Try a new search

Format these results:

Searched for:



Total Results:


Who Wants to Learn How to Teach? Perceptions of Radiology Residents and Radiology Teaching Faculty Regarding Resident as Teacher Training

Paul, Caroline R; Alpert, Jeffrey B; El-Ali, Alexander M; Sheth, Monica M; Qian, Kun; Fefferman, Nancy R
RATIONALE AND OBJECTIVES/OBJECTIVE:While the ACGME requires Resident as Teacher (RAT) training, curricula in radiology remain limited. Our study was performed to examine radiology residents (RR) and teaching faculty (TF) perceptions about RAT training. MATERIALS AND METHODS/METHODS:In 2021, anonymous online surveys were administered to all RR (53-item) and to all TF (24-item) of a radiology residency program. Content domains included attitudes about RAT training and learning topics. RESULTS:Response rates were 97% (38/39) for RR and 54% (58/107) for TF. Most RR desired training to become better educators to medical students (MS) (81%) and other residents (83%). Seventy-seven percent of RR reported the importance regarding how to give feedback to other learners, while 94% desired formal training on delivering case presentations. While 94% of RR reported that resident feedback was valuable, only 6% reported always giving feedback to MS. Seventy-two percent of RR did not apply at least some best-practices in their reading room teaching. Fifty-nine percent of RR wanted TF to observe their own teaching skills and provide feedback although 70% reported rarely or never receiving TF feedback. Ninety-three percent of TF reported RR should receive RAT training, while 88% reported that feedback of RR to MS was important. CONCLUSION/CONCLUSIONS:RR and TF strongly endorsed the need for RAT training. RR anticipate teaching to be an important part of their careers. We identified learning topics and possible gaps regarding how TF are meeting RR needs, which could inform the development of RAT curricula.
PMID: 36528427
ISSN: 1878-4046
CID: 5382652

Design, Implementation and Initial Impact of a Longitudinal Radiology Curriculum in a Primary Care-Focused Medical School

Ocal, Selin; Schiller, Emily; Alpert, Jeffrey B; Stavrakis, Costas; Fefferman, Nancy R; Hoffmann, Jason C
PMID: 36893997
ISSN: 1558-349x
CID: 5432902

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis

Azour, Lea; Hu, Yunan; Ko, Jane P; Chen, Baiyu; Knoll, Florian; Alpert, Jeffrey B; Brusca-Augello, Geraldine; Mason, Derek M; Wickstrom, Maj L; Kwon, Young Joon Fred; Babb, James; Liang, Zhengrong; Moore, William H
PURPOSE/OBJECTIVE:To assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis. METHODS:Simulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction. RESULTS:At the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality (P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures (P = 0.002), in comparison with 100 mAs. CONCLUSIONS:Deep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.
PMID: 36790870
ISSN: 1532-3145
CID: 5432132

Prevalence of Adenopathy at Chest Computed Tomography After Vaccination for Severe Acute Respiratory Syndrome Coronavirus 2

McGuinness, Georgeann; Alpert, Jeffrey B; Brusca-Augello, Geraldine; Azour, Lea; Ko, Jane P; Tamizuddin, Farah; Gozansky, Elliott K; Moore, William H
OBJECTIVE:This study aimed to determine the prevalence of axillary and subpectoral (SP) lymph nodes after ipsilateral COVID-19 vaccine administration on chest computed tomography (CT). METHODS:Subjects with chest CTs between 2 and 25 days after a first or second vaccine dose, December 15, 2020, to February 12, 2021, were included. Orthogonal measures of the largest axillary and SP nodes were recorded by 2 readers blinded to vaccine administration and clinical details. A mean nodal diameter discrepancy of ≥6 mm between contralateral stations was considered positive for asymmetry. Correlation with the side of vaccination, using a Spearman rank correlation, was performed on the full cohort and after excluding patients with diseases associated with adenopathy. RESULTS:Of the 138 subjects (81 women, 57 men; mean [SD] age, 74.4 ± 11.7 years), 48 (35%) had asymmetrically enlarged axillary and/or SP lymph nodes, 42 (30%) had ipsilateral, and 6 (4%) had contralateral to vaccination ( P = 0.003). Exclusion of 29 subjects with conditions associated with adenopathy showed almost identical correlation, with asymmetric nodes in 32 of 109 (29%) ipsilateral and in 5 of 109 (5%) contralateral to vaccination ( P = 0.002). CONCLUSIONS:Axillary and/or SP lymph nodes ipsilateral to vaccine administration represents a clinical conundrum. Asymmetric nodes were detected at CT in 30% of subjects overall and 29% of subjects without conditions associated with adenopathy, approximately double the prevalence rate reported to the Centers for Disease Control and Prevention by vaccine manufacturers. When interpreting examinations correlation with vaccine administration timing and site is important for pragmatic management.
PMID: 36571247
ISSN: 1532-3145
CID: 5418932

Cover Your Base: CT Review of Lower Neck and Thoracic Inlet Variant Anatomy and Pathology [Meeting Abstract]

Patil, S; Nayak, G; Young, M; Alpert, J
Background: The base of neck and thoracic inlet is sometimes considered a 'no man's land,' with imaging anatomy shared by both head and neck and thoracic radiologists. Informally, thoracic radiologists may endorse limited confidence with normal anatomy and pathology in this region, which serves as a conduit for several important anatomic structures. Further, thoracic radiologists may not be aware of the clinical impact of some anatomic variants and abnormalities (which may influence patient symptoms or affect surgical planning). Multiple factors contribute to variability in imaging appearances of the lower neck and thoracic inlet, including z-axis scan range and scan angle, patient kyphosis, and arm position. Accurate recognition of normal and variant anatomy is critical to detect pathology and minimize diagnostic error at the lower neck and thoracic inlet on chest computed tomography (CT). Educational Goals/Teaching Points: We aim to provide a systemsbased, image-rich review of normal and variant anatomy and pathology at the lower neck and thoracic inlet, primarily utilizing CT. When appropriate, the clinical relevance of anatomic variants will be discussed. Challenging and often overlooked pathology will be reviewed in an effort to build radiologists' confidence and improve diagnostic accuracy. This will include cross-sectional imaging of a variety of anatomic organs and systems: Upper aerodigestive tract including larynx and hypopharynx Vascular structures, including variant course, vessel thrombus, dissection, and aneurysm Endocrine glands, including ectopic thyroid, thyroglossal duct cyst, and parathyroid adenoma Lymphatics, including relevant nodal stations, and pathology including lymphocele Nervous system structures including brachial plexus, vagus nerve, recurrent laryngeal nerve, and cervical sympathetic trunk Musculoskeletal structures including cervical ribs and supernumerary heads of the sternocleidomastoid muscle
Conclusion(s): By reviewing normal and variant anatomy and clinically relevant pathology at the lower neck and thoracic inlet, thoracic radiologists can achieve greater diagnostic confidence and accuracy
ISSN: 1536-0237
CID: 5082972

Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management

Azour, Lea; Ko, Jane P; Washer, Sophie L; Lanier, Amelia; Brusca-Augello, Geraldine; Alpert, Jeffrey B; Moore, William H
Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.
PMID: 34053604
ISSN: 1557-8275
CID: 4890782

Medical Student Engagement and Educational Value of a Remote Clinical Radiology Learning Environment: Creation of Virtual Read-Out Sessions in Response to the COVID-19 Pandemic

Alpert, Jeffrey B; Young, Matthew G; Lala, Shailee V; McGuinness, Georgeann
RATIONALE AND OBJECTIVES/OBJECTIVE:The need for social distancing has resulted in rapid restructuring of medical student education in radiology. While students traditionally spend time learning in the reading room, remote clinical learning requires material shared without direct teaching at the radiology workstation. Can remote clinical learning meet or exceed the educational value of the traditional in-person learning experience? Can student engagement be matched or exceeded in a remote learning environment? MATERIALS AND METHODS/METHODS:To replace the in-person reading room experience, a small-group learning session for medical students named Virtual Read-Out (VRO) was developed using teleconferencing software. After Institutional Review Board approval, two student groups were anonymously surveyed to assess differences in student engagement and perceived value between learning environments: "Conventional" students participating in the reading room (before the pandemic) and "Remote" students participating in VRO sessions. Students reported perceived frequency of a series of five-point Likert statements. Based on number of respondents, an independent t-test was performed to determine the significance of results between two groups. RESULTS:Twenty-seven conventional and 41 remote students responded. Remote students reported modest but significantly higher frequency of active participation in reviewing radiology exams (p < 0.05). There was significantly lower frequency of reported boredom among Remote students (p < 0.05). There was no significant difference in perceived educational value between the two groups. CONCLUSION/CONCLUSIONS:Students report a high degree of teaching quality, clinical relevance, and educational value regardless of remote or in-person learning format. Remote clinical radiology education can be achieved with equal or greater student interaction and perceived value in fewer contact hours than conventional learning in the reading room.
PMID: 33268209
ISSN: 1878-4046
CID: 4694252

Quantitative Computed Tomographic Evaluation of Lung Nodules

Alpert, J B
ISSN: 2589-8701
CID: 4986912

Congenital Coronary Artery Anomalies and Implications

Azour, Lea; Jacobi, Adam H; Alpert, Jeffrey B; Uppu, Santosh; Latson, Larry; Mason, Derek; Cham, Matthew D
This pictorial essay presents cases of congenital coronary artery anomalies, including congenital anomalies of origin, course, and termination. Familiarity with atypical coronary anatomy and clinical presentation may facilitate appropriate diagnosis and management, particularly as cardiac and thoracic computed tomographic utilization increases.
PMID: 29979240
ISSN: 1536-0237
CID: 3186202

Image Quality on Dual-energy CTPA Virtual Monoenergetic Images: Quantitative and Qualitative Assessment

Dane, Bari; Patel, Hersh; O'Donnell, Thomas; Girvin, Francis; Brusca-Augello, Geraldine; Alpert, Jeffrey B; Niu, Bowen; Attia, Mariam; Babb, James; Ko, Jane P
RATIONALE AND OBJECTIVES/OBJECTIVE:This study aims to determine the optimal photon energy for image quality of the pulmonary arteries (PAs) on dual-energy computed tomography (CT) pulmonary angiography (CTPA) utilizing low volumes of iodinated contrast. MATERIALS AND METHODS/METHODS:The study received institutional review board exemption and was Health Insurance Portability and Accountability Act compliant. Adults (n = 56) who underwent dual-energy CTPA with 50-60 cc of iodinated contrast on a third-generation dual-source multidetector CT were retrospectively and consecutively identified. Twelve virtual monoenergetic kiloelectron volt (keV) image data sets (40-150 keV, 10-keV increments) were generated with a second-generation noise-reducing algorithm. Standard regions of interest were placed on main, right, left, and right interlobar pulmonary arteries; pectoralis muscle; and extrathoracic air. Attenuation [mean CT number (Hounsfield unit, HU)], noise [standard deviation (HU)], signal to noise (SNR), and contrast to noise ratio were evaluated. Three blinded chest radiologists rated (from 1 to 5, with 5 being the best) randomized monoenergetic and weighted-average images for attenuation and noise. P <.05 was considered significant. RESULTS:Region of interest mean CT number increased as keV decreased, with 40 keV having the highest value (P < .001). Mean SNR was highest for 40-60 keV (P <.05) (14.5-14.7) and was higher (P <.05) than all remaining energies (90-150 keV) for all vessel regions combined. Contrast to noise ratio was highest for 40 keV (P <.001) and decreased as keV increased. SNR was highest at 60 and 70 keV, only slightly higher than 40-50 keV (P <.05). Reader scores for 40-50 keV were greater than other energies and weighted-average images (P <.05). CONCLUSIONS:Kiloelectron volt images of 40-50 keV from the second-generation algorithm optimize attenuation on dual-energy CTPA and can potentially aid in interpretation and avoiding nondiagnostic examinations.
PMID: 29398436
ISSN: 1878-4046
CID: 2979202