Citations

AbstractPurposeConventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep‐learning‐based model observers (DL‐MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL‐MO for a low‐contrast hepatic metastases localization task.MethodsWe adapted our recently developed DL‐MO framework for the liver metastases localization task. Our previously‐validated projection‐domain lesion‐/noise‐insertion techniques were used to synthesize realistic positive and low‐dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL‐MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub‐specialized radiologists in an observer study. The performance of DL‐MO and radiologists was gauged by the area under localization receiver‐operating‐characteristic curves. The generalization performance of the DL‐MO was estimated with the repeated twofold cross‐validation method over the same set of trials used in the human observer study. A multi‐slice Channelized Hoteling Observers (CHO) was compared with the DL‐MO across the same experimental conditions.ResultsThe performance of DL‐MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL‐MO was comparable to that of the grouped radiologists, that is, the mean performance difference was ‐3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (‐29.5%) was statistically significant.ConclusionThe presented study demonstrated the potential of using the DL‐MO for image quality assessment in patient abdominal CT tasks.

Significance Statement This study demonstrates for the first time a role of ferroptosis in ADPKD. We show the Pkd1 mutation makes renal epithelial cells prone to ferroptosis through the dysregulation of iron and lipid metabolism. It also suggests the main form of regulated cell death in ADPKD kidneys is ferroptotic but not apoptotic, which helps clarify the controversy over the role of apoptosis in ADPKD. In addition, we found that induction of ferroptosis by erastin promotes cyst growth in Pkd1RC/RC mice, whereas inhibition of ferroptosis by Fer-1 delays cyst growth in rapidly and slowly progressive ADPKD mouse models. These observations suggest management of ferroptosis may be a novel strategy for the treatment of this disease. Background Autosomal dominant polycystic kidney disease (ADPKD), the most common inherited kidney disease, is regulated by different forms of cell death, including apoptosis and autophagy. However, the role in ADPKD of ferroptosis, a recently discovered form of cell death mediated by iron and lipid metabolism, remains elusive. Methods To determine a pathophysiologic role of ferroptosis in ADPKD, we investigated whether the absence of Pkd1 (encoding polycystin-1) affected the expression of key factors involved in the process of ferroptosis, using Western blot and qRT-PCR analysis in Pkd1 mutant renal cells and tissues. We also examined whether treatment with erastin, a ferroptosis inducer, and ferrostain-1, a ferroptosis inhibitor, affected cyst growth in Pkd1 mutant mouse models. Results We found that kidney cells and tissues lacking Pkd1 exhibit extensive metabolic abnormalities, including reduced expression of the system Xc − amino acid antiporter (critical for import of cystine), of iron exporter (ferroportin), and of GPX4 (a key and negative regulator of ferroptosis). The abnormalities also include increased expression of iron importers (TfR1, DMT1) and HO-1, which in turn result in high iron levels, low GSH and GPX4 activity, increased lipid peroxidation, and propensity to ferroptosis. We further found that erastin increased, and ferrostatin-1 inhibited ferroptotic cell death and proliferation of Pkd1 -deficient cells in kidneys from Pkd1 mutant mice. A lipid peroxidation product increased in Pkd1 -deficient cells, 4HNE, promoted the proliferation of survived Pkd1 mutant cells via activation of Akt, S6, Stat3, and Rb during the ferroptotic process, contributing to cyst growth. Conclusion These findings indicate that ferroptosis contributes to ADPKD progression and management of ferroptosis may be a novel strategy for ADPKD treatment.

Background: Several studies have evaluated preprocedural imaging predictors of left ventricular outflow tract obstruction (LVOTO) after transcatheter mitral valve replacement. The patient cohorts in these studies were heterogeneous and included patients with transcatheter mitral valve replacement in failed bioprostheses, annuloplasty rings, and severe mitral annular calcification (MAC). The goal of this study was to evaluate predictors of LVOTO specific to patients undergoing valve-in-MAC. Methods: This study included patients with severe MAC who underwent valve-in-MAC and had optimal quality preprocedural multidetector row computed tomography scans eligible for retrospective analysis. Baseline demographic, echocardiographic, and procedural data on these patients were collected. multidetector row computed tomography parameters were analyzed for association with LVOTO, defined as increase in mean LVOT gradient by ≥10 mm Hg with accompanying hemodynamic instability. Results: Seventy-one patients with optimal preprocedural computed tomography scans were included in this study (mean age, 72.5±13.5 years), 9 of which developed LVOTO (all female). Baseline mean LVOT area, neo-LVOT area (145.3 versus 270.9 mm 2 ; P =0.006), indexed neo-LVOT area (90.1 versus 157.4; P =0.05), and virtual transcatheter heart valve to septum distance (3.1 versus 6.9 mm; P =0.002) were lower in the LVOTO group. Expected % LVOT area reduction was higher in the latter group (58.3 versus 42.7%; P =0.008). In the univariable analysis, the baseline mean LVOT area, neo-LVOT area, indexed neo-LVOT area, and valve to septum distance were all significantly associated with LVOTO. Conclusions: The systolic mean LVOT area, neo-LVOT area, indexed neo-LVOT, expected percentage LVOT area reduction, and the valve to septum distance were associated with LVOTO after valve-in-MAC.

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

Background: Meniscal allograft transplant (MAT) is an important treatment option for young patients with deficient menisci; however, there is a lack of consensus on the optimal method of allograft fixation. Hypothesis: The various methods of MAT fixation have measurable and significant differences in outcomes. Study Design: Meta-analysis; Level of evidence, 4. Methods: A single-arm meta-analysis of studies reporting graft failure, reoperations, and other clinical outcomes after MAT was performed. Studies were stratified by suture-only, bone plug, and bone bridge fixation methods. Proportionate rates of failure and reoperation for each fixation technique were pooled with a mixed-effects model, after which reconstruction of relative risks with confidence intervals was performed using the Katz logarithmic method. Results: A total of 2604 patients underwent MAT. Weighted mean follow-up was 4.3 years (95% CI, 3.2-5.6 years). During this follow-up period, graft failure rates were 6.2% (95% CI, 3.2%-11.6%) for bone plug fixation, 6.9% (95% CI, 4.5%-10.3%) for suture-only fixation, and 9.3% (95% CI, 6.2%-13.9%) for bone bridge fixation. Transplanted menisci secured using bone plugs displayed a lower risk of failure compared with menisci secured via bone bridges (RR = 0.97; 95% CI, 0.94-0.99; P = .02). Risks of failure were not significantly different when comparing suture fixation to bone bridge (RR = 1.02; 95% CI, 0.99-1.06; P = .12) and bone plugs (RR = 0.99; 95% CI, 0.96-1.02; P = .64). Allografts secured using bone plugs were at a lower risk of requiring reoperations compared with those secured using sutures (RR = 0.91; 95% CI, 0.87-0.95; P < .001), whereas allografts secured using bone bridges had a higher risk of reoperation when compared with those secured using either sutures (RR = 1.28; 95% CI, 1.19-1.38; P < .001) or bone plugs (RR = 1.41; 95% CI, 1.32-1.51; P < .001). Improvements in Lysholm and International Knee Documentation Committee scores were comparable among the different groups. Conclusion: This meta-analysis demonstrates that bone plug fixation of transplanted meniscal allografts carries a lower risk of failure than the bone bridge method and has a lower risk of requiring subsequent operations than both suture-only and bone bridge methods of fixation. This suggests that the technique used in the fixation of a transplanted meniscal allograft is an important factor in the clinical outcomes of patients receiving MATs.

Objectives: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. Material and Methods: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. Results: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). Conclusion: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

AbstractPurposeEye‐tracking approaches have been used to understand the visual search process in radiology. However, previous eye‐tracking work in computer tomography (CT) has been limited largely to single cross‐sectional images or video playback of the reconstructed volume, which do not accurately reflect radiologists' visual search activities and their interactivity with three‐dimensional image data at a computer workstation (e.g., scroll, pan, and zoom) for visual evaluation of diagnostic imaging targets. We have developed a platform that integrates eye‐tracking hardware with in‐house‐developed reader workstation software to allow monitoring of the visual search process and reader‐image interactions in clinically relevant reader tasks. The purpose of this work is to validate the spatial accuracy of eye‐tracking data using this platform for different eye‐tracking data acquisition modes.MethodsAn eye‐tracker was integrated with a previously developed workstation designed for reader performance studies. The integrated system captured real‐time eye movement and workstation events at 1000 Hz sampling frequency. The eye‐tracker was operated either in head‐stabilized mode or in free‐movement mode. In head‐stabilized mode, the reader positioned their head on a manufacturer‐provided chinrest. In free‐movement mode, a biofeedback tool emitted an audio cue when the head position was outside the data collection range (general biofeedback) or outside a narrower range of positions near the calibration position (strict biofeedback). Four radiologists and one resident were invited to participate in three studies to determine eye‐tracking spatial accuracy under three constraint conditions: head‐stabilized mode (i.e., with use of a chin rest), free movement with general biofeedback, and free movement with strict biofeedback. Study 1 evaluated the impact of head stabilization versus general or strict biofeedback using a cross‐hair target prior to the integration of the eye‐tracker with the image viewing workstation. In Study 2, after integration of the eye‐tracker and reader workstation, readers were asked to fixate on targets that were randomly distributed within a volumetric digital phantom. In Study 3, readers used the integrated system to scroll through volumetric patient CT angiographic images while fixating on the centerline of designated blood vessels (from the left coronary artery to dorsalis pedis artery). Spatial accuracy was quantified as the offset between the center of the intended target and the detected fixation using units of image pixels and the degree of visual angle.ResultsThe three head position constraint conditions yielded comparable accuracy in the studies using digital phantoms. For Study 1 involving the digital crosshairs, the median ± the standard deviation of offset values among readers were 15.2 ± 7.0 image pixels with the chinrest, 14.2 ± 3.6 image pixels with strict biofeedback, and 19.1 ± 6.5 image pixels with general biofeedback. For Study 2 using the random dot phantom, the median ± standard deviation offset values were 16.7 ± 28.8 pixels with use of a chinrest, 16.5 ± 24.6 pixels using strict biofeedback, and 18.0 ± 22.4 pixels using general biofeedback, which translated to a visual angle of about 0.8° for all three conditions. We found no obvious association between eye‐tracking accuracy and target size or view time. In Study 3 viewing patient images, use of the chinrest and strict biofeedback demonstrated comparable accuracy, while the use of general biofeedback demonstrated a slightly worse accuracy. The median ± standard deviation of offset values were 14.8 ± 11.4 pixels with use of a chinrest, 21.0 ± 16.2 pixels using strict biofeedback, and 29.7 ± 20.9 image pixels using general biofeedback. These corresponded to visual angles ranging from 0.7° to 1.3°.ConclusionsAn integrated eye‐tracker system to assess reader eye movement and interactive viewing in relation to imaging targets demonstrated reasonable spatial accuracy for assessment of visual fixation. The head‐free movement condition with audio biofeedback performed similarly to head‐stabilized mode.

AbstractObjectiveIn X‐raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisition speed. However, on a typical single‐source helical CT (SSCT) system, the helical pitch p typically cannot exceed 1.5; otherwise, reconstruction artifacts will result from data insufficiency. The purpose of this work is to develop a deep convolutional neural network (CNN) to correct for artifacts caused by an ultra‐fast pitch, which can enable faster acquisition speed than what is currently achievable.MethodsA customized CNN (denoted as ultra‐fast‐pitch network (UFP‐net)) was developed to restore the underlying anatomical structure from the artifact‐corrupted post‐reconstruction data acquired from SSCT with ultra‐fast pitch (i.e., p ≥ 2). UFP‐net employed residual learning to capture the features of image artifacts. UFP‐net further deployed in‐house‐customized functional blocks with spatial‐domain local operators and frequency‐domain non‐local operators, to explore multi‐scale feature representation. Images of contrast‐enhanced patient exams (n = 83) with routine pitch setting (i.e., p < 1) were retrospectively collected, which were used as training and testing datasets. This patient cohort involved CT exams over different scan ranges of anatomy (chest, abdomen, and pelvis) and CT systems (Siemens Definition, Definition Flash, Definition AS+, Siemens Healthcare, Inc.), and the corresponding base CT scanning protocols used consistent settings of major scan parameters (e.g., collimation and pitch). Forward projection of the original images was calculated to synthesize helical CT scans with one regular pitch setting (p = 1) and two ultra‐fast‐pitch setting (p = 2 and 3). All patient images were reconstructed using the standard filtered‐back‐projection (FBP) algorithm. A customized multi‐stage training scheme was developed to incrementally optimize the parameters of UFP‐net, using ultra‐fast‐pitch images as network inputs and regular pitch images as labels. Visual inspection was conducted to evaluate image quality. Structural similarity index (SSIM) and relative root‐mean‐square error (rRMSE) were used as quantitative quality metrics.ResultsThe UFP‐net dramatically improved image quality over standard FBP at both ultra‐fast‐pitch settings. At p = 2, UFP‐net yielded higher mean SSIM (> 0.98) with lower mean rRMSE (< 2.9%), compared to FBP (mean SSIM < 0.93; mean rRMSE > 9.1%). Quantitative metrics at p = 3: UFP‐net—mean SSIM [0.86, 0.94] and mean rRMSE [5.0%, 8.2%]; FBP—mean SSIM [0.36, 0.61] and mean rRMSE [36.0%, 58.6%].ConclusionThe proposed UFP‐net has the potential to enable ultra‐fast data acquisition in clinical CT without sacrificing image quality. This method has demonstrated reasonable generalizability over different body parts when the corresponding CT exams involved consistent base scan parameters.