This easy-to-use worksheet and photographic record method gives any landowner a simple way to monitor streamside erosion and waterway sediment data for use in land management decisions or to demonstrate compliance with water quality standards.

Purpose

Plain radiography of the cervical spine is used as a screening test for trauma patients. We evaluated the diagnostic yield of performing anteroposterior (AP), odontoid, and oblique views in addition to the lateral view in the current era when radiographs are performed only on low-risk patients.

Methods

All imaging reports from cervical spine radiography studies on patients aged 18 years and older in the emergency room of a major academic medical center between November 22, 2003, and January 17, 2012, were retrospectively reviewed. For the clinical workflow employed at the time of study acquisition, radiologists prospectively reviewed the lateral projection and subsequently reviewed the entirety of the images obtained. Exam reports and, when necessary, images were reviewed to determine which patients had fractures and on which projection the fractures were identified.

Results

Six fractures were detected in 7218 exams. Three of these fractures were identified on the lateral radiograph, and three of these fractures were visualized on the additional projections (two on oblique and one on odontoid views). The yield of the additional projections is one fracture per 9713 radiographic projections (90% confidence interval of one fracture per 1245-47,946 examinations). For two of the patients with fractures identified on the lateral projection, an additional fracture was seen when CT was then performed.

Conclusions

Performing additional radiographs of the cervical spine including AP, odontoid, and bilateral oblique projections in trauma patients with low pretest probability of fracture augments the diagnostic yield of lateral radiographs. Considering the potential for devastating neurological outcomes from missed cervical fractures, addition of AP, odontoid, and oblique projections continues to detect fractures at a low rate.

CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. Keywords: CT Angiography, Pulmonary Arteries © RSNA, 2022.

Nonpoint source (NPS), common in agriculture, reaches the environment as runoff from a field, not output from a waste pipe. Its control and regulation are also different from other types of pollution that may seem more familiar.

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021.

Purpose

To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.

Materials and methods

In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.

Results

Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.

Conclusion

The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023.

Botulinum neurotoxins have remarkable persistence (∼weeks to months in cells), outlasting the small-molecule inhibitors designed to target them. To address this disconnect, inhibitors bearing two pharmacophores-a zinc binding group and a Cys-reactive warhead-were designed to leverage both affinity and reactivity. A series of first-generation bifunctional inhibitors was achieved through structure-based inhibitor design. Through X-ray crystallography, engagement of both the catalytic Zn²⁺ and Cys165 was confirmed. A second-generation series improved on affinity by incorporating known reversible inhibitor pharmacophores; the mechanism was confirmed by exhaustive dialysis, mass spectrometry, and in vitro evaluation against the C165S mutant. Finally, a third-generation inhibitor was shown to have good cellular activity and low toxicity. In addition to our findings, an alternative method of modeling time-dependent inhibition that simplifies assay setup and allows comparison of inhibition models is discussed.

Management of chronic thromboembolic pulmonary hypertension (CTEPH) should be determined by a multidisciplinary team, ideally at a specialized CTEPH referral center. Radiologists contribute to this multidisciplinary process by helping to confirm the diagnosis of CTEPH and delineating the extent of disease, both of which help determine a treatment decision. Preoperative assessment of CTEPH usually employs multiple imaging modalities, including ventilation-perfusion (V/Q) scanning, echocardiography, CT pulmonary angiography (CTPA), and right heart catheterization with pulmonary angiography. Accurate diagnosis or exclusion of CTEPH at imaging is imperative, as this remains the only form of pulmonary hypertension that is curative with surgery. Unfortunately, CTEPH is often misdiagnosed at CTPA, which can be due to technical factors, patient-related factors, radiologist-related factors, as well as a host of disease mimics including acute pulmonary embolism, in situ thrombus, vasculitis, pulmonary artery sarcoma, and fibrosing mediastinitis. Although V/Q scanning is thought to be substantially more sensitive for CTEPH compared with CTPA, this is likely due to lack of recognition of CTEPH findings rather than a modality limitation. Preoperative evaluation for pulmonary thromboendarterectomy (PTE) includes assessment of technical operability and surgical risk stratification. While the definitive therapy for CTEPH is PTE, other minimally invasive or noninvasive therapies also lead to clinical improvements including greater survival. Complications of PTE that can be identified at postoperative imaging include infection, reperfusion edema or injury, pulmonary hemorrhage, pericardial effusion or hemopericardium, and rethrombosis. ^©RSNA, 2022 Online supplemental material is available for this article.

Objective

To characterize Alexander disease (AxD) phenotypes and determine correlations with age at onset (AAO) and genetic mutation. AxD is an astrogliopathy usually characterized on MRI by leukodystrophy and caused by glial fibrillary acidic protein (GFAP) mutations.

Methods

We present 30 new cases of AxD and reviewed 185 previously reported cases. We conducted Wilcoxon rank sum tests to identify variables scaling with AAO, survival analysis to identify predictors of mortality, and χ(2) tests to assess the effects of common GFAP mutations. Finally, we performed latent class analysis (LCA) to statistically define AxD subtypes.

Results

LCA identified 2 classes of AxD. Type I is characterized by early onset, seizures, macrocephaly, motor delay, encephalopathy, failure to thrive, paroxysmal deterioration, and typical MRI features. Type II is characterized by later onset, autonomic dysfunction, ocular movement abnormalities, bulbar symptoms, and atypical MRI features. Survival analysis predicted a nearly 2-fold increase in mortality among patients with type I AxD relative to those with type II. R79 and R239 GFAP mutations were most common (16.6% and 20.3% of all cases, respectively). These common mutations predicted distinct clinical outcomes, with R239 predicting the most aggressive course.

Conclusions

AAO and the GFAP mutation site are important clinical predictors in AxD, with clear correlations to defined patterns of phenotypic expression. We propose revised AxD subtypes, type I and type II, based on analysis of statistically defined patient groups.