase was found in the MIA group (p<0.001). For MIA and IA, the mean, maximum, minimum density, and density deviation had a positive correlation over time, whilst benign and pre-invasive GGNs showed a negative correlation for these features. A diagnostic model based on three GGN features (increasing in diameter, mean density, and density deviation) identified invasive GGNs with a sensitivity, specificity and area under the receiver operating characteristic (ROC) curve (AUC) of 83.7%, 61.9%, and 0.786, respectively (p<0.001). CONCLUSION: In GGN follow-up, the diameter of benign and AIS, and invasive GGNs significantly increased. Additional analysis of mean density and density deviation in the non-solid component may help to identify invasive GGNs.

Diagnostic performance of semi-quantitative and quantitative stress CMR perfusion analysis: a meta-analysis.

BACKGROUND: Stress cardiovascular magnetic resonance (CMR) perfusion imaging is a promising modality for the evaluation of coronary artery disease (CAD) due to high spatial resolution and absence of radiation. Semi-quantitative and quantitative analysis of CMR perfusion are based on signal-intensity curves produced during the first-pass of gadolinium contrast. Multiple semi-quantitative and quantitative parameters have been introduced. Diagnostic performance of these parameters varies extensively among studies and standardized protocols are lacking. This study aims to determine the diagnostic accuracy of semi- quantitative and quantitative CMR perfusion parameters, compared to multiple reference standards. METHOD: Pubmed, WebOfScience, and Embase were systematically searched using predefined criteria (3272 articles). A check for duplicates was performed (1967 articles). Eligibility and relevance of the articles was determined by two reviewers using pre-defined criteria. The primary data extraction was performed independently by two researchers with the use of a predefined template. Differences in extracted data were resolved by discussion between the two researchers. The quality of the included studies was assessed using the 'Quality Assessment of Diagnostic Accuracy Studies Tool' (QUADAS-2). True positives, false positives, true negatives, and false negatives were subtracted/calculated from the articles. The principal summary measures used to assess diagnostic accuracy were sensitivity, specificity, andarea under the receiver operating curve (AUC). Data was pooled according to analysis territory, reference standard and perfusion parameter. RESULTS: Twenty-two articles were eligible based on the predefined study eligibility criteria. The pooled diagnostic accuracy for segment-, territory- and patient-based analyses showed good diagnostic performance with sensitivity of 0.88, 0.82, and 0.83, specificity of 0.72, 0.83, and 0.76 and AUC of 0.90, 0.84, and 0.87, respectively. In per territory analysis our results show similar diagnostic accuracy comparing anatomical (AUC 0.86(0.83-0.89)) and functional reference standards (AUC 0.88(0.84-0.90)). Only the per territory analysis sensitivity did not show significant heterogeneity. None of the groups showed signs of publication bias. CONCLUSIONS: The clinical value of semi-quantitative and quantitative CMR perfusion analysis remains uncertain due to extensive inter-study heterogeneity and large differences in CMR perfusion acquisition protocols, reference standards, and methods of assessment of myocardial perfusion parameters. For wide spread implementation, standardization of CMR perfusion techniques is essential. TRIAL REGISTRATION: CRD42016040176 .

Effects of caffeine intake prior to stress cardiac magnetic resonance perfusion imaging on regadenoson- versus adenosine-induced hyperemia as measured by T1 mapping.

The antagonistic effects of caffeine on adenosine receptors are a possible cause of false-negative stress perfusion imaging. The purpose of this study was to determine the effects of coffee intake <4 h prior to stress perfusion cardiac magnetic resonance imaging (CMR) in regadenoson- versus adenosine-induced hyperemia as measured with T1-mapping. 98 consecutive patients with suspected coronary artery disease referred for either adenosine or regadenoson perfusion CMR were included in this analysis. Twenty-four patients reported coffee consumption <4 h before CMR (15 patients with adenosine, and 9 patients with regadenoson); 74 patients reported no coffee intake (50 patients with adenosine, and 24 patients with regadenoson). T1 mapping was performed using a modified look-locker inversion recovery sequence. T1 reactivity was determined by subtracting T1rest from T1stress. T1rest, T1stress, and T1 reactivity in patients referred for regadenoson perfusion CMR were not significantly different when comparing patients with <4 h coffee intake and patients who reported no coffee intake (976 ± 4 ms, 1019 ± 48 ms, and 4.4 ± 3.2% vs 971 ± 33 ms, 1023 ± 43 ms, and 5.4 ± 2.4%) (p = 0.70, 0.79, and 0.40), and similar to values in patients without coffee intake undergoing adenosine CMR. In patients with <4 h coffee intake, T1stress, and T1 reactivity were significantly lower for adenosine (898 ± 51 ms, and -7.8 ± 5.0%) compared to regadenoson perfusion CMR (p < 0.001). Coffee intake <4 h prior to regadenoson perfusion CMR has no effect on stress-induced hyperemia as measured with T1 mapping.

Bone density loss on computed tomography at 3-year follow-up in current compared to former male smokers.

OBJECTIVES: Cigarette smoking negatively affects bone quality and increases fracture risk. Little is known on the effect of smoking cessation and computed tomography (CT)-derived bone mineral density (BMD) decline in the spine. We evaluated the association of current and former smoking with BMD decline after 3-year follow-up. METHODS: Male current and former smokers participating in a lung cancer screening trial who underwent baseline and 3-year follow-up CT were included. BMD was measured by manual placement of a region of interest in the first lumbar vertebra and expressed in Hounsfield Unit (HU). Multiple linear regression analysis was used to evaluate the association between pack years smoked and smoking status with BMD decline. RESULTS: 408 participants were included with median (25th-75th percentile) age of 59.4 (55.9-63.5) years. At the start of the study, 197 (48.3%) participants were current smokers and 211 (51.7%) were former smokers and had a similar amount of pack years. Current smokers had quit smoking for 6 (4-8) years prior to inclusion. There was no difference in BMD between current and former smokers at baseline (109±34 HU vs. 108±32 HU, p=0.96). At 3-year follow-up, current smokers had a mean BMD decline of -3±13 HU (p=0.001), while BMD in former smokers did not change as compared to baseline (1±13 HU, p=0.34). After adjustment for BMD at baseline and body mass index, current smoking was independently associated with BMD decline (-3.8 HU, p=0.003). Age, pack years, and the presence of a fracture at baseline did not associate with BMD decline. CONCLUSIONS: Current smokers showed a more rapid BMD decline over a 3-year period compared to former smokers. This information might be important to identify subjects at risk for osteoporosis and emphasizes the importance of smoking cessation in light of BMD decline.

Complication rates of CT-guided transthoracic lung biopsy: meta-analysis.

OBJECTIVES: To meta-analyze complication rate in computed tomography (CT)-guided transthoracic lung biopsy and associated risk factors. METHODS: Four databases were searched from 1/2000 to 8/2015 for studies reporting complications in CT-guided lung biopsy. Overall and major complication rates were pooled and compared between core biopsy and fine needle aspiration (FNA) using the random-effects model. Risk factors for complications in core biopsy and FNA were identified in meta-regression analysis. RESULTS: For core biopsy, 32 articles (8,133 procedures) were included and for FNA, 17 (4,620 procedures). Pooled overall complication rates for core biopsy and FNA were 38.8 % (95 % CI: 34.3-43.5 %) and 24.0 % (95 % CI: 18.2-30.8 %), respectively. Major complication rates were 5.7 % (95 % CI: 4.4-7.4 %) and 4.4 % (95 % CI: 2.7-7.0 %), respectively. Overall complication rate was higher for core biopsy compared to FNA (p < 0.001). For FNA, larger needle diameter was a risk factor for overall complications, and increased traversed lung parenchyma and smaller lesion size were risk factors for major complications. For core biopsy, no significant risk factors were identified. CONCLUSIONS: In CT-guided lung biopsy, minor complications were common and occurred more often in core biopsy than FNA. Major complication rate was low. For FNA, smaller nodule diameter, larger needle diameter and increased traversed lung parenchyma were risk factors for complications. KEY POINTS: • Minor complications are common in CT-guided lung biopsy • Major complication rate is low in CT-guided lung biopsy • CT-guided lung biopsy complications occur more often in core biopsy than FNA • Major complication rate is similar in core biopsy and FNA • Risk factors for FNA are larger needle diameter, smaller lesion size.

Optimal timing of image acquisition for arterial first pass CT myocardial perfusion imaging.

PURPOSE: To determine the optimal timing of arterial first pass computed tomography (CT) myocardial perfusion imaging (CTMPI) based on dynamic CTMPI acquisitions. METHODS AND MATERIALS: Twenty-five patients (59±8.4years, 14 male)underwent adenosine-stress dynamic CTMPI on second-generation dual-source CT in shuttle mode (30s at 100kV and 300mAs). Stress perfusion magnetic resonance imaging (MRI) was used as reference standard for differentiation of non-ischemic and ischemic segments. The left ventricle (LV) wall was manually segmented according to the AHA 16-segment model. Hounsfield units (HU) in myocardial segments and ascending (AA) and descending aorta (AD) were monitored over time. Time difference between peak AA and peak AD and peak myocardial enhancement was calculated, as well as the, time delay from fixed HU thresholds of 150 and 250 HU in the AA and AD to a minimal difference of 15 HU between normal and ischemic segments. Furthermore, the duration of the 15 HU difference between ischemic and non-ischemic segments was calculated. RESULTS: Myocardial ischemia was observed by MRI in 10 patients (56.3±9.0years; 8 male). The delay between the maximum HU in the AA and AD and maximal HU in the non-ischemic segments was 2.8s [2.2-4.3] and 0.0s [0.0-2.8], respectively. Differentiation between ischemic and non-ischemic myocardial segments in CT was best during a time window of 8.6±3.8s. Time delays for AA triggering were 4.5s [2.2-5.6] and 2.2s [0-2.8] for the 150 HU and 250 HU thresholds, respectively. While for AD triggering, time delays were 2.4s [0.0-4.8] and 0.0s [-2.2-2.6] for the 150 HU and 250 HU thresholds, respectively. CONCLUSION: In CTMPI, the differentiation between normal and ischemic myocardium is best accomplished during a time interval of 8.6±3.8s. This time window can be utilized by a test bolus or bolus tracking in the AA or AD using the time delays identified here.

Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation.

Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.

Image De-Identification Methods for Clinical Research in the XDS Environment.

To investigate possible de-identification methodologies within the Cross-Enterprise Document Sharing for imaging (XDS-I) environment in order to provide strengthened support for image data exchange as part of clinical research projects. De-identification, using anonymization or pseudonymization, is the most common method to perform information removal within DICOM data. However, it is not a standard part of the XDS-I profiles. Different methodologies were observed to define how and where de-identification should take place within an XDS environment used for scientific research. De-identification service can be placed in three locations within the XDS-I framework: 1) within the Document Source, 2) between the Document Source and Document Consumer, and 3) within the Document Consumer. First method has a potential advantage with respect to the exposure of the images to outside systems but has drawbacks with respect to additional hardware and configuration requirements. Second and third method have big concern in exposing original documents with all identifiable data being intact after leaving the Document Source. De-identification within the Document Source has more advantages compared to the other methods. On the contrary, it is less recommended to perform de-identification within the Document Consumer since it has the highest risk of the exposure of patients identity due to the fact that images are exposed without de-identification during the transfers.

Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography.

OBJECTIVE: The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets. METHODS: The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded. RESULTS: Mean differences from TTE were -0.19 (95% CI: -0.74 to 0.34) cm(2) for manual and -0.10 (95% CI: -0.45 to 0.25) cm(2) for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively. CONCLUSION: Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy.

PURPOSE: To compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header. MATERIALS AND METHODS: Ten DICOM toolkits were selected for de-identification tests. Tests were performed by using the system's default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization. RESULTS: Only one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification. CONCLUSION: Only five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed. KEY POINTS: • Free DICOM toolkits should be carefully used to prevent patient identity disclosure. • Each DICOM tool produces its own specific outcomes from the de-identification process. • In case optimal security is required, using one DICOM toolkit is proposed.