Head-to-head Comparison of Qualitative Radiologist Assessment With Automated Quantitative Computed Tomography Analysis for Bronchiolitis Obliterans Syndrome After Hematopoietic Cell Transplantation

Pulmonary transplant complications: a radiologic review

Lung transplantation has become the definitive treatment for end stage respiratory disease. Numbers and survival rates have increased over the past decade, with transplant recipients living longer and with greater comorbidities, resulting in greater complexity of care. Common and uncommon complications that occur in the immediate, early, intermediate, and late periods can have significant impact on the course of the transplant. Fortunately, advancements in surgery, medical care, and imaging as well as other diagnostics work to prevent, identify, and manage complications that would otherwise have a negative impact on survivability. This review will focus on contextualizing complications both categorically and chronologically, with highlights of specific imaging and clinical features in order to inform both radiologists and clinicians involved in post-transplant care.

Imaging of pediatric hematopoietic stem cell transplant recipients: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper

Imaging in hematopoietic stem cell transplantation patients is not targeted at evaluating the transplant per se. Rather, imaging is largely confined to evaluating peri-procedural and post-procedural complications. Alternatively, imaging may be performed to establish a baseline study for comparison should the patient develop certain post-procedural complications. This article looks to describe the various imaging modalities available with recommendations for which imaging study should be performed in specific complications. We also provide select imaging protocols for different indications and modalities for the purpose of establishing a set minimal standard for imaging in these complex patients.

CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT

PURPOSE

To develop a convolutional neural network (CNN)-based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification.

MATERIALS AND METHODS

In this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model. Agreement between LungReg and SyN air trapping measurements was assessed using intraclass correlation coefficient. The ability of LungReg versus SyN emphysema and air trapping measurements to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages was compared using area under the receiver operating characteristic curves.

RESULTS

Average performance of LungReg versus SyN showed lobar Dice overlap score of 0.91-0.97 versus 0.89-0.95, respectively (P < .001); percentage voxels with nonpositive Jacobian determinant of 0.04 versus 0.10, respectively (P < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central processing unit) versus 418.46 seconds (central processing unit) (P < .001); and LCE of 7.21 mm versus 6.93 mm (P < .001). LungReg and SyN whole-lung and lobar air trapping measurements achieved excellent agreement (intraclass correlation coefficients > 0.98). LungReg versus SyN area under the receiver operating characteristic curves for predicting GOLD stage were not statistically different (range, 0.88-0.95 vs 0.88-0.95, respectively; P = .31-.95).

CONCLUSION

CNN-based deformable lung registration is accurate and fully automated, with runtime feasible for clinical lobar air trapping quantification, and has potential to improve diagnosis of small airway diseases.Keywords: Air Trapping, Convolutional Neural Network, Deformable Registration, Small Airway Disease, CT, Lung, Semisupervised Learning, Unsupervised Learning Supplemental material is available for this article. © RSNA, 2021 An earlier incorrect version of this article appeared online. This article was corrected on December 22, 2021.