Inoue, Akitoshi

Abstract Objectives To develop a volume of interest (VOI) imaging technique in multi-detector-row helical CT to reduce radiation dose or improve image quality within the VOI. Methods A deep-learning method based on a residual U-Net architecture, named VOI-Net, was developed to correct truncation artifacts in VOI helical CT. Three patient cases, a chest CT of interstitial lung disease and 2 abdominopelvic CT of liver tumour, were used for evaluation through simulation. Results VOI-Net effectively corrected truncation artifacts (root mean square error [RMSE] of 5.97 ± 2.98 Hounsfield Units [HU] for chest, 3.12 ± 1.93 HU, and 3.71 ± 1.87 HU for liver). Radiation dose was reduced by 71% without sacrificing image quality within a 10-cm diameter VOI, compared to a full scan field of view (FOV) of 50 cm. With the same total energy deposited as in a full FOV scan, image quality within the VOI matched that at 350% higher radiation dose. A radiologist confirmed improved lesion conspicuity and visibility of small linear reticulations associated with ground-glass opacity and liver tumour. Conclusions Focusing radiation on the VOI and using VOI-Net in a helical scan, total radiation can be reduced or higher image quality equivalent to those at higher doses in standard full FOV scan can be achieved within the VOI. Advances in knowledge A targeted helical VOI imaging technique enabled by a deep-learning-based artifact correction method improves image quality within the VOI without increasing radiation dose.

Background SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. Purpose To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. Material and Methods In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0–100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: −0.10). Results ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=−0.051–0.057]) and fractures (0.048 [95% CI=−0.050–0.145]) but not for intracranial findings alone (−0.024 [95% CI=−0.100–0.052]). Conclusion Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

Abstract Objective. To develop a convolutional neural network (CNN) noise reduction technique for ultra-high-resolution photon-counting detector computed tomography (UHR-PCD-CT) that can be efficiently implemented using only clinically available reconstructed images. The developed technique was demonstrated for skeletal survey, lung screening, and head angiography (CTA). Approach . There were 39 participants enrolled in this study, each received a UHR-PCD and an energy integrating detector (EID) CT scan. The developed CNN noise reduction technique uses image-based noise insertion and UHR-PCD-CT images to train a U-Net via supervised learning. For each application, 13 patient scans were reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) and allocated into training, validation, and testing datasets (9:1:3). The subtraction of FBP and IR images resulted in approximately noise-only images. The 5-slice average of IR produced a thick reference image. The CNN training input consisted of thick reference images with reinsertion of spatially decoupled noise-only images. The training target consisted of the corresponding thick reference images without noise insertion. Performance was evaluated based on difference images, line profiles, noise measurements, nonlinear perturbation assessment, and radiologist visual assessment. UHR-PCD-CT images were compared with EID images (clinical standard). Main results. Up to 89% noise reduction was achieved using the proposed CNN. Nonlinear perturbation assessment indicated reasonable retention of 1 mm radius and 1000 HU contrast signals (>80% for skeletal survey and head CTA, >50% for lung screening). A contour plot indicated reduced retention for small-radius and low contrast perturbations. Radiologists preferred CNN over IR for UHR-PCD-CT noise reduction. Additionally, UHR-PCD-CT with CNN was preferred over standard resolution EID-CT images. Significance. CT images reconstructed with very sharp kernels and/or thin sections suffer from increased image noise. Deep learning noise reduction can be used to offset noise level and increase utility of UHR-PCD-CT images.