Diagnostic accuracy of low-mA chest CT reconstructed with Model Based Iterative Reconstruction in the detection of early pleuro-pulmonary complications following a lung transplantation.

OBJECTIVES: To assess the accuracy of reduced-dose, low-mA chest CT (RD-CT) reconstructed with model-based iterative reconstruction (MBIR) in detecting usual early complications following pulmonary transplantation, as compared to standard-dose chest CT (SD-CT) reconstructed with adaptative statistical iterative reconstruction (ASIR). METHODS: Our institutional review board approved this prospective study and patients provided written informed consent. Two thoracic radiologists independently evaluated 47 examinations performed routinely in 20 patients during 6 months following lung transplantation for the detection and/or evolution of usual pleuropulmonary complications and for subjective image quality. Each examination consisted of successive acquisition of unenhanced SD-CT (100-120 kV, noise index 45, ASIR) and RD-CT (100 kV, 16-24mAs/slice, MBIR). RESULTS: Mean CTDIvol was 4.12 ± 0.88 and 0.65 ± 0.09 mGy for SD-CT and RD-CT, respectively. Complications were found in 40/47 (85 %) examinations. Sensitivity and negative predictive value of RD-CT were 92-100 % for the detection of pneumonia, fungal infection, pleural effusion, pneumothorax, and bronchial dehiscence or stenosis, as compared to SD-CT. Image quality of RD-CT was graded good for 81 % of examinations. CONCLUSIONS: MBIR-RD-CT is accurate, as compared to SD-CT, for delineating most usual pleuropulmonary complications during the 6 months following pulmonary transplantation and might be used routinely for the early monitoring of pulmonary allografts. KEY POINTS: • Early chest complications are frequent following a pulmonary transplantation • CT has a key role for their detection and follow-up • Low-mAMBIR CT is accurate for monitoring most lung allograft early pleuropulmonary complications • MBIR chest CT allows a six-fold dose reduction compared to standard CT.

Model-based iterative reconstruction in pediatric chest CT: assessment of image quality in a prospective study of children with cystic fibrosis.

BACKGROUND: The potential effects of ionizing radiation are of particular concern in children. The model-based iterative reconstruction VEO(TM) is a technique commercialized to improve image quality and reduce noise compared with the filtered back-projection (FBP) method. OBJECTIVE: To evaluate the potential of VEO(TM) on diagnostic image quality and dose reduction in pediatric chest CT examinations. MATERIALS AND METHODS: Twenty children (mean 11.4 years) with cystic fibrosis underwent either a standard CT or a moderately reduced-dose CT plus a minimum-dose CT performed at 100 kVp. Reduced-dose CT examinations consisted of two consecutive acquisitions: one moderately reduced-dose CT with increased noise index (NI = 70) and one minimum-dose CT at CTDIvol 0.14 mGy. Standard CTs were reconstructed using the FBP method while low-dose CTs were reconstructed using FBP and VEO. Two senior radiologists evaluated diagnostic image quality independently by scoring anatomical structures using a four-point scale (1 = excellent, 2 = clear, 3 = diminished, 4 = non-diagnostic). Standard deviation (SD) and signal-to-noise ratio (SNR) were also computed. RESULTS: At moderately reduced doses, VEO images had significantly lower SD (P < 0.001) and higher SNR (P < 0.05) in comparison to filtered back-projection images. Further improvements were obtained at minimum-dose CT. The best diagnostic image quality was obtained with VEO at minimum-dose CT for the small structures (subpleural vessels and lung fissures) (P < 0.001). The potential for dose reduction was dependent on the diagnostic task because of the modification of the image texture produced by this reconstruction. CONCLUSIONS: At minimum-dose CT, VEO enables important dose reduction depending on the clinical indication and makes visible certain small structures that were not perceptible with filtered back-projection.

Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays.

PURPOSE: To appraise the performances of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation. METHODS: We retrospectively collected all radiographic examinations with the associated radiologists' reports performed after a traumatic injury of the limbs and pelvis during 3 consecutive months (January to March 2017) in a private imaging group of 14 centers. Each examination was analyzed by an AI (BoneView, Gleamer) and its results were compared to those of the radiologists' reports. In case of discrepancy, the examination was reviewed by a senior skeletal radiologist to settle on the presence of fractures, dislocations, elbow effusions, and focal bone lesions (FBL). The lesion-wise sensitivity of the AI and the radiologists' reports was compared for each lesion type. This study received IRB approval (CRM-2106-177). RESULTS: A total of 4774 exams were included in the study. Lesion-wise sensitivity was 73.7% for the radiologists' reports vs. 98.1% for the AI (+24.4 points) for fracture detection, 63.3% vs. 89.9% (+26.6 points) for dislocation detection, 84.7% vs. 91.5% (+6.8 points) for elbow effusion detection, and 16.1% vs. 98.1% (+82 points) for FBL detection. The specificity of the radiologists' reports was always 100% whereas AI specificity was 88%, 99.1%, 99.8%, 95.6% for fractures, dislocations, elbow effusions, and FBL respectively. The NPV was measured at 99.5% for fractures, 99.8% for dislocations, and 99.9% for elbow effusions and FBL. CONCLUSION: AI has the potential to prevent diagnosis errors by detecting lesions that were initially missed in the radiologists' reports.