In x-ray computed tomography (CT), materials having different elemental compositions can be represented by identical pixel values on a CT image (ie, CT numbers), depending on the mass density of the ...material. Thus, the differentiation and classification of different tissue types and contrast agents can be extremely challenging. In dual-energy CT, an additional attenuation measurement is obtained with a second x-ray spectrum (ie, a second "energy"), allowing the differentiation of multiple materials. Alternatively, this allows quantification of the mass density of two or three materials in a mixture with known elemental composition. Recent advances in the use of energy-resolving, photon-counting detectors for CT imaging suggest the ability to acquire data in multiple energy bins, which is expected to further improve the signal-to-noise ratio for material-specific imaging. In this review, the underlying motivation and physical principles of dual- or multi-energy CT are reviewed and each of the current technical approaches is described. In addition, current and evolving clinical applications are introduced.
Photon-counting detector (PCD) CT is a new CT technology utilizing a direct conversion X-ray detector, where incident X-ray photon energies are directly recorded as electronical signals. The design ...of the photon-counting detector itself facilitates improvements in spatial resolution (via smaller detector pixel design) and iodine signal (via count weighting) while still permitting multi-energy imaging. PCD-CT can eliminate electronic noise and reduce artifacts due to the use of energy thresholds. Improved dose efficiency is important for low dose CT and pediatric imaging. The ultra-high spatial resolution of PCD-CT design permits lower dose scanning for all body regions and is particularly helpful in identifying important imaging findings in thoracic and musculoskeletal CT. Improved iodine signal may be helpful for low contrast tasks in abdominal imaging. Virtual monoenergetic images and material classification will assist with numerous diagnostic tasks in abdominal, musculoskeletal, and cardiovascular imaging. Dual-source PCD-CT permits multi-energy CT images of the heart and coronary arteries at high temporal resolution. In this special review article, we review the clinical benefits of this technology across a wide variety of radiological subspecialties.
Photon-counting detector (PCD) CT is an emerging technology that has shown tremendous progress in the last decade. Various types of PCD CT systems have been developed to investigate the benefits of ...this technology, which include reduced electronic noise, increased contrast-to-noise ratio with iodinated contrast material and radiation dose efficiency, reduced beam-hardening and metal artifacts, extremely high spatial resolution (33 line pairs per centimeter), simultaneous multienergy data acquisition, and the ability to image with and differentiate among multiple CT contrast agents. PCD technology is described and compared with conventional CT detector technology. With the use of a whole-body research PCD CT system as an example, PCD technology and its use for in vivo high-spatial-resolution multienergy CT imaging is discussed. The potential clinical applications, diagnostic benefits, and challenges associated with this technology are then discussed, and examples with phantom, animal, and patient studies are provided.
RSNA, 2019.
The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT ...examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.
Articles in the scientific literature and lay press over the past several years have implied that computed tomography (CT) may cause cancer and that physicians and patients must exercise caution in ...its use. Although there is broad agreement on the latter point--unnecessary medical tests of any type should always be avoided--there is considerable controversy surrounding the question of whether, or to what extent, CT scans can lead to future cancers. Although the doses used in CT are higher than those used in conventional radiographic examinations, they are still 10 to 100 times lower than the dose levels that have been reported to increase the risk of cancer. Despite the fact that at the low doses associated with a CT scan the risk either is too low to be convincingly demonstrated or does not exist, the magnitude of the concern among patients and some medical professionals that CT scans increase cancer risk remains unreasonably high. In this article, common questions about CT scanning and radiation are answered to provide physicians with accurate information on which to base their medical decisions and respond to patient questions.
To determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to ...compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.
This HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.
Noise reduction of up to 59% (28.7 of 65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0 of 18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24 of 61) noise reduction and 67% (0.74 of 1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.
Iodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.
Background Controversy exists regarding the optimal management of incidentally discovered, small pancreatic neuroendocrine tumors (PNETs). Our aim was to review the outcomes of patients who underwent ...nonoperative and operative management. Methods We retrospectively reviewed patients with nonfunctioning PNETs at our institution from January 1, 2000 to June 30, 2011. Patients were included if the tumor was sporadic and <4 cm without radiographic evidence of local invasion or metastases. Results Nonoperative patients ( n = 77, median age, 67 years; range, 31–94) had a median tumor size of 1.0 cm (range, 0.3–3.2). Mean follow-up (F/U) was 45 months (max. 153 months). Median tumor size did not change throughout F/U; there was no disease progression or disease specific mortality. In the operative group ( n = 56, median age, 60 years; range, 27–82), median neoplasm size was 1.8 cm (range, 0.5–3.6). Mean F/U was 52 months (max. 138 months). A total of 46% of the operative patients had some type of complication, more than half due to a clinically significant pancreatic leak. No recurrence or disease specific mortality was seen in the operative group, including 5 patients with positive lymph nodes. Conclusion Small nonfunctioning PNETs usually exhibit minimal or no growth over many years. Nonoperative management may be advocated when serial imaging demonstrates minimal or no growth without suspicious features.
Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3–36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare ...performance against radiologists in a case-control study.
Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator–based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale.
Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97–1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5–100.0), specificity (90.3; 84.3–91.5), F1-score (89.5; 82.3–91.7), area under the curve (AUC) (0.98; 0.94–0.98), and accuracy (92.2%; 86.7–93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen’s kappa 0.3) and their mean AUC (0.66; 0.46–0.86) was lower than each of the 4 ML models (AUCs: 0.95–0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5).
Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.
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Artificial intelligence can detect pancreas cancer on computed tomography well before its detection by radiologists and before the onset of cancer symptoms when surgical cure is a possibility.
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