Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Multi-institutional Protocol Guidance for Pediatric Photon-counting CT.

Performing CT in children comes with unique challenges such as greater degrees of patient motion, smaller and densely packed anatomy, and potential risks of radiation exposure. The technical advancements of photon-counting detector (PCD) CT enable decreased radiation dose and noise, as well as increased spatial and contrast resolution across all ages, compared with conventional energy-integrating detector CT. It is therefore valuable to review the relevant technical aspects and principles specific to protocol development on the new PCD CT platform to realize the potential benefits for this population. The purpose of this article, based on multi-institutional clinical and research experience from pediatric radiologists and medical physicists, is to provide protocol guidance for use of PCD CT in the imaging of pediatric patients.

When will the immune-stimulating antibody conjugates (ISACs) be transferred from bench to bedside?

Immunostimulatory antibody conjugates (ISACs) as a promising new generation of targeted therapeutic antibody-drug conjugates (ADCs), that not only activate innate immunity but also stimulate adaptive immunity, providing a dual therapeutic effect to eliminate tumor cells. However, several ISACs are still in the early stages of clinical development or have already failed. Therefore, it is crucial to design ISACs more effectively to overcome their limitations, including high toxicity, strong immunogenicity, long development time, and poor pharmacokinetics. This review aims to summarize the composition and function of ISACs, incorporating current design considerations and ongoing clinical trials. Additionally, the review delves into the current issues with ISACs and potential solutions, such as adjusting the drug-antibody ratio (DAR) to improve the bioavailability of ISACs. By leveraging the affinity and bioavailability-enhancing properties of bispecific antibodies, the utility between antibodies and immunostimulatory agents can be balanced. Commonly used immunostimulatory agents may induce systemic immune reactions, and BTK (Bruton's tyrosine kinase) inhibitors can regulate immunogenicity. Finally, the concept of grafting ADC's therapeutic principles is simple, but the combination of payload, linker, and targeted functional molecules is not a simple permutation and combination problem. The development of conjugate drugs faces more complex pharmacological and toxicological issues. Standing on the shoulders of ADC, the development and application scenarios of ISAC are endowed with broader space.

Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT.

Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting.

Understanding Reader Variability: A 25-Radiologist Study on Liver Metastasis Detection at CT.

Background Substantial interreader variability exists for common tasks in CT imaging, such as detection of hepatic metastases. This variability can undermine patient care by leading to misdiagnosis. Purpose To determine the impact of interreader variability associated with (a) reader experience, (b) image navigation patterns (eg, eye movements, workstation interactions), and (c) eye gaze time at missed liver metastases on contrast-enhanced abdominal CT images. Materials and Methods In a single-center prospective observational trial at an academic institution between December 2020 and February 2021, readers were recruited to examine 40 contrast-enhanced abdominal CT studies (eight normal, 32 containing 91 liver metastases). Readers circumscribed hepatic metastases and reported confidence. The workstation tracked image navigation and eye movements. Performance was quantified by using the area under the jackknife alternative free-response receiver operator characteristic (JAFROC-1) curve and per-metastasis sensitivity and was associated with reader experience and image navigation variables. Differences in area under JAFROC curve were assessed with the Kruskal-Wallis test followed by the Dunn test, and effects of image navigation were assessed by using the Wilcoxon signed-rank test. Results Twenty-five readers (median age, 38 years; IQR, 31-45 years; 19 men) were recruited and included nine subspecialized abdominal radiologists, five nonabdominal staff radiologists, and 11 senior residents or fellows. Reader experience explained differences in area under the JAFROC curve, with abdominal radiologists demonstrating greater area under the JAFROC curve (mean, 0.77; 95% CI: 0.75, 0.79) than trainees (mean, 0.71; 95% CI: 0.69, 0.73) (P = .02) or nonabdominal subspecialists (mean, 0.69; 95% CI: 0.60, 0.78) (P = .03). Sensitivity was similar within the reader experience groups (P = .96). Image navigation variables that were associated with higher sensitivity included longer interpretation time (P = .003) and greater use of coronal images (P < .001). The eye gaze time was at least 0.5 and 2.0 seconds for 71% (266 of 377) and 40% (149 of 377) of missed metastases, respectively. Conclusion Abdominal radiologists demonstrated better discrimination for the detection of liver metastases on abdominal contrast-enhanced CT images. Missed metastases frequently received at least a brief eye gaze. Higher sensitivity was associated with longer interpretation time and greater use of liver display windows and coronal images. © RSNA, 2022 Online supplemental material is available for this article.

The technical development of photon-counting detector CT.

Since 1971 and Hounsfield's first CT system, clinical CT systems have used scintillating energy-integrating detectors (EIDs) that use a two-step detection process. First, the X-ray energy is converted into visible light, and second, the visible light is converted to electronic signals. An alternative, one-step, direct X-ray conversion process using energy-resolving, photon-counting detectors (PCDs) has been studied in detail and early clinical benefits reported using investigational PCD-CT systems. Subsequently, the first clinical PCD-CT system was commercially introduced in 2021. Relative to EIDs, PCDs offer better spatial resolution, higher contrast-to-noise ratio, elimination of electronic noise, improved dose efficiency, and routine multi-energy imaging. In this review article, we provide a technical introduction to the use of PCDs for CT imaging and describe their benefits, limitations, and potential technical improvements. We discuss different implementations of PCD-CT ranging from small-animal systems to whole-body clinical scanners and summarize the imaging benefits of PCDs reported using preclinical and clinical systems. KEY POINTS: • Energy-resolving, photon-counting-detector CT is an important advance in CT technology. • Relative to current energy-integrating scintillating detectors, energy-resolving, photon-counting-detector CT offers improved spatial resolution, improved contrast-to-noise ratio, elimination of electronic noise, increased radiation and iodine dose efficiency, and simultaneous multi-energy imaging. • High-spatial-resolution, multi-energy imaging using energy-resolving, photon-counting-detector CT has been used in investigations into new imaging approaches, including multi-contrast imaging.

Clinical applications of photon counting detector CT.

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

Seeing More with Less: Clinical Benefits of Photon-counting Detector CT.

Photon-counting detector (PCD) CT is an emerging technology that has led to continued innovation and progress in diagnostic imaging after it was approved by the U.S. Food and Drug Administration for clinical use in September 2021. Conventional energy-integrating detector (EID) CT measures the total energy of x-rays by converting photons to visible light and subsequently using photodiodes to convert visible light to digital signals. In comparison, PCD CT directly records x-ray photons as electric signals, without intermediate conversion to visible light. The benefits of PCD CT systems include improved spatial resolution due to smaller detector pixels, higher iodine image contrast, increased geometric dose efficiency to allow high-resolution imaging, reduced radiation dose for all body parts, multienergy imaging capabilities, and reduced artifacts. To recognize these benefits, diagnostic applications of PCD CT in musculoskeletal, thoracic, neuroradiologic, cardiovascular, and abdominal imaging must be optimized and adapted for specific diagnostic tasks. The diagnostic benefits and clinical applications resulting from PCD CT in early studies have allowed improved visualization of key anatomic structures and radiologist confidence for some diagnostic tasks, which will continue as PCD CT evolves and clinical use and applications grow. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Ananthakrishnan in this issue.

Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.

OBJECTIVE: Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning-based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating the local noise level within each region of a CT image. The local noise level will be referred to as a pixel-wise noise map. METHODS: The SILVER architecture resembled a U-Net convolutional neural network with mean-square-error loss. To generate training data, 100 replicate scans were acquired of 3 anthropomorphic phantoms (chest, head, and pelvis) using a sequential scan mode; 120,000 phantom images were allocated into training, validation, and testing data sets. Pixel-wise noise maps were calculated for the phantom data by taking the per-pixel SD from the 100 replicate scans. For training, the convolutional neural network inputs consisted of phantom CT image patches, and the training targets consisted of the corresponding calculated pixel-wise noise maps. Following training, SILVER noise maps were evaluated using phantom and patient images. For evaluation on patient images, SILVER noise maps were compared with manual noise measurements at the heart, aorta, liver, spleen, and fat. RESULTS: When tested on phantom images, the SILVER noise map prediction closely matched the calculated noise map target (root mean square error <8 Hounsfield units). Within 10 patient examinations, SILVER noise map had an average percent error of 5% relative to manual region-of-interest measurements. CONCLUSION: The SILVER framework enabled accurate pixel-wise noise level estimation directly from patient images. This method is widely accessible because it operates in the image domain and requires only phantom data for training.

Lung Cancer Screening Using Clinical Photon-Counting Detector Computed Tomography and Energy-Integrating-Detector Computed Tomography: A Prospective Patient Study.

OBJECTIVE: To evaluate the diagnostic quality of photon-counting detector (PCD) computed tomography (CT) in patients undergoing lung cancer screening compared with conventional energy-integrating detector (EID) CT in a prospective multireader study. MATERIALS: Patients undergoing lung cancer screening with conventional EID-CT were prospectively enrolled and scanned on a PCD-CT system using similar automatic exposure control settings and reconstruction kernels. Three thoracic radiologists blinded to CT system compared PCD-CT and EID-CT images and scored examinations using a 5-point Likert comparison score (-2 [left image is worse] to +2 [left image is better]) for artifacts, sharpness, image noise, diagnostic image quality, emphysema visualization, and lung nodule evaluation focusing on the border. Post hoc correction of Likert scores was performed such that they reflected PCD-CT performance in comparison to EID-CT. A nonreader radiologist measured objective image noise. RESULTS: Thirty-three patients (mean, 66.9 ± 5.6 years; 11 female; body mass index; 30.1 ± 5.1 kg/m 2 ) were enrolled. Mean volume CT dose index for PCD-CT was lower (0.61 ± 0.21 vs 0.73 ± 0.22; P < 0.001). Pooled reader results showed significant differences between imaging modalities for all comparative rankings ( P < 0.001), with PCD-CT favored for sharpness, image noise, image quality, and emphysema visualization and lung nodule border, but not artifacts. Photon-counting detector CT had significantly lower image noise (74.4 ± 10.5 HU vs 80.1 ± 8.6 HU; P = 0.048). CONCLUSIONS: Photon-counting detector CT with similar acquisition and reconstruction settings demonstrated improved image quality and less noise despite lower radiation dose, with improved ability to depict pulmonary emphysema and lung nodule borders compared with EID-CT at low-dose lung cancer CT screening.

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.

PURPOSE: To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease. METHODS: T2-weighted MRI scans of 804 patients with symptoms of lumbar degenerative disease were retrospectively collected from three hospitals. The training dataset (n = 456) and internal validation dataset (n = 134) were randomly selected from the center I. Two external validation datasets comprising 100 and 114 patients were from center II and center III, respectively. A DL model based on 3D ResNet18 and transformer architecture was proposed to detect lumbar degenerative disease. In addition, a cervical MR image dataset comprising 200 patients from an independent hospital was used to evaluate the generalized performance of the DL model. The diagnostic performance was assessed by the free-response receiver operating characteristic (fROC) curve and precision-recall (PR) curve. Precision, recall, and F1-score were used to measure the DL model. RESULTS: A total of 2497 three-dimension retrogression annotations were labeled for training (n = 1157) and multicenter validation (n = 1340). The DL model showed excellent detection efficiency in the internal validation dataset, with F1-score achieving 0.971 and 0.903 on the sagittal and axial MR images, respectively. Good performance was also observed in the external validation dataset I (F1-score, 0.768 on sagittal MR images and 0.837 on axial MR images) and external validation dataset II (F1-score, 0.787 on sagittal MR images and 0.770 on axial MR images). Furthermore, the robustness of the DL model was demonstrated via transfer learning and generalized performance evaluation on the external cervical dataset, with the F1-score yielding 0.931 and 0.919 on the sagittal and axial MR images, respectively. CONCLUSION: The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.

Pilot study to determine whether reduced-dose photon-counting detector chest computed tomography can reliably display Brody II score imaging findings for children with cystic fibrosis at radiation doses that approximate radiographs.

BACKGROUND: The Brody II score uses chest CT to guide therapeutic changes in children with cystic fibrosis; however, patients and providers are often reticent to undergo chest CT given concerns about radiation. OBJECTIVE: We sought to determine the ability of a reduced-dose photon-counting detector (PCD) chest CT protocol to reproducibly display pulmonary disease severity using the Brody II score for children with cystic fibrosis (CF) scanned at radiation doses similar to those of a chest radiograph. MATERIALS AND METHODS: Pediatric patients with CF underwent non-contrast reduced-dose chest PCD-CT. Volumetric inspiratory and expiratory scans were obtained without sedation or anesthesia. Three pediatric radiologists with Certificates of Added Qualification scored each scan on an ordinal scale and assigned a Brody II score to grade bronchiectasis, peribronchial thickening, parenchymal opacity, air trapping and mucus plugging. We report image-quality metrics using descriptive statistics. To calculate inter-rater agreement for Brody II scoring, we used the Krippendorff alpha and intraclass correlation coefficient (ICC). RESULTS: Fifteen children with CF underwent reduced-dose PCD chest CT in both inspiration and expiration (mean age 8.9 years, range, 2.5-17.5 years; 4 girls). Mean volumetric CT dose index (CTDIvol) was 0.07 ± 0.03 mGy per scan. Mean effective dose was 0.12 ± 0.04 mSv for the total examination. All three readers graded spatial resolution and noise as interpretable on lung windows. The average Brody II score was 12.5 (range 4-19), with moderate inter-reader reliability (ICC of 0.61 [95% CI=0.27, 0.84]). Inter-rater reliability was moderate to substantial for bronchiectasis (0.52), peribronchial thickening (0.55), presence of opacity (0.62) and air trapping (0.70) and poor for mucus plugging (0.09). CONCLUSION: Reduced-dose PCD-CT permits diagnostic image quality and reproducible identification of Brody II scoring imaging findings at radiation doses similar to those for chest radiography.

Establishing a quality assurance program for photon counting detector (PCD) CT: Tips and caveats.

PURPOSE: To determine the suitability of a quality assurance (QA) program based on the American College of Radiology's (ACR) CT quality control (QC) manual to fully evaluate the unique capabilities of a clinical photon-counting-detector (PCD) CT system. METHODS: A daily QA program was established to evaluate CT number accuracy and artifacts for both standard and ultra-high-resolution (UHR) scan modes. A complete system performance evaluation was conducted in accordance with the ACR CT QC manual by scanning the CT Accreditation Phantom with routine clinical protocols and reconstructing low-energy-threshold (T3D) and virtual monoenergetic images (VMIs) between 40 and 120 keV. Spatial resolution was evaluated by computing the modulation transfer function (MTF) for the UHR mode, and multi-energy performance was evaluated by scanning a body phantom containing four iodine inserts with concentrations between 2 and 15 mg I/cc. RESULTS: The daily QA program identified instances when the detector needed recalibration or replacement. CT number accuracy was impacted by image type: CT numbers at 70 keV VMI were within the acceptable range (defined for 120 kV). Other keV VMIs and the T3D reconstruction had at least one insert with CT number outside the acceptable range. The limiting resolution was nearly 40 lp/cm based on MTF measurements, which far exceeds the 12 lp/cm maximum capability of the ACR phantom. The CT numbers in the iodine inserts were accurate on all VMIs (3.8% average percentage error), while the iodine concentrations had an average root mean squared error of 0.3 mg I/cc. CONCLUSION: Protocols and parameters must be properly selected on PCD-CT to meet current accreditation requirements with the ACR CT phantom. Use of the 70 keV VMI allowed passing all tests prescribed in the ACR CT manual. Additional evaluations such an MTF measurement and multi-energy phantom scans are also recommended to comprehensively evaluate PCD-CT scanner performance.

Dependence of Water-equivalent Diameter and Size-specific Dose Estimates on CT Tube Potential.

Background The size-specific dose estimate (SSDE) is a patient-focused CT dose metric. However, published size-dependent conversion factors (fsize) used to calculate SSDE were determined primarily by using phantoms; only eight to 15 patient data sets were used, all at 120 kV. Purpose To determine the effect of different tube potentials on the water-equivalent diameter (WED) and SSDE for patient CT scans of the head, chest, and abdomen. Materials and Methods This retrospective study used 250 noncontrast CT scans acquired between March 2013 and June 2017. Bony structures were segmented, and their CT numbers were modified to reflect bone attenuation at 70, 90, 110, 130, and 150 kV. Soft-tissue CT numbers were unchanged because of negligible energy dependence. fsize was measured in anthropomorphic phantoms for each tube potential and fit to an exponential function. WED and SSDE were determined for each patient at all tube potentials, regression analysis was performed relative to the WED and SSDE at 120 kV, and mean differences relative to 120 kV were calculated. Results In 250 patients (median age, 21.5 years; interquartile range, 44 years; 130 women), WED for all tube potentials was linearly related to the WED at 120 kV in all body regions (R2 = 0.995-1.000). The effect of tube potential on WED was negligible for torso examinations (Cohen d < 0.05). In the head, a medium effect size was observed at 70 kV; however, the mean absolute difference in WED was small (-0.49 cm ± 0.08 [standard deviation]; P < .001). For commonly used combinations of tube potential and patient size, the mean differences in SSDE at alternative tube potentials relative to SSDE at 120 kV were less than 5%. Conclusion At noncontrast CT, published size-dependent conversion factors accurately determined size-specific dose estimates on 250 patient scans at five tube potentials other than 120 kV. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Boone in this issue.

First Clinical Photon-counting Detector CT System: Technical Evaluation.

Background The first clinical CT system to use photon-counting detector (PCD) technology has become available for patient care. Purpose To assess the technical performance of the PCD CT system with use of phantoms and representative participant examinations. Materials and Methods Institutional review board approval and written informed consent from four participants were obtained. Technical performance of a dual-source PCD CT system was measured for standard and high-spatial-resolution (HR) collimations. Noise power spectrum, modulation transfer function, section sensitivity profile, iodine CT number accuracy in virtual monoenergetic images (VMIs), and iodine concentration accuracy were measured. Four participants were enrolled (between May 2021 and August 2021) in this prospective study and scanned using similar or lower radiation doses as their respective clinical examinations performed on the same day using energy-integrating detector (EID) CT. Image quality and findings from the participants' PCD CT and EID CT examinations were compared. Results All standard technical performance measures met accreditation and regulatory requirements. Relative to filtered back-projection reconstructions, images from iterative reconstruction had lower noise magnitude but preserved noise power spectrum shape and peak frequency. Maximum in-plane spatial resolutions of 125 and 208 µm were measured for HR and standard PCD CT scans, respectively. Minimum values for section sensitivity profile full width at half maximum measurements were 0.34 mm (0.2-mm nominal section thickness) and 0.64 mm (0.4-mm nominal section thickness) for HR and standard PCD CT scans, respectively. In a 120-kV standard PCD CT scan of a 40-cm phantom, VMI iodine CT numbers had a mean percentage error of 5.7%, and iodine concentration had root mean squared error of 0.5 mg/cm3, similar to previously reported values for EID CT. VMIs, iodine maps, and virtual noncontrast images were created for a coronary CT angiogram acquired with 66-msec temporal resolution. Participant PCD CT images showed up to 47% lower noise and/or improved spatial resolution compared with EID CT. Conclusion Technical performance of clinical photon-counting detector (PCD) CT is improved relative to that of a current state-of-the-art CT system. The dual-source PCD geometry facilitated 66-msec temporal resolution multienergy cardiac imaging. Study participant images illustrated the effect of the improved technical performance. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Willemink and Grist in this issue.

Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys.

OBJECTIVE: This study evaluated the clinical utility of a phantom-based convolutional neural network noise reduction framework for whole-body-low-dose CT skeletal surveys. MATERIALS AND METHODS: The CT exams of ten patients with multiple myeloma were retrospectively analyzed. Exams were acquired with routine whole-body-low-dose CT protocol and projection noise insertion was used to simulate 25% dose exams. Images were reconstructed with either iterative reconstruction or filtered back projection with convolutional neural network post-processing. Diagnostic quality and structure visualization were blindly rated (subjective scale ranging from 0 [poor] to 100 [excellent]) by three musculoskeletal radiologists for iterative reconstruction and convolutional neural network images at routine whole-body-low-dose and 25% dose CT. RESULTS: For the diagnostic quality rating, the convolutional neural network outscored iterative reconstruction at routine whole-body-low-dose CT (convolutional neural network: 95 ± 5, iterative reconstruction: 85 ± 8) and at the 25% dose level (convolutional neural network: 79 ± 10, iterative reconstruction: 22 ± 13). Convolutional neural network applied to 25% dose was rated inferior to iterative reconstruction applied to routine dose. Similar trends were observed in rating experiments focusing on structure visualization. CONCLUSION: Results indicate that the phantom-based convolutional neural network noise reduction framework can improve visualization of critical structures within CT skeletal surveys. At matched dose level, the convolutional neural network outscored iterative reconstruction for all conditions studied. The image quality improvement of convolutional neural network applied to 25% dose indicates a potential for dose reduction; however, the 75% dose reduction condition studied is not currently recommended for clinical implementation.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques.

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.

Reader Performance as a Function of Patient Size for the Detection of Hepatic Metastases.

OBJECTIVE: To investigate reader performance as a function of patient size for the detection of hepatic metastases when an automatic exposure control (AEC) system is used, which varies image noise as a function of patient size. METHODS: Abdominal computed tomograhy examinations across 100, 120, 160, and 200 quality reference tube current-time product were collected, involving a cohort of 83 patients. Three radiologists identified hepatic metastases across all dose levels. Partial Spearman rank correlation and multivariate logistic regression were used to evaluate correlations between reader performance and patient size and lesion size/contrast while accounting for potential confounding effects. Analyses were repeated on an emulated less-variable noise AEC. RESULTS: No statistically significant correlation was observed between patient size and radiologist performance (for variable-noise AEC: range of partial Spearman ρ, -0.157 to -0.035]; range of adjusted odds ratios, 0.987, 1.006). CONCLUSIONS: Reader performance was independent of patient size, suggesting that variable-noise AEC provides better modulation for larger patients than constant-noise AEC.

Feasibility of Dual-Energy Computed Tomography Imaging of Gadolinium-Based Contrast Agents and Its Application in Computed Tomography Cystography: An Exploratory Study to Assess an Alternative Option When Iodinated Contrast Agents Are Contraindicated.

OBJECTIVE: The aim of this study was to compare the contrast enhancement differences between gadolinium-based and iodine-based contrast agents at different single-energy tube potentials and dual-energy-based virtual monochromatic energies. In addition, we describe the application of a gadolinium-based contrast agent in computed tomography (CT) cystography for a patient with contraindications to iodine. METHODS: A phantom study was performed using 3 iodine samples (concentrations: 5, 10, and 15 mgI/mL) and 3 gadolinium samples (concentrations: 3.3, 6.6, and 9.9 mgGd/mL). The prepared phantom was scanned by a dual-energy CT (DECT) at 80, 100, 120, and 140 kV in the single-energy mode and at 100/Sn140 kV in the dual-energy mode. Virtual monoenergetic images (VMIs) at 50 keV were generated from the DECT scan. In addition, a DECT cystogram was performed using a gadolinium-based contrast agent in a patient with contraindications to iodinated contrast. RESULTS: Strong linear correlations between mean signal of contrast enhancement and mass concentration were found for both iodine and gadolinium samples across all single-energy CT (SECT) and DECT scan conditions. The VMI at 50 keV provided the highest contrast enhancement for both types of contrast samples at each concentration level, and single-energy CT scans at low-energy beams showed higher contrast enhancement than higher beam energies. In addition, the contrast enhancement for pure gadolinium solution was constantly higher than pure iodine solution at an identical mass concentration level. The DECT cystogram was performed with excellent technical success. The urinary bladder was appropriately distended with intravesical contrast measuring 606 Hounsfield units and no evidence of bladder leak or fistula. CONCLUSIONS: Imaging of gadolinium-based contrast agents is improved using a DECT technique, with VMI at 50 keV providing the highest contrast enhancement among our tested parameters. Dual-energy CT cystography using a gadolinium-based agent can be a safe and effective alternative when iodinated agents are contraindicated.

Evaluating a Convolutional Neural Network Noise Reduction Method When Applied to CT Images Reconstructed Differently Than Training Data.

OBJECTIVE: The aim of this study was to evaluate a narrowly trained convolutional neural network (CNN) denoising algorithm when applied to images reconstructed differently than training data set. METHODS: A residual CNN was trained using 10 noise inserted examinations. Training images were reconstructed with 275 mm of field of view (FOV), medium smooth kernel (D30), and 3 mm of thickness. Six examinations were reserved for testing; these were reconstructed with 100 to 450 mm of FOV, smooth to sharp kernels, and 1 to 5 mm of thickness. RESULTS: When test and training reconstruction settings were not matched, there was either reduced denoising efficiency or resolution degradation. Denoising efficiency was reduced when FOV was decreased or a smoother kernel was used. Resolution loss occurred when the network was applied to an increased FOV, sharper kernel, or decreased image thickness. CONCLUSIONS: The CNN denoising performance was degraded with variations in FOV, kernel, or decreased thickness. Denoising performance was not affected by increased thickness.