Personalized Chest Computed Tomography

Abstract

Objectives The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs). Materials and Methods A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ). Results The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant ( P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups ( P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols. Conclusions Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.

Hip Imaging in Children With Cerebral Palsy

“…Recently, LDCT techniques have been successfully applied to many pediatric and adult populations for various clinical indications 36–42 . The newest CT dose reduction technology is based on photon counting, which has been gaining popularity and is being introduced into the clinical arena by simultaneously touting the additional advantages of high spatial resolution and high contrast-to-noise ratio 43–47 .…”

“…Recently, LDCT techniques have been successfully applied to many pediatric and adult populations for various clinical indications. [36][37][38][39][40][41][42] The newest CT dose reduction technology is based on photon counting, which has been gaining popularity and is being introduced into the clinical arena by simultaneously touting the additional advantages of high spatial resolution and high contrast-to-noise ratio. [43][44][45][46][47] Thus, a potential future line of inquiry would be to investigate the use of photon counting CT for imaging hip surveillance in children with CP, with the hopes of further lowering the radiation exposure to a level comparable to radiography while providing important 3D anatomical details of the hips for diagnosis and presurgical planning.…”

Ultra-High-Resolution Photon-Counting CT Imaging of the Chest

“…This could be illustrated in the field of ILDs whose depiction, pretherapeutic evaluation, and follow-up are situations often associated with high radiation dose examinations. In a study investigating personalized chest CT tailored to the clinical situation, May et al 33 showed that the evaluation of fibrotic changes and other subtle interstitial lesions was reserved for HRCT protocols, with >2 mSv effective radiation dose when obtained with a modern single-source CT scanner. Using the chest CT categories recently described by Taekker et al 34 comprises ultra-low-dose (ie, 0.22 ± 0.05 mSv), low-dose (ie, 1.22 ± 0.34 mSv), and standard dose (ie, 3.17 ± 1.47 mSv), an adequate HRCT evaluation of ILDs is currently obtained with a “standard” radiation dose.…”

Deep Learning–Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality

“…Because cigarette smoking is a common risk factor for both lung cancer and COPD, evaluation of pulmonary emphysema in screening LDCT may enhance the value of lung cancer screening with LDCT. However, image noise in screening LDCTs may hamper the quantitative evaluation of pulmonary emphysema, especially because they are commonly reconstructed using high-frequency algorithms to assess small lung nodules 11–13 …”

“…However, image noise in screening LDCTs may hamper the quantitative evaluation of pulmonary emphysema, especially because they are commonly reconstructed using high-frequency algorithms to assess small lung nodules. [11][12][13] The quantification of emphysema is vulnerable to variations in CT scanner, radiation dose, slice thickness, and reconstruction filter, making it a critical issue, especially for retrospective multicenter studies, as it is impossible to standardize scan parameters. To address this issue, Gallardo-Estrella et al 7 proposed kernel normalization for emphysema quantification, using 183 SDCTs (120 kVp tube energy, 200 mAs effective dose) with 5 different scanners.…”