Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction.

PURPOSE: To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learning image reconstruction (DLIR), and whether DECT spectral curve usage improves accuracy of indeterminate lesion characterization. METHODS: In this prospective Health Insurance Portability and Accountability Act-compliant study (March through August 2022), adult men and non-pregnant adult women with biopsy-proven colorectal cancer and liver metastases underwent SECT (120 kVp) and a DECT (70 keV) portovenous abdominal CT scan using DLIR in the same breath-hold (Revolution CT ES; GE Healthcare). Participants were excluded if consent could not be obtained, if there were nonequivalent radiation doses between the two scans, or if the examination was cancelled/rescheduled. Three radiologists independently performed lesion detection and characterization during two separate sessions (SECT DLIRmedium and DECT DLIRhigh) as well as reported lesion confidence and overall image quality. Hounsfield units were measured. Spectral HU curves were provided for any lesions rated as indeterminate. McNemar's test was used to test the marginal homogeneity in terms of diagnostic sensitivity, accuracy and lesion detection. A generalized estimating equation method was used for categorical outcomes. RESULTS: 30 participants (mean age, 58 years ± 11, 21 men) were evaluated. Mean CTDIvol was 34 mGy for both scans. 141 lesions (124 metastases, 17 benign) with a mean size of 0.8 cm ± 0.3 cm were identified. High scores for image quality (scores of 4 or 5) were not significantly different between DECT (N = 71 out of 90 total scores from the three readers) and SECT (N = 62) (OR, 2.01; 95% CI:0.89, 4.57; P = 0.093). Equivalent image noise to SECT DLIRmed (HU SD 10 ± 2) was obtained with DECT DLIRhigh (HU SD 10 ± 3) (P = 1). There was no significant difference in lesion detection between DECT and SECT (140/141 lesions) (99.3%; 95% CI:96.1%, 100%).The mean lesion confidence scores by each reader were 4.2 ± 1.3, 3.9 ± 1.0, and 4.8 ± 0.8 for SECT and 4.1 ± 1.4, 4.0 ± 1.0, and 4.7 ± 0.8 for DECT (odds ratio [OR], 0.83; 95% CI: 0.62, 1.11; P = 0.21). Small lesion (≤5mm) characterization accuracy on SECT and DECT was 89.1% (95% CI:76.4%, 96.4%; 41/46) and 84.8% (71.1%, 93.7%; 39/46), respectively (P = 0.41). Use of spectral HU lesion curves resulted in 34 correct changes in characterizations and no mischaracterizations. CONCLUSION: DECT required a higher strength of DLIR to obtain equivalent noise compared to SECT DLIR. At equivalent radiation doses and image noise, there was no significant difference in subjective image quality or observer lesion performance between DECT (70 keV) and SECT. However, DECT spectral HU curves of indeterminate lesions improved characterization.

Appendiceal Neuroendocrine Neoplasms: A Comprehensive Review.

ABSTRACT: Appendiceal neuroendocrine neoplasm (NEN) is the most common adult appendiceal malignant tumor, constituting 16% of gastrointestinal NENs. They are versatile tumors with varying morphology, immunohistochemistry, secretory properties, and cancer genomics. They are slow growing and clinically silent, to begin with, or present with features of nonspecific vague abdominal pain. Most acute presentations are attributed clinically to appendicitis, with most cases detected incidentally on pathology after an appendectomy. Approximately 40% of them present clinically with features of hormonal excess, which is likened to the functional secretory nature of their parent cell of origin. The symptoms of carcinoid syndrome render their presence clinically evident. However, slow growing and symptomatically silent in its initial stages, high-grade neuroendocrine tumors and neuroendocrine carcinomas of the appendix are aggressive and usually have hepatic and lymph node metastasis at presentation. This review article focuses on imaging characteristics, World Health Organization histopathological classification and grading, American Joint Committee on Cancer/Union or International Cancer Control, European Neuroendocrine Tumor Society staging, European Neuroendocrine Tumor Society standardized guidelines for reporting, data interpretation, early-stage management protocols, and advanced-stage appendiceal NENs. Guidelines are also set for the follow-up and reassessment. The role of targeted radiotherapy, chemotherapy, and high-dose somatostatin analogs in treating advanced disease are discussed, along with types of ablative therapies and liver transplantation for tumor recurrence. The search for newer location-specific biomarkers in NEN is also summarized. Regarding the varying aggressiveness of the tumor, there is a scope for research in the field, with plenty of data yet to be discovered.

Low pitch significantly reduces helical artifacts in abdominal CT.

PURPOSE: High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter. METHODS: To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested. RESULTS: For a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1-3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10-9), while study arm was not a significant factor (p = 0.76). CONCLUSION: The incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed.

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.

OBJECTIVE: To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS: We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS: Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION: Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

Perirenal lymphatics: anatomy, pathophysiology, and imaging spectrum of diseases.

Despite being rarely discussed, perinephric lymphatics are involved in many pathological and benign processes. The lymphatic system in the kidneys has a harmonious dynamic with ureteral and venous outflow, which can result in pathology when this dynamic is disturbed. Although limited by the small size of lymphatics, multiple established and emerging imaging techniques are available to visualize perinephric lymphatics. Manifestations of perirenal pathology may be in the form of dilation of perirenal lymphatics, as with peripelvic cysts and lymphangiectasia. Lymphatic collections may also occur, either congenital or as a sequela of renal surgery or transplantation. The perirenal lymphatics are also intimately involved in lymphoproliferative disorders, such as lymphoma as well as the malignant spread of disease. Although these pathologic entities often have overlapping imaging features, some have distinguishing characteristics that can suggest the diagnosis when paired with the clinical history.

How Do Cancer-Specific Computed Tomography Protocols Compare With the American College of Radiology Dose Index Registry? An Analysis of Computed Tomography Dose at 2 Cancer Centers.

BACKGROUND: Little guidance exists on how to stratify radiation dose according to diagnostic task. Changing dose for different cancer types is currently not informed by the American College of Radiology Dose Index Registry dose survey. METHODS: A total of 9602 patient examinations were pulled from 2 National Cancer Institute designated cancer centers. Computed tomography dose (CTDI vol ) was extracted, and patient water equivalent diameter was calculated. N-way analysis of variance was used to compare the dose levels between 2 protocols used at site 1, and three protocols used at site 2. RESULTS: Sites 1 and 2 both independently stratified their doses according to cancer indications in similar ways. For example, both sites used lower doses ( P < 0.001) for follow-up of testicular cancer, leukemia, and lymphoma. Median dose at median patient size from lowest to highest dose level for site 1 were 17.9 (17.7-18.0) mGy (mean [95% confidence interval]) and 26.8 (26.2-27.4) mGy. For site 2, they were 12.1 (10.6-13.7) mGy, 25.5 (25.2-25.7) mGy, and 34.2 (33.8-34.5) mGy. Both sites had higher doses ( P < 0.001) between their routine and high-image-quality protocols, with an increase of 48% between these doses for site 1 and 25% for site 2. High-image-quality protocols were largely applied for detection of low-contrast liver lesions or subtle pelvic pathology. CONCLUSIONS: We demonstrated that 2 cancer centers independently choose to stratify their cancer doses in similar ways. Sites 1 and 2 dose data were higher than the American College of Radiology Dose Index Registry dose survey data. We thus propose including a cancer-specific subset for the dose registry.

Oncology-specific radiation dose and image noise reference levels in adult abdominal-pelvic CT.

OBJECTIVES: To provide our oncology-specific adult abdominal-pelvic CT reference levels for image noise and radiation dose from a high-volume, oncologic, tertiary referral center. METHODS: The portal venous phase abdomen-pelvis acquisition was assessed for image noise and radiation dose in 13,320 contrast-enhanced CT examinations. Patient size (effective diameter) and radiation dose (CTDIvol) were recorded using a commercial software system, and image noise (Global Noise metric) was quantified using a custom processing system. The reference level and range for dose and noise were calculated for the full dataset, and for examinations grouped by CT scanner model. Dose and noise reference levels were also calculated for exams grouped by five different patient size categories. RESULTS: The noise reference level was 11.25 HU with a reference range of 10.25-12.25 HU. The dose reference level at a median effective diameter of 30.7 cm was 26.7 mGy with a reference range of 19.6-37.0 mGy. Dose increased with patient size; however, image noise remained approximately constant within the noise reference range. The doses were 2.1-2.5 times than the doses in the ACR DIR registry for corresponding patient sizes. The image noise was 0.63-0.75 times the previously published reference level in abdominal-pelvic CT examinations. CONCLUSIONS: Our oncology-specific abdominal-pelvic CT dose reference levels are higher than in the ACR dose index registry and our oncology-specific image noise reference levels are lower than previously proposed image noise reference levels. ADVANCES IN KNOWLEDGE: This study reports reference image noise and radiation dose levels appropriate for the indication of abdomen-pelvis CT examination for cancer diagnosis and staging. The difference in these reference levels from non-oncology-specific CT examinations highlight a need for indication-specific, dose index and image quality reference registries.

Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.

BACKGROUND: Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI). AIM: To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models. Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth: Skip-slice annotation and AI-initiated annotation. METHODS: We developed a 2D U-Net as an AI second observer for detecting colorectal cancer (CRC) and an ensemble of 5 differently initiated 2D U-Net for ensemble technique. Each model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis, tested with 7 cases, and validated with 20 cases from The Cancer Imaging Archive cases. The sensitivity, false positives per case, and estimated Dice coefficient were obtained for each method of training. We compared the two methods of annotations and the time reduction associated with the technique. The time differences were tested using Friedman's two-way analysis of variance. RESULTS: Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time (P < 0.001). Reduction of up to 2/3 of the annotation does not reduce AI model sensitivity or false positives per case. Although initializing human annotation with AI reduces the annotation time, the reduction is minimal, even when using an ensemble AI to decrease false positives. CONCLUSION: Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth.

Neurofibromatosis from Head to Toe: What the Radiologist Needs to Know.

Neurofibromatosis type 1 (NF1) and neurofibromatosis type 2 (NF2) are autosomal dominant inherited neurocutaneous disorders or phakomatoses secondary to mutations in the NF1 and NF2 tumor suppressor genes, respectively. Although they share a common name, NF1 and NF2 are distinct disorders with a wide range of multisystem manifestations that include benign and malignant tumors. Imaging plays an essential role in diagnosis, surveillance, and management of individuals with NF1 and NF2. Therefore, it is crucial for radiologists to be familiar with the imaging features of NF1 and NF2 to allow prompt diagnosis and appropriate management. Key manifestations of NF1 include café-au-lait macules, axillary or inguinal freckling, neurofibromas or plexiform neurofibromas, optic pathway gliomas, Lisch nodules, and osseous lesions such as sphenoid dysplasia, all of which are considered diagnostic features of NF1. Other manifestations include focal areas of signal intensity in the brain, low-grade gliomas, interstitial lung disease, various abdominopelvic neoplasms, scoliosis, and vascular dysplasia. The various NF1-associated abdominopelvic neoplasms can be categorized by their cellular origin: neurogenic neoplasms, interstitial cells of Cajal neoplasms, neuroendocrine neoplasms, and embryonal neoplasms. Malignant peripheral nerve sheath tumors and intracranial tumors are the leading contributors to mortality in NF1. Classic manifestations of NF2 include schwannomas, meningiomas, and ependymomas. However, NF2 may have shared cutaneous manifestations with NF1. Lifelong multidisciplinary management is critical for patients with either disease. The authors highlight the genetics and molecular pathogenesis, clinical and pathologic features, imaging manifestations, and multidisciplinary management and surveillance of NF1 and NF2. Online supplemental material is available for this article. ©RSNA, 2022.

Review of uterine fibroids: imaging of typical and atypical features, variants, and mimics with emphasis on workup and FIGO classification.

Uterine fibroids are the most common gynecologic neoplasm. Although non-degenerated fibroids are easily identifiable on imaging, degenerated fibroids, fibroid variants, and fibroids with unusual growth patterns can constitute a diagnostic dilemma. Identification of these abnormal morphologic features can alter the diagnosis of presumed uterine fibroids and hence change management plans. This article reviews the typical and atypical radiologic imaging features of uterine fibroids, with an emphasis on the pitfalls, mimics, and radiologically identifiable features that can alter clinical management plans.

Performance of Multidetector Computed Tomography and Negative Versus Positive Enteric Contrast for Evaluation of Gastrointestinal Neuroendocrine Neoplasms.

BACKGROUND: Routine computed tomography (CT) scans are thought to have poor performance for detection of gastrointestinal (GI) neuroendocrine neoplasms (NENs), which leads to delayed workup. Detection of even 1 bowel tumor can guide diagnostic workup and management. The purposes of this study were to assess the accuracy of multidetector computed tomography (MDCT) and to compare negative versus positive enteric contrast in detecting at least 1 GI tumor per patient with suspected or confirmed diagnosis of a NEN. METHODS: This retrospective study included 107 patients with intravenous and oral contrast (65 positive, 40 negative, and 2 no oral contrast) abdominopelvic MDCT. Two abdominal radiologists independently analyzed the CTs for detection and localization of bowel NENs. Surgical pathology was considered the reference standard. Analyses included κ and summary statistics, McNemar test, Pearson χ2 test, and Fisher exact test. RESULTS: Among the 107 CT scans, there were 30 pathology negative studies and 77 studies with positive pathology for GI NEN. Interreader agreement for CT evaluation was substantial (κ = 0.61). At least 1 GI NEN per patient was detected with 51% to 53% sensitivity, 87% to 93% specificity, 91% to 95% positive predictive value (PPV), 42% negative predictive value, and 63% accuracy for each reader, and 57% accuracy when only the concordant (ie, matching) results of the 2 readers were considered. Computed tomography scans with negative enteric contrast had significantly higher sensitivity for concordant results than CTs with positive enteric contrast (58% vs 30%, P = 0.01). Specificity (100% vs 95%, P = 0.5), PPV (100% vs 93%, P = 0.49), negative predictive value (39% vs 39%, P = 0.99), and accuracy (67% vs 51%, P = 0.10) were not significantly different for negative versus positive enteric contrast for the concordant results. There was no significant difference in GI NEN localization between the readers. CONCLUSIONS: Routine MDCT with either positive or negative enteric contrast can detect at least 1 GI tumor per patient with more than 90% PPV and more than 50% accuracy in patients suspected of GI NEN. Using negative enteric contrast improves sensitivity for GI NEN versus positive enteric contrast. In addition, there is high accuracy in localizing the bowel tumor with positive or negative enteric contrast, which may guide surgery. Radiologists should have heightened awareness that evaluating such scans closely may lead to detection of primary bowel NENs at a higher rate than previously reported.

Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Imaging of Neuroendocrine Prostatic Carcinoma.

Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment resistance during therapy for conventional prostatic adenocarcinoma, the latter is also termed as castration-resistant prostate cancer (CRPC). The incidence of NEPC increases after hormonal therapy and they represent a challenge, both in the radiological and pathological diagnosis, as well as in the clinical management. This article provides a comprehensive imaging review of prostatic neuroendocrine tumors.

Mastocytosis-A Review of Disease Spectrum with Imaging Correlation.

Mastocytosis is a rare disorder due to the abnormal proliferation of clonal mast cells. Mast cells exist in most tissues, mature in situ from hematopoietic stem cells and develop unique characteristics of local effector cells. Mastocytosis develops by activation mutation of the KIT surface receptor which is involved in the proliferation of a number of cell lines such as mast cells, germ cells, melanocytes, and hematopoietic cells. It manifests as two main categories: cutaneous mastocytosis and systemic mastocytosis. Imaging can play an important role in detection and characterization of the disease manifestation, not only by radiography and bone scans, but also magnetic resonance imaging and computed tomography, which can be more sensitive in the assessment of distinctive disease patterns. Radiologists should be aware of various appearances of this disease to better facilitate diagnosis and patient management. Accordingly, this review will discuss the clinical presentation, pathophysiology, and role of imaging in detection and extent estimation of the systemic involvement of the disease, in addition to demonstration of appearance on varying imaging modalities. Familiarity with the potential imaging findings associated with mastocytosis can aid in early disease diagnosis and classification and accordingly can lead directing further work up and better management.

Oncologic Imaging of the Lymphatic System: Current Perspective with Multi-Modality Imaging and New Horizon.

The lymphatic system is an anatomically complex vascular network that is responsible for interstitial fluid homeostasis, transport of large interstitial particles and cells, immunity, and lipid absorption in the gastrointestinal tract. This network of specially adapted vessels and lymphoid tissue provides a major pathway for metastatic spread. Many malignancies produce vascular endothelial factors that induce tumoral and peritumoral lymphangiogenesis, increasing the likelihood for lymphatic spread. Radiologic evaluation for disease staging is the cornerstone of oncologic patient treatment and management. Multiple imaging modalities are available to access both local and distant metastasis. In this manuscript, we review the anatomy, physiology, and imaging of the lymphatic system.

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.

Characteristics of infective endocarditis in a cancer population.

BACKGROUND: Infective endocarditis (IE) is more common in patients with cancer as compared with the general population. Due to an immunocompromised state, the need for invasive procedures, hypercoagulability and the presence of indwelling catheters, patients with cancer are particularly predisposed to the development of IE. OBJECTIVES: Limited information exists about IE in patients with cancer. We aimed to evaluate the characteristics of patients with cancer and IE at our tertiary care centre, including a comparison of the microorganisms implicated and their association with mortality. METHODS: A retrospective chart review of patients with cancer who had echocardiography for suspicion of endocarditis was conducted. A total of 56 patients with a confirmed diagnosis of cancer and endocarditis, based on the modified Duke criteria, were included in the study. Baseline demographics, risk factors for developing IE, echocardiography findings, microbiology and mortality data were analysed. RESULTS: Following the findings of vegetations by echocardiography, the median survival time was 8.5 months. Staphylococcus aureus was the most common organism identified as causing endocarditis. The mitral and aortic valves were the most commonly involved sites of endocarditis. Patients with S. aureus endocarditis (SAE) had a significantly poorer survival when compared with patients without SAE (p=0.0217) over the 12-month period from diagnosis of endocarditis. CONCLUSIONS: Overall survival of patients with cancer and endocarditis is poor, with a worse outcome in patients with SAE.