Canadian Association of Radiologists Journal

Call is among the most challenging aspects of radiology residency, fostering both knowledge development and decision-making under pressure. As imaging volumes have increased, call conditions have evolved. However, little is currently known about the variability in call structures among institutions. This study explores the perceptions of resident call across Canadian radiology programs.

A 28-question, bilingual, anonymous survey was distributed to all Canadian radiology residents via the Canadian Association of Radiologists (CAR) and institutional representatives of the CAR Resident and Fellow Section (RFS). Anonymous data was collected including demographic information, sleep habits, staff supervision, interactions with referring physicians, and wellness.

Our survey was open from Sept 1, 2024, to Feb 28, 2025. Altogether, 112 residents responded from all training levels and programs, with 71.4% completing it in English and 28.6% in French. Most PGY-2 residents reported 8 to 11 call shifts in the preceding 8 weeks, compared to 4 to 7 shifts among PGY-3 to PGY-5. Close to half of residents reported that they did not sleep at all on call. Among those who did, 70.3% of them reported moderately insufficient sleep. Interactions with referring physicians were poor, with 49.2% of residents rating their experiences as unsatisfactory or very unsatisfactory. Lower call satisfaction was significantly associated with greater call volume and reduced sleep.

Canadian radiology residents reported high call volumes, limited rest, and challenging interactions with referring physicians, all of which were linked to lower satisfaction and well-being. These findings highlight opportunities to optimize call structures, strengthen supervision, and improve resident wellness.

The carbon footprint of imaging equipment in radiology is high, but the impact of patient travel for imaging is unclear. This study aimed to quantify distances traveled by ambulatory patients for outpatient MRI, determine the proportion traveling beyond their nearest MRI facility, and estimate the associated excess carbon dioxide equivalent (CO₂e) emissions.

With research ethics board approval, we retrospectively analyzed provincial MRI data from 2023. Distances from patient home address to the nearest and attended MRI facilities were calculated. CO₂e estimates were derived using an online tool (www.carbonfootprint.com/calculator.aspx) with average vehicle size and emissions.

In 2023, 40 062 provincial MRI scans were performed; isolating single visits for adult patients yielded 27 755 data points. Of these, 19.5% (n = 5400) traveled beyond their nearest MRI facility. Mean round-trip travel was 60.4 km for those attending their nearest site versus 295.6 km for those traveling further (P < .0001). The excess travel produced ~288 metric tons of additional CO₂e, equivalent to emissions from 61 large-size gasoline-powered cars driven for 1 year or carbon sequestered by 4762 tree seedlings grown for 10 years.

Nearly one-fifth of patients traveled farther than necessary for MRI, generating substantial avoidable emissions. Aligning MRI service distribution with population density could lessen the environmental impact of medical imaging.

Breast arterial calcification (BAC), detectable on routine mammograms, offers a promising independent risk factor for cardiovascular disease (CVD) risk stratification. However, current BAC assessment methods lack standardization and rely on subjective interpretations. This study introduces a semi-supervised deep learning (DL) model to automate BAC severity grading, enhance cross-system generalizability, and align with clinical consensus.

A U-Net-based segmentation model was trained on 2560 annotated screening mammograms from 7 vendors. A semi-supervised learning strategy employing progressive pseudo-labeling incorporated 6000 unlabeled images to enhance model robustness. BAC severity was graded by thresholding the percentage area covered by BAC and benchmarked against radiologists’ assessments using Canadian Society of Breast Imaging (CSBI) guidelines. Performance was evaluated using the Jaccard Similarity Coefficient (JSC) for segmentation, along with accuracy, precision, F1-score, and recall. For detecting clinically significant (Grade 3) BAC, sensitivity, specificity, and area under the curve (AUC) were assessed. Agreement with experts was evaluated using weighted kappa statistics.

The proposed model achieved a JSC of 0.614, an accuracy of 0.991, an F1-score of 0.756, a precision of 0.763, and a recall of 0.764. It demonstrated superior segmentation accuracy compared to the baseline U-Net model. Agreement with consensus radiologists was high, with a weighted kappa of 0.90, 95% CI = (0.70, 1.00). For clinically significant (Grade 3) BAC, the model achieved an AUC of 0.87, 95% CI = (0.72, 1.00), sensitivity of 0.80, and specificity of 0.93.

The framework holds promise for clinical adoption, integrating into mammography workflows and improving women’s cardiovascular risk stratification.

Reduced field-of-view (rFOV) T2WI improves in-plane spatial resolution. Deep learning-based reconstruction (DLR) has emerged as enhancing image quality. We compared examination time, image quality, and lesion detection rates between pancreaticobiliary rFOV T2WI with and without DLR.

198 patients who underwent pancreaticobiliary rFOV T2WI were included. The protocol included rFOV T2WI with 2 NEX (rFOV T2WI_N2) and 1 NEX (rFOV T2WI_N1). DLR was applied to generate corresponding datasets: rFOV T2WI_N2-DLR and rFOV T2WI_N1-DLR datasets. Three observers evaluated the noise, respiratory motion artifacts (RMA), overall image quality (OIQ), and diagnostic confidence (DC). Two observers measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality metrics were compared using either ANOVA or Friedman test. Lesion detection rates were tested using the Cochran’s Q test and McNemar test.

The noise, RMA, OIQ, and DC scores of rFOV T2WI_N1-DLR were notably higher than those of rFOV T2WI_N1. The noise, OIQ, and DC scores of rFOV T2WI_N2 were notably higher than those of rFOV T2WI_N1 (P < .001). The SNR and CNR of rFOV T2WI with DLR were notably higher than those of rFOV T2WI without DLR (P < .05). The rFOV T2WI_N1-DLR sequence yielded a higher lesion detection rate (92.5%; 491/531) compared to rFOV T2WI_N1 (76.5%; 406/531).

Pancreaticobiliary rFOV T2WI with DLR is feasible and yields superior image quality compared to rFOV T2WI without DLR. A 37.1% to 74.4% reduction in acquisition time is achievable without increasing image noise or compromising overall image quality and lesion detection rate.

The perfect balance of image quality, scanning time, and lesion detection rate can be achieved by using DLR in pancreaticobiliary rFOV T2WI.

Local tumour progression (LTP) after percutaneous ablation of small renal cell carcinoma (RCC) is suspected when new enhancing or enlarging soft tissue appears within the ablation zone. Benign post-treatment changes can mimic this finding. This study compares the incidence and imaging characteristics of non-malignant changes (NMC) versus LTP after renal ablation.

In this single-center, retrospective study, all patients with RCC treated with radiofrequency ablation (RFA) from February 2004 to May 2016 were identified. Post-ablation imaging reports from through May 2017 were reviewed to detect findings suspicious for LTP. Patients with suspicious findings underwent clinical, imaging, and histopathologic follow-up through May 2025 to determine the reference diagnosis. Imaging features were categorized by morphology, location within the ablation zone, and enhancement pattern.

Among 256 patients (mean age 65.6 years ± 10.8, 193 men) with 268 treated tumours, 18 tumours (6.7%) developed suspicious imaging findings. Eight tumours (3.0%) were classified as NMC and 10 tumours (3.7%) as LTP. NMC had significantly lower CT enhancement than LTP (31 vs 152 HU, P < .001). Lesions along the renal parenchymal margin were exclusively associated with LTP (9/9), whereas abnormalities at the extrarenal margin or centrally within the ablation zone were predominantly NMC (8/9). Enhancement with washout was seen only in LTP.

Non-malignant post-ablation changes can mimic LTP and occur with similar frequency. Imaging features can help differentiate benign changes from local tumour progression and reduce unnecessary re-interventions.

Seizures are common neurological events in children, with neuroimaging playing a crucial role in evaluating new-onset seizures. While magnetic resonance imaging (MRI) is often preferred over computed tomography (CT) for pediatric seizure imaging due to higher sensitivity and lack of ionizing radiation, practices regarding imaging protocols and sedation use vary. Currently, there are no published Canada-wide guidelines describing the practices for workup of pediatric seizures.

A cross-sectional survey was conducted among radiologists at 16 Canadian tertiary pediatric centers to assess neuroimaging practices for children with new-onset seizures. The survey explored the presence and content of dedicated seizure MRI protocols, sedation use, and strategies to facilitate non-sedated MRI.

Fifteen centers (94%) responded. Only 2 (13%) reported using dedicated new-onset seizure protocols, while 10 (67%) used epilepsy-specific MRI protocols, and the others used different approaches, including variations of routine brain MRI. MRI sequences varied across institutions. Sedation use also varied, with a median sedation age range of 3 months to 6 years. Non-sedated MRI techniques such as feed-and-swaddle (93%) and natural sleep (27%) were commonly used for infants. Video goggles (67%) and child life specialist support (53%) were often used for older children. Only 2 institutions (13%) had fast MRI protocols, and virtual reality preparation was uncommon (13%).

Considerable variability exists in MRI protocols and sedation practices across Canadian pediatric centers evaluating new-onset seizures. Our findings emphasize the need for national consensus guidelines to standardize imaging protocols, reduce sedation use, and optimize care for pediatric seizure patients.

MRI often requires general anesthesia in children, which carries risks, increases costs, and prolongs scan wait times.

Our study aimed to evaluate whether virtual reality (VR) simulations could familiarize children with the MRI experience to enable awake scans without anesthesia. Secondary objectives included assessing child anxiety and determining whether movement during the simulation correlated with scan quality.

In this prospective study, 18 participants underwent a 10-minute VR simulation of an MRI procedure presented as an avatar-led game before their head MRI scan. Child and caregiver anxiety surveys were completed before the simulation and after the MRI. The VR software recorded head motion during the simulation, which was correlated with MRI scan quality.

All participants (n = 18) successfully completed an awake MRI after the simulation session, aiding clinical diagnoses. The average participant age was 5.0 years (±1.3 years). MRI quality assessments indicated 44.4% excellent, 27.8% high-acceptable, 22.2% acceptable, and 5.6% low-acceptable scan quality. No statistically significant changes in anxiety levels were observed. 94.1% of legal guardians reported the VR simulation was effective at preparing their child for the MRI scan.

VR sessions were associated with a significant improvement in caregiver perceptions and enabled successful completion of MRI scans without the need for sedation in all children initially considered to require anesthesia. While no statistically significant reduction in anxiety was observed, the intervention resulted in diagnostic-quality imaging with minimal motion artifacts, supporting its utility as a strategy to facilitate pediatric MRI without anesthesia.

Negative appendicectomies in children can be associated with morbidity. MRI has become a radiation-free alternative to CT to assist in the diagnosis of paediatric appendicitis, particularly when ultrasound is equivocal. A systematic review and meta-analysis were performed to assess the diagnostic accuracy of MRI for paediatric appendicitis and its role in reducing negative appendicectomy rates (NAR).

Searches were performed across PubMed, Embase, Cochrane Library, Scopus, Web of Science, for studies from 1 January 2000 to 31 July 2025. The inclusion criteria were studies of children (<18 years) with suspected appendicitis undergoing MRI. Bivariate random-effects meta-analyses were performed. Meta-regression explored the impact of covariates for example, DWI, contrast use, image reviewer blinding, and magnetic field strength.

Twelve studies (n = 3242) met inclusion. Pooled MRI sensitivity was 96.7% (95% CI: 93.4%-98.9%), specificity 97.9% (95% CI: 95.6%-99.3%). MRI NARs ranged from 0.66% to 11.1%, with 3/12 studies reporting NARs <5%. Meta-regression showed non-significant associations between NAR and DWI use (+3.12%, P = .264), MRI contrast use (−5.64%, P = .110), and magnetic field strength (−1.63% per Tesla, P = .361). No significant publication bias was detected (Deeks’ P = .152).

MRI demonstrates high diagnostic accuracy and is associated with low negative appendicectomy rates in children. Its use is particularly effective where ultrasound/clinical findings are inconclusive as MRI overcomes ultrasound limitations, poor appendix visualisation, operator-dependence, and inability to exclude alternate diagnoses. Given its radiation-free nature and CT-like diagnostic performance, wider adoption of MRI within paediatric appendicitis pathways may help avoid surgery and reduce exposure to ionising radiation.