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To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD). Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin’s concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride’s definition. The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model’s prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks). Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester. • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.

Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. Materials and Methods: For lung segmentation, CXR images from the Japanese Society of Radiological Technology (JSRT, N = 247) and Montgomery databases (N = 138), were included, and 65 additional images depicting severe abnormalities from a public database were evaluated and annotated by a radiologist, thereby adding lung segmentation results to these images. Baseline U-net was used to segment the lungs in images from the three databases. Subsequently, the U-net network architecture was automatically optimized for lung segmentation from CXR images using Bayesian optimization. Dice similarity coefficient (DSC) was calculated to confirm segmentation. Results: Our results demonstrated that using baseline U-net yielded poorer lung segmentation results in our database than those in the JSRT and Montgomery databases, implying that robust segmentation of lungs may be difficult because of severe abnormalities. The DSC values with baseline U-net for the JSRT, Montgomery and our databases were 0.979, 0.941, and 0.889, respectively, and with optimized U-net, 0.976, 0.973, and 0.932, respectively. Conclusion: For robust lung segmentation, the U-net architecture was optimized via Bayesian optimization, and our results demonstrate that the optimized U-net was more robust than baseline U-net in lung segmentation from CXR images with large-sized abnormalities.

To develop and evaluate a radiomics approach for classifying histological subtypes and epidermal growth factor receptor (EGFR) mutation status in lung cancer on PET/CT images. PET/CT images of lung cancer patients were obtained from public databases and used to establish two datasets, respectively to classify histological subtypes (156 adenocarcinomas and 32 squamous cell carcinomas) and EGFR mutation status (38 mutant and 100 wild-type samples). Seven types of imaging features were obtained from PET/CT images of lung cancer. Two types of machine learning algorithms were used to predict histological subtypes and EGFR mutation status: random forest (RF) and gradient tree boosting (XGB). The classifiers used either a single type or multiple types of imaging features. In the latter case, the optimal combination of the seven types of imaging features was selected by Bayesian optimization. Receiver operating characteristic analysis, area under the curve (AUC), and tenfold cross validation were used to assess the performance of the approach. In the classification of histological subtypes, the AUC values of the various classifiers were as follows: RF, single type: 0.759; XGB, single type: 0.760; RF, multiple types: 0.720; XGB, multiple types: 0.843. In the classification of EGFR mutation status, the AUC values were: RF, single type: 0.625; XGB, single type: 0.617; RF, multiple types: 0.577; XGB, multiple types: 0.659. The radiomics approach to PET/CT images, together with XGB and Bayesian optimization, is useful for classifying histological subtypes and EGFR mutation status in lung cancer.

To compare observer performance of detecting bone metastases between bone scintigraphy, including planar scan and single-photon emission computed tomography, and computed tomography (CT) temporal subtraction (TS). Data on 60 patients with cancer who had undergone CT (previous and current) and bone scintigraphy were collected. Previous CT images were registered to the current ones by large deformation diffeomorphic metric mapping; the registered previous images were subtracted from the current ones to produce TS. Definitive diagnosis of bone metastases was determined by consensus between two radiologists. Twelve readers independently interpreted the following pairs of examinations: NM-pair, previous and current CTs and bone scintigraphy, and TS-pair, previous and current CTs and TS. The readers assigned likelihood levels to suspected bone metastases for diagnosis. Sensitivity, number of false positives per patient (FPP), and reading time for each pair of examinations were analysed for evaluating observer performance by performing the Wilcoxon signed-rank test. Figure-of-merit (FOM) was calculated using jackknife alternative free-response receiver operating characteristic analysis. The sensitivity of TS was significantly higher than that of bone scintigraphy (54.3% vs. 41.3%, p = 0.006). FPP with TS was significantly higher than that with bone scintigraphy (0.189 vs. 0.0722, p = 0.003). FOM of TS tended to be better than that of bone scintigraphy (0.742 vs. 0.691, p = 0.070). Sensitivity of TS in detecting bone metastasis was significantly higher than that of bone scintigraphy, but still limited to 54%. TS might be superior to bone scintigraphy for early detection of bone metastasis. • Computed tomography temporal subtraction was helpful in early detection of bone metastases. • Sensitivity for bone metastasis was higher for computed tomography temporal subtraction than for bone scintigraphy. • Figure-of-merit of computed tomography temporal subtraction was better than that of bone scintigraphy.

To assess whether temporal subtraction (TS) images of brain CT improve the detection of suspected brain infarctions. Study protocols were approved by our institutional review board, and informed consent was waived because of the retrospective nature of this study. Forty-two sets of brain CT images of 41 patients, each consisting of a pair of brain CT images scanned at two time points (previous and current) between January 2011 and November 2016, were collected for an observer performance study. The 42 sets consisted of 23 cases with a total of 77 newly developed brain infarcts or hyperdense artery signs confirmed by two radiologists who referred to additional clinical information and 19 negative control cases. To create TS images, the previous images were registered to the current images by partly using a non-rigid registration algorithm and then subtracted. Fourteen radiologists independently interpreted the images to identify the lesions with and without TS images with an interval of over 4 weeks. A figure of merit (FOM) was calculated along with the jackknife alternative free-response receiver-operating characteristic analysis. Sensitivity, number of false positives per case (FPC) and reading time were analyzed by the Wilcoxon signed-rank test. The mean FOM increased from 0.528 to 0.737 with TS images (p < 0.0001). The mean sensitivity and FPC improved from 26.5% and 0.243 to 56.0% and 0.153 (p < 0.0001 and p = 0.239), respectively. The mean reading time was 173 s without TS and 170 s with TS (p = 0.925). The detectability of suspected brain infarctions was significantly improved with TS CT images. • Although it is established that MRI is superior to CT in the detection of strokes, the first choice of modality for suspected stroke patients is often CT. • An observer performance study with 14 radiologists was performed to evaluate whether temporal subtraction images derived from a non-rigid transformation algorithm can significantly improve the detectability of newly developed brain infarcts on CT. • Temporal subtraction images were shown to significantly improve the detectability of newly developed brain infarcts on CT.

This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.

The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n = 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality, visibility of the iliac artery, and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure.

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

4 TECHNICAL EFFICACY STAGE: 4 J. Magn. Reson. Imaging 2020;52:1263-1264.

Background This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders. Methods This study included 122 female patients (14 with uterine endometrial cancer, 15 with uterine cervical cancer, and 55 with uterine leiomyoma). U-net architecture optimized for our research was used for automatic segmentation. Three-fold cross-validation was performed for validation. The results of manual segmentation of the uterus by a radiologist on T2-weighted sagittal images were used as the gold standard. Dice similarity coefficient (DSC) and mean absolute distance (MAD) were used for quantitative evaluation of the automatic segmentation. Visual evaluation using a 4-point scale was performed by two radiologists. DSC, MAD, and the score of the visual evaluation were compared between uteruses with and without uterine disorders. Results The mean DSC of our model for all patients was 0.82. The mean DSCs for patients with and without uterine disorders were 0.84 and 0.78, respectively (p = 0.19). The mean MADs for patients with and without uterine disorders were 18.5 and 21.4 [pixels], respectively (p = 0.39). The scores of the visual evaluation were not significantly different between uteruses with and without uterine disorders. Conclusions Fully automatic uterine segmentation with our modified U-net was clinically feasible. The performance of the segmentation of our model was not influenced by the presence of uterine disorders.

Temporal subtraction of CT (TS) images improves detection of newly developed bone metastases (BM). We sought to determine whether TS improves detection of BM by radiology residents as well. We performed an observer study using a previously reported dataset, consisting of 60 oncology patients, each with previous and current CT images. TS images were calculated using in-house software. Four residents independently interpreted twice the 60 sets of CT images, without and with TS. They identified BM by marking suspicious lesions likely to be BM. Lesion-based sensitivity and number of false positives per patient were calculated. Figure-of-merit (FOM) was calculated. Detectability of BM, with and without TS, was compared between radiology residents and board-certified radiologists, as published previously. FOM of residents significantly improved by implementing TS (p value

We have developed a novel medical device software program that is intended to improve radiologists’ performance with regard to the detection of bone metastases on CT images. To establish the protocol of the clinical performance evaluation for regulatory approval, the feasibility of nonclinical study (NCS) and clinical study (CS) conforming to the principles of GCP was compared. There were two main issues with establishing the protocol in Japan. Firstly, retrospective case collection was not accepted for CS due to concerns regarding selection bias. However, it is difficult to prospectively collect cases for use in an observer study due to the wide variety of bone metastases cases required. Secondly, when setting the reference standard (RS) for bone metastases, the utilization of any patient information was not accepted for NCS due to concerns regarding an inappropriate RS setting. However, it was predicted that it would be difficult to set accurate RSs only using CT images. Consequently, following discussion with a Japanese regulatory body, the protocol was established as an NCS using retrospectively collected CT images. Furthermore, we agreed to use anonymized clinical images to set the RS and to rescue the test results by reviewing the RS after the observer study, hence ensuring reliability. An NCS was a feasible method of evaluating the novel medical device software while also complying with quality management systems. By using real-world clinical image databases, this clinical performance evaluation method may be both effective and efficient as a path to regulatory approval of certain medical device software.

The purpose of this study was to evaluate agreement between standard-dose CT (SDCT) and ultralow-dose CT (ULDCT) findings with respect to emphysema quantification. ULDCT images were reconstructed with and without iterative reconstruction (IR). Adaptive iterative dose reduction with 3D processing was used for IR.Fifty patients who underwent SDCT and ULDCT were included. The tube current for SDCT was 250 mA, and that for ULDCT was 10 mA. SDCT, ULDCT without IR, and ULDCT with IR were used for emphysema quantification. The low-attenuation volume percentage (LAV%) in the lungs at four thresholds (-970, -950, -930, and -910 HU), mean lung attenuation, and total lung volume were computed. Concordance correlation coefficients (CCC) were used to assess the agreement of emphysema quantification between SDCT and ULDCT.The LAV% CCC values were 0.310-0.789 between SDCT and ULDCT without IR and 0.934-0.966 between SDCT and ULDCT with IR. The agreement of LAV% improved when IR was used for ULDCT. The mean lung attenuation CCC value between SDCT and ULDCT without IR was substantial (0.957), whereas that between SDCT and ULDCT with IR was poor (0.890). The total lung volume CCC values were substantial (0.982 with IR, 0.983 without IR).ULDCT with and without IR can substitute for SDCT in emphysema quantification.

Background and objective Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep learning model. Methods We included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24 h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning detection model was constructed from the training set using the You Only Look Once v3 model and Visual Geometry Group 16 classification model. Then, the two-stage model performed the AIS detection process in the test set. To assess the detection model's results, a board-certified radiologist also evaluated the test set head CT images with and without the aid of the detection model. The sensitivity of AIS detection and number of false positives were calculated for the evaluation of the test set detection results. The sensitivity of the radiologist with and without the software detection results was compared using the McNemar test. A p-value of less than 0.05 was considered statistically significant. Results For the two-stage model and radiologist without and with the use of the software results, the sensitivity was 37.3%, 33.3%, and 41.3%, respectively, and the number of false positives per one case was 1.265, 0.327, and 0.388, respectively. On using the two-stage detection model's results, the board-certified radiologist's detection sensitivity significantly improved (p-value = 0.0313). Conclusions Our detection system involving the two-stage deep learning model significantly improved the radiologist's sensitivity in AIS detection.

BACKGROUND AND PURPOSE: Pretreatment diagnosis for the location of shunts and arterial feeders of spinal arteriovenous fistulas is crucial. This study aimed to evaluate the utility of subtracted CT angiography imaging by using nonrigid registration (R-CTA) in patients with spinal arteriovenous fistulas compared with conventional CTA imaging. MATERIALS AND METHODS: The records of 15 consecutive subjects (mean age, 65 years; 2 women) who had undergone CTA and digital subtraction angiography for clinically suspected spinal arteriovenous fistula were reviewed. From CTA images obtained at the arterial and late arterial phases, warped images of the late arterial phase were obtained by using nonrigid registration that was adjusted to the arterial phase images. R-CTA images were then obtained by subtracting the warped images from the arterial phase images. The accuracies of using nonrigid registration and conventional spinal CTA and the time required for detecting arterial feeders in spinal arteriovenous fistulas were analyzed for each patient with DSA results as a standard reference. The difference between R-CTA and conventional spinal CTA was assessed by the Welch test and the McNemar χ2 test. RESULTS: R-CTA had a higher accuracy compared with conventional spinal CTA (80% versus 47%, P = .025). The time for interpretation was reduced in R-CTA compared with conventional spinal CTA (45.1 versus 97.1 seconds, P = .002). CONCLUSIONS: Our subtracted CTA imaging by using nonrigid registration detects feeders of spinal arteriovenous fistulas more accurately and quickly than conventional CTA.

The purpose of this study was to assess usefulness of local binary patterns (LBP) and related texture features, namely completed local binary patterns (CLBP) and local ternary patterns (LTP), for the classification of emphysema subtypes on low-dose CT images. Fifty patients (34 men and 16 women; age, 67.5 ± 10.1 years) who underwent low-dose CT (60 mAs) were included. They were comprised of 17 never smokers, 13 smokers without COPD, and 20 smokers with COPD. By consensus reading of low-dose CT images from these patients, two radiologists selected 3681 nonoverlapping regions of interest (ROIs) and annotated them as one of the following three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. From these ROIs, histogram of CT densities, LBP, CLBP, and LTP were calculated, and the 3 types of texture histograms were concatenated with the CT density histogram. These 3 types of histograms (referred to as combined LBP, combined CLBP, and combined LTP) were used to classify ROI using linear support vector machine. For each type of the combined histogram, the accuracy of classification was determined by patient-based 10-fold cross validation. The best accuracy of combined LBP, combined CLBP, and combined LTP were 81.36%, 82.99%, and 83.29%, respectively. Compared to the classification accuracies obtained with combined LBP, those with combined LTP or combined CLBP were consistently improved. In conclusion, the results of this study suggest that, on low-dose CT, LTP and CLBP were more useful for the classification of emphysema subtypes than LBP.

To compare the use of dynamic oxygen-enhanced magnetic resonance (MR) imaging with the use of quantitatively assessed computed tomography (CT) for assessment of clinical stage and evaluation of pulmonary functional change due to treatment in patients with asthma.The institutional review board of Kobe University Hospital approved this study, and written informed consent was obtained from each subject. Thirty consecutive patients with asthma (17 men and 13 women; age range, 27-78 years) underwent dynamic oxygen-enhanced MR imaging, multidetector CT, and assessment of forced expiratory volume in 1 second. All patients were classified as having one of four stages of asthma according to the guidelines of the National Asthma Education and Prevention Program. Relative enhancement ratio ( RER relative enhancement ratio ) and wash-in time maps were generated by means of pixel-by-pixel analyses. Regions of interest were placed on images of the lung in all sections, and all measurements were averaged to determine mean RER relative enhancement ratio and mean wash-in time for each subject. Percentage of airway wall area and mean lung density were determined at quantitative CT. For comparison of the modalities for assessment of clinical stage, indexes of subjects at all clinical stages were compared by means of the Tukey honestly significant difference test. Evaluation of pulmonary functional improvement was assessed by correlating improvement of each index with that of forced expiratory volume.Mean wash-in time was significantly different among patients with asthma of different clinical stages (P.05), but significant differences between mean RER relative enhancement ratio and percentage of airway wall area were observed for a limited number of clinical stages (P.05). Improvement of mean RER relative enhancement ratio (r = 0.63, P = .0002) and mean wash-in time (r = -0.75, P.0001) was significantly correlated with forced expiratory volume.Dynamic oxygen-enhanced MR imaging has potential as a tool for assessment of clinical stage and evaluation of pulmonary functional change due to treatment in patients with asthma.

Rationale and Objectives The purpose of this study was to analyze the relationship between airflow limitation and two types of computed tomography (CT) measurements: expiratory/inspiratory (E/I) ratio and E/I difference of percentage of low-attenuation lung regions (LAA%). Materials and Methods Thirty patients who underwent inspiratory and expiratory CT scans were included in this study. The CT data were used to calculate the LAA% E/I ratio and E/I difference. Other types of CT measurements were also obtained, including the E/I ratio and E/I difference of lung volume, mean lung density, standard deviation, skewness, and kurtosis. LAA% was calculated at 20 thresholds (−990 to −800 HU). Pearson's correlation between the measurements and forced expiratory flow in 1 second was used to determine the efficacy of LAA% E/I ratio and E/I difference. P values of Results The LAA% E/I ratio and E/I difference significantly correlated with forced expiratory flow in 1 second. The best correlation coefficient for the LAA% E/I ratio was −0.699 (P = 1.75 × 10−5) and for the LAA% E/I difference was −0.723 (P = 6.53 × 10−6). The best correlation coefficient for the LAA% E/I difference was stronger than that for the other types of CT measurements. Conclusions The LAA% E/I ratio and E/I difference significantly correlated with airflow limitation in chronic obstructive pulmonary disease.

The purpose of this study was to investigate efficacy of two types of emphysema quantification: percentage of low-attenuation lung regions (%LA); and size distribution of these regions. On a log-log plot, cumulative frequency-size distribution of low-attenuation lung regions can be fitted by a straight line whose slope (D) has been reported to reflect diffusing capacity. In this study, %LA and D were compared with pulmonary function test (PFT) parameters, especially with ratio of diffusing capacity of carbon monoxide to effective alveolar ventilation (i.e., DLCO/VA).Thin-section unenhanced CT images were acquired from 30 patients (25 men, five women; mean [SD] age, 70.1 ± 12.1 years), of whom 25 had received diagnosis of COPD, and %LA and D were calculated at 20 thresholds, ranging from -995 to -900 HU. To determine utility of %LA and D, we used Pearson's correlation for emphysema quantification and PFT. Significance of the coefficients was determined with Bonferroni correction (p0.0025). Finally, the relationships between emphysema quantification and DLCO/VA were examined by linear models and Akaike information criterion (AIC).The correlation coefficients for %LA and DLCO/VA were statistically significant at all the thresholds (optimal coefficient, -0.761). The correlation coefficients for D and DLCO/VA were statistically significant at the thresholds from -945 to -900 HU (optimal coefficient, -0.646). AIC values showed that the most accurate prediction of DLCO/VA was obtained by the model incorporating both %LA and D.Both %LA and D showed significant correlation with DLCO/VA. Combining %LA and D resulted in more accurate evaluation of DLCO/VA than did using %LA or D alone.

To evaluate and compare the capability of diffusion-weighted MR imaging (DWI) and CT for assessment of management needs for anterior mediastinal solitary tumors.Thirty-five patients with pathologically confirmed anterior mediastinal tumors were enrolled. The tumors were divided into two groups according to need for management: tumors not needing further intervention or treatment (group A; thymoma type A, AB and B1) and tumors needing further intervention and treatment (group B; other thymoma types and malignancies). The apparent diffusion coefficient (ADC) of each tumor was measured, and probabilities of malignancy and need for further intervention and treatment were visually assessed on CT. The differences in ADCs between group A and B and between malignancies and thymomas in group B were evaluated with the Mann-Whitney's U-test. Feasible threshold values for differentiation of group B from group A and distinguishing malignancies from thymomas assessed as group B were determined by the ROC-based positive test, and McNemar's test was used for comparing diagnostic capabilities of DWI with those of CT.ADCs for the two groups were significantly different (p0.001). Application of the threshold value for differentiation of group B from A showed no significant difference (p0.05). Application of the feasible threshold value for distinguishing malignant from thymomas assessed as group B showed that specificity (76.9%) and accuracy (85.2%) of DWI were significantly better than those of visual score (p0.05).DWI has useful potential for the assessment of management needs for anterior mediastinum solitary tumors as well as CT.

To prospectively and directly compare oxygen-enhanced (O2-enhanced) MRI with thin-section CT for pulmonary functional loss and disease severity assessment in connective tissue disease (CTD) patients with interstitial lung disease (ILD).Thin-section CT, O2-enhanced MRI, pulmonary function test and serum KL-6 were administered to 36 CTD patients with ILD (23 men, 13 women; mean age: 63.9 years) and nine CTD patients without ILD (six men, and three women; mean age: 62.0 years). A relative-enhancement ratio (RER) map was generated from O2-enhanced MRI and mean relative enhancement ratio (MRER) for each subject was calculated from all ROI measurements. CT-assessed disease severity was evaluated with a visual scoring system from each of the thin-section CT data. MRER and CT-assessed disease severities of CTD patients with and without ILD were then statistically compared. To assess capability for pulmonary functional loss and disease severity assessment in CTD patients, correlations of MRER and CT-assessed disease severity with pulmonary functional parameters and serum KL-6 in all subjects were statistically determined.MRER and CT-assessed disease severity showed significant differences between CTD patients with (MRER: 0.15 ± 0.08, CT-assessed disease severity: 13.0 ± 7.4%) and without ILD (MRER: 0.25 ± 0.06, p=0.0011; CT-assessed disease severity: 1.6 ± 1.6%, p0.0001). MRER and CT-assessed disease severity correlated significantly with pulmonary functional parameters and serum KL-6 in all subjects (0.61 ≤ r ≤ 0.79, p0.05).O2-enhanced MRI was found to be as useful as thin-section CT for pulmonary functional loss and disease severity assessment of CTD patients with ILD.

The purpose of this study was to assess whether homology-based emphysema quantification (HEQ) is significantly associated with lung cancer risk. This retrospective study was approved by our institutional review board. We included 576 patients with lung nodules (317 men and 259 women; age, 66.8 ± 12.3 years), who were selected from a database previously generated for computer-aided diagnosis. Of these, 283 were diagnosed with lung cancer, whereas the remaining 293 showed benign lung nodules. HEQ was performed and percentage of low-attenuation lung area (LAA%) was calculated on the basis of computed tomography scans. Statistical models were constructed to estimate lung cancer risk using logistic regression; sex, age, smoking history (Brinkman index), LAA%, and HEQ were considered independent variables. The following three models were evaluated: the base model (sex, age, and smoking history); the LAA% model (the base model + LAA%); and the HEQ model (the base model + HEQ). Model performance was assessed using receiver operating characteristic analysis and the associated area under the curve (AUC). Differences in AUCs among the models were evaluated using Delong’s test. AUCs of the base, LAA%, and HEQ models were 0.585, 0.593, and 0.622, respectively. HEQ coefficient was statistically significant in the HEQ model (P = 0.00487), but LAA% coefficient was not significant in the LAA% model (P = 0.199). Delong’s test revealed significant difference in AUCs between the LAA% and HEQ models (P = 0.0455). In conclusion, after adjusting for age, sex, and smoking history (Brinkman index), HEQ was significantly associated with lung cancer risk.

Purpose The purpose of this study was to compare diagnostic capabilities for assessment of recurrence in non-small cell lung cancer (NSCLC) patients by contrast-enhanced whole-body MRI (CE-WB-MRI) with and without CE-Quick 3D and double RF fat suppression technique (DFS), FDG-PET/CT and conventional radiological examinations. Materials and methods A total of 134 pathologically proven and completely resected NSCLC patients (78 males, 56 females; mean age: 72 years) underwent FDG-PET/CT, CE-WB-MRI with and without Quick 3D and DFS at 3 T as well as conventional radiological examinations. The probability of recurrence was assessed with a 5-point scoring system on a per-patient basis, and final diagnosis was made by consensus between two readers. The capability for overall recurrence assessment by all the methods was compared by means of ROC analysis and their sensitivity, specificity and accuracy by means of McNemar's test. Results Although areas under the curve did not show any significant differences, specificity (100%) and accuracy (95.5%) of CE-WB-MRI with CE-Quick 3D and DFS were significantly higher than those of FDG-PET/CT (specificity: 93.6%, p = 0.02; accuracy: 89.6%, p = 0.01) and conventional radiological examinations (specificity: 92.7%, p = 0.01; accuracy: 91.0%, p = 0.03). In addition, specificity of CE-WB-MRI without CE-Quick 3D and DFS (100%) was significantly higher than that of FDG-PET/CT (p = 0.02) and conventional radiological examinations (p = 0.01). Conclusion Specificity and accuracy of CE-WB-MRI with CE-Quick 3D and DFS for assessment of recurrence in NSCLC patients are at least as high as, or higher than those of others.

Purpose To assess the influence of ultrashort TE (UTE) intervals on pulmonary magnetic resonance imaging (MRI) with UTEs (UTE-MRI) for pulmonary functional loss assessment and clinical stage classification of smokers. Materials and Methods A total 60 consecutive smokers (43 men and 17 women; mean age 70 years) with and without COPD underwent thin-section multidetector row computed tomography (MDCT), UTE-MRI, and pulmonary functional measurements. For each smoker, UTE-MRI was performed with three different UTE intervals (UTE-MRI A: 0.5 msec, UTE-MRI B: 1.0 msec, UTE-MRI C: 1.5 msec). By using the GOLD guidelines, the subjects were classified as: “smokers without COPD,” “mild COPD,” “moderate COPD,” and “severe or very severe COPD.” Then the mean T2* value from each UTE-MRI and CT-based functional lung volume (FLV) were correlated with pulmonary function test. Finally, Fisher's PLSD test was used to evaluate differences in each index among the four clinical stages. Results Each index correlated significantly with pulmonary function test results (P

Purpose To evaluate the utility of pulmonary magnetic resonance (MR) imaging with ultra-short echo times (UTEs) at a 3.0 T MR system for pulmonary functional loss and disease severity assessments of connective tissue disease (CTD) patients with interstitial lung disease (ILD). Materials and methods This prospective study was approved by the institutional review board, and written informed consent was obtained from 18 CTD patients (eight men and ten women) and eight normal subjects with suspected chest disease (three men and five women). All subjects underwent thin-section MDCT, pulmonary MR imaging with UTEs, pulmonary function test and serum KL-6. Regional T2* maps were generated from each MR data set, and mean T2* values were determined from ROI measurements. From each thin-section MDCT data set, CT-based disease severity was evaluated with a visual scoring system. Mean T2* values for normal and CTD subjects were statistically compared by using Student's t-test. To assess capability for pulmonary functional loss and disease severity assessments, mean T2* values were statistically correlated with pulmonary functional parameters, serum KL-6 and CT-based disease severity. Results Mean T2* values for normal and CTD subjects were significantly different (p = 0.0019) and showed significant correlations with %VC, %DLCO, serum KL-6 and CT-based disease severity of CTD patients (p Conclusion Pulmonary MR imaging with UTEs is useful for pulmonary functional loss and disease severity assessments of CTD patients with ILD.

The objective of our study was to prospectively compare the capability of dynamic area-detector CT analyzed with different mathematic methods and PET/CT in the management of pulmonary nodules.Fifty-two consecutive patients with 96 pulmonary nodules underwent dynamic area-detector CT, PET/CT, and microbacterial or pathologic examinations. All nodules were classified into the following groups: malignant nodules (n = 57), benign nodules with low biologic activity (n = 15), and benign nodules with high biologic activity (n = 24). On dynamic area-detector CT, the total, pulmonary arterial, and systemic arterial perfusions were calculated using the dual-input maximum slope method; perfusion was calculated using the single-input maximum slope method; and extraction fraction and blood volume (BV) were calculated using the Patlak plot method. All indexes were statistically compared among the three nodule groups. Then, receiver operating characteristic analyses were used to compare the diagnostic capabilities of the maximum standardized uptake value (SUVmax) and each perfusion parameter having a significant difference between malignant and benign nodules. Finally, the diagnostic performances of the indexes were compared by means of the McNemar test.No adverse effects were observed in this study. All indexes except extraction fraction and BV, both of which were calculated using the Patlak plot method, showed significant differences among the three groups (p0.05). Areas under the curve of total perfusion calculated using the dual-input method, pulmonary arterial perfusion calculated using the dual-input method, and perfusion calculated using the single-input method were significantly larger than that of SUVmax (p0.05). The accuracy of total perfusion (83.3%) was significantly greater than the accuracy of the other indexes: pulmonary arterial perfusion (72.9%, p0.05), systemic arterial perfusion calculated using the dual-input method (69.8%, p0.05), perfusion (66.7%, p0.05), and SUVmax (60.4%, p0.05).Dynamic area-detector CT analyzed using the dual-input maximum slope method has better potential for the diagnosis of pulmonary nodules than dynamic area-detector CT analyzed using other methods and than PET/CT.

Since the publication of the Radiologic Diagnostic Oncology Group Report in 1991, the clinical application of pulmonary magnetic resonance imaging (MRI) in patients with lung cancer has been limited. In contrast, MRI for lung cancer has undergone continuous development, and several promising techniques have been introduced to overcome the previously suggested limitations. In addition, comparative studies involving multidetector-row computed tomography and positron emission tomography or positron emission tomography/computed tomography with 2-deoxy-2-[F]fluoro-D-glucose have shown useful new clinical applications for MRI in lung cancer. Moreover, MRI can provide not only morphologic information based on various parameters such as T1 and T2 relaxation times, tissue diffusion, perfusion, etc. but also functional information; it also has a significant role in nuclear medicine studies. In this review article, we describe recent advances made in MRI with respect to lung cancer, focusing on (1) detection of solid pulmonary nodules; (2) characterization of solid pulmonary nodules; (3) TNM staging assessment using chest and whole-body MRI examinations; (4) prediction of postsurgical lung function; and (5) prediction of tumor treatment response. We believe that further basic studies, as well as studies on clinical applications of new MRI techniques, are important for improving the management of lung cancer patients.

The purpose of this study was to compare three-dimensional airway lumen volumetry with bronchial wall area and parenchymal densitometry in the assessment of airway obstruction in pulmonary emphysema.56 patients, who were smokers, underwent CT examination and pulmonary function tests (PFTs). For quantitative assessments, the following parameters were computationally calculated: (1) percentage of voxels -950, -960 and -970 HU in the lung (%LAA(-950), %LAA(-960) and %LAA(-970), respectively); (2) percentage of partial bronchi luminal volumes per total luminal volumes (LV(main), main and distal bronchial volume/total luminal volume; LV(lobe), lobar and distal bronchial volume/total luminal volume); and (3) mean wall area percentages of segmental bronchi of the right apical and left apicoposterior segment (WA%(seg)) and of subsegmental bronchi (WA%(sub)) in the upper lobes. These parameters were correlated with PFTs and statistically compared between a chronic obstructive pulmonary disease (COPD) group [forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC)70] and a non-COPD group.FEV1, maximum mid-expiratory flow rate and forced expiratory flow at 25% vital capacity had significant correlation with LV(main) (r0.53, p0.0001), LV(lobe) (r0.52, p0.0001), WA%(seg) (|r|0.29, p0.05) and WA%(sub) (|r|0.31, p0.05). FEV1/FVC had significant correlation with all parameters (0.27|r|0.52, p0.05). LV(main) and LV(lobe) and WA%(seg) and WA%(sub) were significantly different between the two groups (LV(main) and LV(lobe); p0.0001, WA%(seg) and WA%(sub); p0.05).Bronchial luminal volumetric assessment better reflected the airflow limitation parameters.Bronchial luminal volumetric assessment can potentially be used to gauge airflow limitation in pulmonary emphysema.

Mizuho Nishio,1 Kazuaki Nakane,2 Yutaka Tanaka3 1Clinical PET Center, Institute of Biomedical Research and Innovation, Hyogo, Japan; 2Department of Molecular Pathology, Osaka University Graduate School of Medicine and Health Science, Osaka, Japan; 3Department of Radiology, Chibune General Hospital, Osaka, Japan Background: Homology is a mathematical concept that can be used to quantify degree of contact. Recently, image processing with the homology method has been proposed. In this study, we used the homology method and computed tomography images to quantify emphysema.Methods: This study included 112 patients who had undergone computed tomography and pulmonary function test. Low-attenuation lung regions were evaluated by the homology method, and homology-based emphysema quantification (b0, b1, nb0, nb1, and R) was performed. For comparison, the percentage of low-attenuation lung area (LAA%) was also obtained. Relationships between emphysema quantification and pulmonary function test results were evaluated by Pearson’s correlation coefficients. In addition to the correlation, the patients were divided into the following three groups based on guidelines of the Global initiative for chronic Obstructive Lung Disease: Group A, nonsmokers; Group B, smokers without COPD, mild COPD, and moderate COPD; Group C, severe COPD and very severe COPD. The homology-based emphysema quantification and LAA% were compared among these groups.Results: For forced expiratory volume in 1 second/forced vital capacity, the correlation coefficients were as follows: LAA%, -0.603; b0, -0.460; b1, -0.500; nb0, -0.449; nb1, -0.524; and R, -0.574. For forced expiratory volume in 1 second, the coefficients were as follows: LAA%, -0.461; b0, -0.173; b1, -0.314; nb0, -0.191; nb1, -0.329; and R, -0.409. Between Groups A and B, difference in nb0 was significant (P-value = 0.00858), and those in the other types of quantification were not significant.Conclusion: Feasibility of the homology-based emphysema quantification was validated. The homology-based emphysema quantification was useful for the assessment of emphysema severity. Keywords: homology, emphysema, COPD, low-attenuation lung region

The purpose of our study was to prospectively and directly compare capability of O2-enhanced MRI, MDCT, and perfusion SPECT/CT to clinical outcome measurements in candidates for lung volume reduction surgery (LVRS).Twenty-five consecutive candidates for LVRS (20 men and five women; age range, 45-76 years) underwent MDCT, O2-enhanced MRI, and perfusion SPECT/CT before and after LVRS. Clinical outcomes for each candidate were evaluated in terms of differences between pre- and postoperative percentage forced expiratory volume in 1 second (%FEV1), Pao2, and 6-minute walking distance. Quantitatively assessed upper/lower lung ratios on O2-enhanced MRI, MDCT, and SPECT/CT were calculated from regional relative enhancement ratios, functional lung volumes, and radioisotope uptakes between upper and lower lungs. Qualitatively assessed upper/lower lung ratios on O2-enhanced MRI, MDCT, and SPECT/CT were estimated using visual scoring systems. To evaluate the correlation for individual upper/lower lung ratios and clinical outcomes, all upper/lower lung ratios were correlated with clinical outcomes. Improvements in mean relative enhancement ratio were directly correlated with clinical outcomes to assess the capability of O2-enhanced MRI to assess therapeutic effect.All quantitatively (-0.63≤r≤-0.47, p0.05) and qualitatively (0.41≤r≤0.57, p0.05) assessed upper/lower lung ratios showed moderate and statistically significant correlation with clinical outcomes, and improvement in mean relative enhancement ratio showed moderate or good correlation, both statistically significant (-0.44≤r≤0.71, p0.05).O2-enhanced MRI shows potential for more accurate evaluation of postoperative clinical outcome for LVRS candidates than SPECT/CT and can be considered at least as reliable as MDCT.

Purpose: To compare therapeutic effect assessment capability of multidetector-row computed tomography (MDCT), magnetic resonance angiography (MRA), and dynamic perfusion MRI for chronic thromboembolic pulmonary hypertension (CTEPH) patients. Materials and Methods: Twenty-four consecutive CTEPH patients treated with conventional therapy underwent pre- and posttherapeutic MDCT, MRA, dynamic perfusion MRI, 6-minute walk distance (6-MWD), cardiac ultrasound (US), and right heart catheterization. According to therapeutic results, all patients were divided into response (n = 13) and nonresponse (n = 11) groups. CTEPH indexes for MDCT (CTEPHCT) and MRA (CTEPHMRA) were calculated on the basis of embolic burden. Pulmonary perfusion parameter maps were generated from all perfusion MR data, followed by determination of improvements in mean perfusion parameter at regions of interest (ROIs) for each patient. Receiver operating characteristic (ROC)-based positive tests were performed to determine the feasible threshold values for distinguishing two groups. Finally, diagnostic capabilities were compared by means of McNemar's test. Results: When feasible threshold values adapted, specificity (90.9 〈10/11〉%, P < 0.05) and accuracy (95.8 〈23/24〉%, P < 0.05) for improvement in pulmonary blood flow were significantly higher than those for CTEPHCTA (specificity: 36.4 〈4/11〉%, accuracy: 70.8 〈17/24〉%). Conclusion: Dynamic perfusion MRI has better capability for assessment of therapeutic effect on CTEPH patients than does MDCT. J. Magn. Reson. Imaging 2012;36:612–623. © 2012 Wiley Periodicals, Inc.

Pulmonary magnetic resonance (MR) imaging has been put forward as a new research and diagnostic tool mainly to overcome the limitations of computed tomography and nuclear medicine studies. However, pulmonary MR imaging has been difficult to use because of inherently low proton density, a multitude of air-tissue interfaces, which create significant magnetic field distortions and are commonly referred to as susceptibility artifacts; diminishing signal in the lung; and respiratory and/or cardiac motion artifacts. To overcome these drawbacks of pulmonary MR imaging, technical advances made during the last decade in sequencing, scanner and coil, adaptation of parallel imaging techniques, and utilization of contrast media have been reported as being useful for functional and morphologic assessment of various pulmonary diseases including airway diseases. This review article covers (1) pulmonary MR techniques for morphologic and functional assessment of airway diseases, and (2) pulmonary MR imaging for cystic fibrosis, asthma, and chronic obstructive pulmonary disease. Pulmonary MR imaging provides not only morphology-related but also pulmonary function-related information. It has the potential to replace nuclear medicine studies for the identification of regional pulmonary function and may perform a complementary role in airway disease assessment instead of nuclear medicine study. We believe that the findings of further basic studies as well as clinical applications of this new technique will validate the real significance of pulmonary MR imaging for the future of airway disease assessment and its usefulness for diagnostic radiology and pulmonary medicine.

Purpose: The role of early stage functional assessment of muscle blood flow response (MFR) by dynamic muscle blood oxygen level-dependent (BOLD) magnetic resonance (MR) imaging is unknown. We investigated the effect of smoking on vascular function according to MFR derived from dynamic muscle BOLD MR imaging during postocclusive reactive hyperemia in young smokers and nonsmokers. Methods: Sixteen healthy male volunteers (8 smokers, 8 nonsmokers; mean age, 30.4 « 4.6 years) underwent BOLD MR imaging of the left calf muscle. During reactive hyperemia provoked by a cuff-compression technique, we measured muscle BOLD (mB) using a 3-tesla single-shot multi-echo gradient-echo echo-planar imaging sequence. The 2 key mB variables in the reactive hyperemic phase that we studied were times to half hyperemic peak (T1/2peak) and peak (TTP), each measured from cuff deflation. We used the Welch test to assess differences in these between smokers and nonsmokers. Results: T1/2peak and TTP were significantly longer in smokers (P < 0.05) in reactive hyperemia. T1/2peak was 13.8 « 5.4s in smokers and 7.6 « 1.5s in nonsmokers, and TTP was 67.5 « 18.8s in smokers and 45.4« 7.1s in nonsmokers. Conclusion: Dynamic BOLD MR imaging of calf muscle during postocclusive reactive hyperemia demonstrated statistically significant differences in T1/2peak and TTP between young smokers and nonsmokers, indicating th ep resence of early stage smoking-related deterioration in MFR.

Objective The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. Materials and methods A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from −1000 HU to −700 HU. Spearman’s correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was predicted by machine learning and emphysema quantification (LAA% or HEQ). Random Forest was used as a machine learning algorithm, and accuracy of prediction was evaluated by leave-one-patient-out cross validation. The difference in the accuracy was assessed using McNemar’s test. Results The correlation coefficients between emphysema quantification and visual score were as follows: LAA% (−950 HU), 0.567; LAA% (−910 HU), 0.654; LAA% (−875 HU), 0.704; nb0 (−950 HU), 0.552; nb0 (−910 HU), 0.629; nb0 (−875 HU), 0.473; nb1 (−950 HU), 0.149; nb1 (−910 HU), 0.519; and nb1 (−875 HU), 0.716. The accuracy of prediction was as follows: LAA%, 55.7% and HEQ, 66.1%. The difference in accuracy was statistically significant (p = 0.0290). Conclusion LAA% and HEQ at −875 HU showed a stronger correlation with visual score than those at −910 or −950 HU. HEQ was more useful than LAA% for predicting visual score.

To prospectively compare the capabilities of dynamic perfusion area-detector computed tomography (CT), dynamic magnetic resonance (MR) imaging, and positron emission tomography (PET) combined with CT (PET/CT) with use of fluorine 18 fluorodeoxyglucose (FDG) for the diagnosis of solitary pulmonary nodules.The institutional review board approved this study, and written informed consent was obtained from each subject. A total of 198 consecutive patients with 218 nodules prospectively underwent dynamic perfusion area-detector CT, dynamic MR imaging, FDG PET/CT, and microbacterial and/or pathologic examinations. Nodules were classified into three groups: malignant nodules (n = 133) and benign nodules with low (n = 53) or high (n = 32) biologic activity. Total perfusion was determined with dual-input maximum slope models at area-detector CT, maximum and slope of enhancement ratio at MR imaging, and maximum standardized uptake value (SUVmax) at PET/CT. Next, all indexes for malignant and benign nodules were compared with the Tukey honest significant difference test. Then, receiver operating characteristic analysis was performed for each index. Finally, sensitivity, specificity, and accuracy were compared with the McNemar test.All indexes showed significant differences between malignant nodules and benign nodules with low biologic activity (P.0001). The area under the receiver operating characteristic curve for total perfusion was significantly larger than that for other indexes (.0006 ≤ P ≤ .04). The specificity and accuracy of total perfusion were significantly higher than those of maximum relative enhancement ratio (specificity, P.0001; accuracy, P.0001), slope of enhancement ratio (specificity, P.0001; accuracy, P.0001), and SUVmax (specificity, P.0001; accuracy, P.0001).Dynamic perfusion area-detector CT is more specific and accurate than dynamic MR imaging and FDG PET/CT in the diagnosis of solitary pulmonary nodules in routine clinical practice.

Objectives To determine the appropriate parameters and evaluation method for characterizing solitary pulmonary nodules (SPNs) using quantitative parameters of diffusion-weighted imaging (DWI). Methods Thirty-two subjects with 36 SPNs underwent DWI with seven different b values (0, 50, 100, 150, 300, 500, and 1000 s/mm 2 ). Five quantitative parameters were obtained from the region of interest drawn over each SPN: apparent diffusion coefficients (ADCs), true diffusion coefficients (DCs), and perfusion fractions (PFs), and signal–intensity ratios between lesion and spinal cord from DWI ( b values: 1000 [LSR 1000 ] and 500 [LSR 500 )]). All quantitative parameters and the diagnostic capabilities were statistically compared. Results SPNs were diagnosed as follow: malignant ( n = 27) and benign ( n = 9). Parameter comparisons for malignant and benign showed both LSRs differed significantly ( p 500 specificity (88.9% [8/9]) and accuracy (77.8% [28/36]) were significantly higher than ADC, DC, and PF specificity and accuracy ( p 1000 accuracy (72.2% [26/36]) was significantly higher than DC accuracy, and its specificity (88.9% [8/9]) was significantly higher than ADC, DC, and PF specificities ( p Conclusions For quantitative differentiation of SPNs, LSR evaluation was more useful and practical than ADC, DC, and PF, and choice of b values showed little impact for the differentiation.

Rationale and Objectives To evaluate paired inspiratory/expiratory computed tomography (CT; iCT/eCT) and deformable image registration for quantitative and qualitative assessment of airflow limitation in smokers. Materials and Methods Paired iCT/eCT images acquired from 35 smokers (30 men and 5 women) were coregistered and subtraction images (air trapping CT images [aCT]) generated. To evaluate emphysema quantitatively, the percentage of low-attenuation volume (LAV%) on iCT was calculated at −950 HU, as were mean and kurtosis on aCT for quantitative assessment of air trapping. Parametric response maps of emphysema (PRMe) and of functional small airways disease (PRMs) were also obtained. For qualitative evaluation of emphysema, low-attenuation areas on iCT were scored by consensus of two radiologists using Goddard classification. To assess air trapping qualitatively, the degree of air trapping on aCT was scored. For each quantitative and qualitative index, the Spearman rank correlation coefficient for forced expiratory flow in 1 second was calculated, and differences in correlation coefficients were statistically tested. Results The correlation coefficients for the indices were as follows: mean on aCT, 0.800; kurtosis on aCT, −0.726; LAV%, −0.472; PRMe, −0.570; PRMs, −0.565; addition of PRMe and PRMs, −0.653; emphysema score, −0.502; air trapping score, −0.793. The indices showing significant differences were as follows: mean on aCT and addition of PRMe and PRMs ( P = 1.43 × 10 −8 ); air trapping score and emphysema score ( P = .0169). Conclusions Air trapping images yielded more accurate quantitative and qualitative evaluation of airflow limitation than did LAV%, PRMe, PRMs, and Goddard classification.

The purpose of this article is to prospectively and directly compare the capabilities of non-contrast-enhanced MR angiography (MRA), 4D contrast-enhanced MRA, and contrast-enhanced MDCT for assessing pulmonary vasculature in patients with non-small cell lung cancer (NSCLC) before surgical treatment.A total of 77 consecutive patients (41 men and 36 women; mean age, 71 years) with pathologically proven and clinically assessed stage I NSCLC underwent thin-section contrast-enhanced MDCT, non-contrast-enhanced and contrast-enhanced MRA, and surgical treatment. The capability for anomaly assessment of the three methods was independently evaluated by two reviewers using a 5-point visual scoring system, and final assessment for each patient was made by consensus of the two readers. Interobserver agreement for pulmonary arterial and venous assessment was evaluated with the kappa statistic. Then, sensitivity, specificity, and accuracy for the detection of anomalies were directly compared among the three methods by use of the McNemar test.Interobserver agreement for pulmonary artery and vein assessment was substantial or almost perfect (κ=0.72-0.86). For pulmonary arterial and venous variation assessment, there were no significant differences in sensitivity, specificity, and accuracy among non-contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 50%; specificity, 98.5%; accuracy, 93.2%), 4D contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 62.5%; specificity, 100.0%; accuracy, 95.9%), and thin-section contrast-enhanced MDCT (pulmonary arteries: sensitivity, 91.4%; specificity, 89.5%; accuracy, 90.4%; pulmonary veins: sensitivity, 50%; specificity, 100.0%; accuracy, 95.9%) (p0.05).Pulmonary vascular assessment of patients with NSCLC before surgical resection by non-contrast-enhanced MRA can be considered equivalent to that by 4D contrast-enhanced MRA and contrast-enhanced MDCT.

OBJECTIVE. The purpose of this article is to review advanced imaging of pulmonary nodules, including pathologic and pharmacokinetic background, conventional contrast-enhanced CT and MRI assessment, dynamic contrast-enhanced CT and MRI techniques, and dual-source and area-detector CT systems for pulmonary nodule evaluation. CONCLUSION. Clinicians need to understand the underlying principles and pathologic and pharmacokinetic backgrounds of contrast-enhanced CT and MRI to further improve diagnostic performance. With adjustments in image acquisition and postprocessing, contrast-enhanced CT and MRI, especially the dynamic versions, can have enhanced clinical application for pulmonary nodules and expanded clinical relevance for other thoracic diseases.

The aim of this study was to evaluate the utility of the iterative reconstruction (IR) technique for quantitative bronchial assessment during low-dose computed tomography (CT) as a substitute for standard-dose CT in patients with/without chronic obstructive pulmonary disease. Fifty patients (mean age, 69.2; mean % predicted FEV1, 79.4) underwent standard-dose CT (150mAs) and low-dose CT (25mAs). Except for tube current, the imaging parameters were identical for both protocols. Standard-dose CT was reconstructed using filtered back-projection (FBP), and low-dose CT was reconstructed using IR and FBP. For quantitative bronchial assessment, the wall area percentage (WA%) of the sub-segmental bronchi and the airway luminal volume percentage (LV%) from the main bronchus to the peripheral bronchi were acquired in each dataset. The correlation and agreement of WA% and LV% between standard-dose CT and both low-dose CTs were statistically evaluated. WA% and LV% between standard-dose CT and both low-dose CTs were significant correlated (r > 0.77, p

The purpose of this study is to investigate the effect of a novel reconstruction algorithm, adaptive iterative dose reduction using 3D processing, on emphysema quantification by low-dose CT.Twenty-six patients who had undergone standard-dose (150 mAs) and low-dose (25 mAs) CT scans were included in this retrospective study. Emphysema was quantified by several quantitative measures, including emphysema index given by the percentage of lung region with low attenuation (lower than -950 HU), the 15th percentile of lung density, and size distribution of low-attenuation lung regions, on standard-dose CT images reconstructed without adaptive iterative dose reduction using 3D processing and on low-dose CT images reconstructed both without and with adaptive iterative dose reduction using 3D processing. The Bland-Altman analysis was used to assess whether the agreement between emphysema quantifications on low-dose CT and on standard-dose CT was improved by the use of adaptive iterative dose reduction using 3D processing.For the emphysema index, the mean differences between measurements on low-dose CT and on standard-dose CT were 1.98% without and -0.946% with the use of adaptive iterative dose reduction using 3D processing. For 15th percentile of lung density, the mean differences without and with adaptive iterative dose reduction using 3D processing were -6.67 and 1.28 HU, respectively. For the size distribution of low-attenuation lung regions, the ranges of the mean relative differences without and with adaptive iterative dose reduction using 3D processing were 21.4-85.5% and -14.1% to 11.2%, respectively. For 15th percentile of lung density and the size distribution of low-attenuation lung regions, the agreement was thus improved by the use of adaptive iterative dose reduction using 3D processing.The use of adaptive iterative dose reduction using 3D processing resulted in greater consistency of emphysema quantification by low-dose CT, with quantification by standard-dose CT.

To prospectively compare the diagnostic capability of short inversion time inversion-recovery (STIR) turbo spin-echo (SE) imaging, diffusion-weighted (DW) magnetic resonance (MR) imaging, and fluorodeoxyglucose (FDG) combined positron emission tomography (PET) and computed tomography (CT) in N stage assessment in patients with non-small cell lung cancer (NSCLC).This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. A total of 250 consecutive patients with NSCLC (136 men; mean age, 73 years; 114 women; mean age, 72 years) prospectively underwent pretherapeutic STIR turbo SE imaging, DW MR imaging, and FDG PET/CT, as well as surgical and pathologic examinations (N0 disease, n = 157; N1 disease, n = 72; N2 disease, n = 16; N3 disease, n = 5). Lymph node-to-saline ratio (LSR), lymph node-to-muscle ratio (LMR), apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV(max)), and visual scoring were assessed for 135 metastatic lymph nodes and 135 randomly selected nonmetastatic lymph nodes. Receiver operating characteristic curve analysis was used to determine feasible threshold values. Diagnostic capabilities for N stage assessment were compared with the McNemar test on a per-patient basis.When feasible, threshold values were used for quantitative assessment; sensitivity and accuracy of LSR and LMR (sensitivity, 82.8%; accuracy, 86.8%) proved to be significantly higher than those of ADC (sensitivity: 74.2%, P = .01; accuracy: 84.4%, P = .04) and SUV(max) (sensitivity: 74.2%, P = .01). For qualitative assessment, sensitivity of STIR turbo SE imaging (77.4%) was significantly higher than that of DW MR imaging (71.0%, P = .03) and FDG PET/CT (69.9%, P = .02).Quantitative and qualitative assessments of N stage disease in patients with NSCLC obtained with STIR turbo SE MR imaging are more sensitive and/or more accurate than those obtained with DW MR imaging and FDG PET/CT.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110281/-/DC1.

To determine the capability of quantitative bronchial luminal volume to assess pulmonary function loss and disease severity in pulmonary emphysema patients.Thirty-seven smokers (mean age, 68.1 years) underwent CT examinations and pulmonary function tests. For the quantitative assessment, luminal voxels of trachea and bronchi were computationally counted and the ratio of the following luminal voxels to all luminal voxels was obtained: (1) the lobe bronchi and the peripheral bronchi (Ratio(lobe)), and (2) the main bronchi and the peripheral bronchi (Ratio(main)). To determine the capability of these assessments to predict pulmonary function loss, these ratios were correlated with pulmonary function tests. To determine the capability for predicting disease severity, these ratios were compared between clinical groups.These ratios were no significant correlated with vital capacity and forced vital capacity (FVC) (p0.05), however significantly correlated with forced expiratory volume in 1s (FEV1) (Ratio(lobe): r = 0.61, p0.0001, Ratio(main): r = 0.58, p0.0005) and FEV1/FVC (Ratio(lobe): r = 0.36, p0.05, Ratio(main): r = 0.33, p0.05). The Ratio(lobe) of smokers without COPD was significantly different from those of moderate COPD and severe or very severe COPD (p0.05), while that of mild COPD was significantly different from that of severe or very severe COPD (p0.01). The Ratio(main) of severe or very severe COPD patients was significantly different from those of other groups (p0.05).Quantitative bronchial luminal volumes were reflected the airflow limitation parameters and was corresponded to clinical groups in emphysema patients.

To compare the capability of differentiation of small-cell lung cancer (SCLC) from non-SCLC (NSCLC) between diffusion-weighted imaging (DWI) and short tau inversion recovery (STIR) turbo spin-echo imaging.The institutional review board of Kobe University Hospital, Kobe, Japan, approved this study, and written informed consent was obtained from each patient. 49 patients with NSCLC (30 males and 19 females; mean age, 66.8 years) and 7 patients with SCLC (5 males and 2 females; mean age, 68.6 years) enrolled and underwent DWI and STIR. To quantitatively differentiate SCLC from NSCLC, apparent diffusion coefficient (ADC) values on DWI and contrast ratios (CRs) between cancer and muscle on STIR were evaluated. ADC values and CRs were then compared between the two cell types by Mann-Whitney's U-tests, and the diagnostic performances were compared by McNemar's test.There were significant differences of mean ADC values (p0.001) and mean CRs (p = 0.003). With adopted threshold values, the specificity (85.7%) and accuracy (85.7%) of DWI were higher than those of STIR (specificity, 63.3%; p = 0.001 and accuracy, 66.1%; p = 0.001). In addition, the accuracy of combination of both indexes (94.6%; p = 0.04) could significantly improve as compared with DWI alone.DWI is more useful for the differentiation of SCLC from NSCLC than STIR, and their combination can significantly improve the accuracy in this setting.Pulmonary MRI, including DWI and STIR, had a potential of the suggestion of the possibility as SCLC.