Deep Learning Body Region Classification of MRI and CT Examinations.

This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed tomography (CT) axial images across diverse acquisition protocols and modality manufacturers. Pixel-based analysis of anatomy contained in image sets can provide accurate anatomic labeling. For this purpose, a convolutional neural network (CNN)-based classifier was developed to identify body regions in CT and MRI studies. Seventeen CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective datasets were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test datasets originated from a different healthcare network than the train and validation datasets. Sensitivity and specificity of the classifier was evaluated for patient age, patient sex, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2891 anonymized CT cases (training, 1804 studies; validation, 602 studies; test, 485 studies) and 3339 anonymized MRI cases (training, 1911 studies; validation, 636 studies; test, 792 studies). Twenty-seven institutions from primary care hospitals, community hospitals, and imaging centers contributed to the test datasets. The data included cases of all sexes in equal proportions and subjects aged from 18 years old to + 90 years old. Image-level weighted sensitivity of 92.5% (92.1-92.8) for CT and 92.3% (92.0-92.5) for MRI and weighted specificity of 99.4% (99.4-99.5) for CT and 99.2% (99.1-99.2) for MRI were achieved. Deep learning models can classify CT and MR images by body region including lower and upper extremities with high accuracy.

Patterns of Emphysema Heterogeneity.

BACKGROUND: Although lobar patterns of emphysema heterogeneity are indicative of optimal target sites for lung volume reduction (LVR) strategies, the presence of segmental, or sublobar, heterogeneity is often underappreciated. OBJECTIVE: The aim of this study was to understand lobar and segmental patterns of emphysema heterogeneity, which may more precisely indicate optimal target sites for LVR procedures. METHODS: Patterns of emphysema heterogeneity were evaluated in a representative cohort of 150 severe (GOLD stage III/IV) chronic obstructive pulmonary disease (COPD) patients from the COPDGene study. High-resolution computerized tomography analysis software was used to measure tissue destruction throughout the lungs to compute heterogeneity (≥15% difference in tissue destruction) between (inter-) and within (intra-) lobes for each patient. Emphysema tissue destruction was characterized segmentally to define patterns of heterogeneity. RESULTS: Segmental tissue destruction revealed interlobar heterogeneity in the left lung (57%) and right lung (52%). Intralobar heterogeneity was observed in at least one lobe of all patients. No patient presented true homogeneity at a segmental level. There was true homogeneity across both lungs in 3% of the cohort when defining heterogeneity as ≥30% difference in tissue destruction. CONCLUSION: Many LVR technologies for treatment of emphysema have focused on interlobar heterogeneity and target an entire lobe per procedure. Our observations suggest that a high proportion of patients with emphysema are affected by interlobar as well as intralobar heterogeneity. These findings prompt the need for a segmental approach to LVR in the majority of patients to treat only the most diseased segments and preserve healthier ones.

Computed tomography predictors of response to endobronchial valve lung reduction treatment. Comparison with Chartis.

RATIONALE: Chartis Pulmonary Assessment System (Pulmonx Inc., Redwood, CA) is an invasive procedure used to assess collateral ventilation and select candidates for valve-based lung volume reduction (LVR) therapy. Quantitative computed tomography (QCT) is a potential alternative to Chartis and today consists primarily of assessing fissure integrity (FI). OBJECTIVES: In this retrospective analysis, we aimed to determine QCT predictors of LVR outcome and compare the QCT model with Chartis in selecting likely responders to valve-based LVR treatment. METHODS: Baseline CT scans of 146 subjects with severe emphysema who underwent endobronchial valve LVR were analyzed retrospectively using dedicated lung quantitative imaging software (Apollo; VIDA Diagnostics, Coralville, IA). A lobar volume reduction greater than 350 ml at 3 months was considered to be indicative of positive response to treatment. Thirty-four CT baseline variables, including quantitative measurements of FI, density, and vessel volumetry, were used to feed a multiple logistic regression analysis to find significant predictors of LVR outcome. The primary predictors were then used in 33 datasets with Chartis results to evaluate the relative performance of QCT versus Chartis. MEASUREMENTS AND MAIN RESULTS: FI (P < 0.0001) and low attenuation clusters (P = 0.01) measured in the treated lobe and vascular volumetric percentage of patient's detected smallest vessels (P = 0.02) were identified as the primary QCT predictors of LVR outcome. Accuracy for QCT patient selection based on these primary predictors was comparable to Chartis (78.8 vs. 75.8%). CONCLUSIONS: Quantitative CT led to comparable results to Chartis for classifying LVR and is a promising tool to effectively select patients for valve-based LVR procedures.

Image quality improvement using an image-based noise reduction algorithm: initial experience in a phantom model for urinary stones.

OBJECTIVE: To determine signal-to-noise (SNR), contrast-to-noise ratio, and segmentation error measurements in various low-dose computed tomographic (CT) acquisitions of an anthropomorphic phantom containing urinary stones before and after implementation of a structure-preserving diffusion (SPD) denoising algorithm, and to compare the measurements with those of standard-dose CT acquisitions. METHODS: After institutional review board approval, written informed consent was waived and 36 calcium oxalate stones were evaluated after CT acquisitions in an anthropomorphic phantom at variable tube currents (33-137 mA s). The SPD denoising algorithm was applied to all images. Signal-to-noise ratio, contrast-to-noise ratio, and expected segmentation error were determined using manually drawn regions of interest to quantify the effect of the noise reduction on the image quality. RESULTS: The value of segmentation error measurements using the SPD denoising algorithm obtained at tube currents as low as 33 mA s (up to 75% dose reduction level) were similar to standard imaging at 137 mA s. The denoised images at reduced doses up to 75% dose reduction have higher SNR than the standard-dose images without denoising (P < 0.005). Stepwise regression showed significant (P < 0.001) effect of dose length product on SNR, and segmentation error measurements. CONCLUSIONS: Based on objective noise-related image quality metrics, the SPD denoising algorithm may be useful as a robust and fast tool, and it has the potential to improve image quality in low-dose CT ureter protocols.

Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time.

OBJECTIVE: The purpose of this article is to assess detection, tracking, and reading time of solid lung nodules > or = 4 mm on pairs of MDCT chest screening examinations using a computer-aided detection (CAD) system. MATERIALS AND METHODS: Of 54 pairs of low-dose MDCT chest examinations (1.25-mm collimation), two chest radiologists in consensus established that 25 examinations contained 52 nodules > or = 4 mm. All paired examinations were interpreted on the CAD workstation--first without and then with CAD input--for the detection and tracking of lung nodules. A subset of 33 examination pairs was later read on the clinical workstation used in daily practice, and the results were compared for reading time with those on the CAD workstation. RESULTS: After CAD input, the sensitivity for nodule detection increased statistically significantly for both readers (9.6% and 23%; p < or = 0.025). One cancer initially missed by one radiologist was correctly identified with CAD input. The overall reading time on the CAD workstation and clinical workstation was comparable for both radiologists. On average, readers spent 4-5 minutes per case to read the paired examinations on the CAD workstation and 6-8 seconds per CAD mark. The CAD system successfully matched 91.3% of nodules detected in both examinations. The overall rate of available CAD growth assessment was 54.9% of all nodule pairs. CONCLUSION: In the context of temporal comparison of MDCT screening examinations, the sensitivity of radiologists for detecting lung nodules > or = 4 mm increased significantly (p < or = 0.025) with CAD input without compromising reading time.

Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.

RATIONALE AND OBJECTIVES: To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size. MATERIALS AND METHODS: A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics. RESULTS: Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors. CONCLUSION: CAD detection performance of solid lung nodules did not suffer until 48:1 compression.