Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献

{"title":"Enhancing Environmental Sustainability in Diagnostic Radiology: Focus on CT, MRI, and Nuclear Medicine.","authors":"David A Leswick, Roshini Kulanthaivelu, Hasan Jamil, Chloe L Nguyen, Omer Munir, Seyed Ali Mirshahvalad, Omar Islam","doi":"10.1177/08465371251327143","DOIUrl":"10.1177/08465371251327143","url":null,"abstract":"<p><p>Medical imaging, including MRI, CT, and nuclear medicine play a critical role in healthcare but also imposes significant environmental burdens due to high energy consumption and waste production. Of the diagnostic modalities, MRI is the most energy-intensive modality, consuming up to 60 kWh per scan, followed by CT, which ranges from 1.0 to 11.4 kWh per scan. Lifecycle analyses show that operational energy use far exceeds manufacturing emissions, highlighting the need for energy-saving strategies. Implementing standby and power-off modes, optimizing scan protocols, and using AI-driven efficiency improvements can significantly reduce unnecessary energy use. Additionally, sustainable infrastructure, such as variable-flow cooling systems and strategic equipment placement, can further minimize environmental impact. Nuclear medicine, while relatively lower in energy consumption, relies on energy-intensive radioisotope production, often requiring fossil fuel-powered reactors and extensive transport logistics. Contrast agents in MRI and CT pose contamination risks in wastewater, as they are inadequately removed via conventional treatment plants methods. This results in the accumulation of gadolinium and iodinated byproducts in drinking water sources, posing potential human and ecological risks. Nuclear medicine radioisotopes, including Tc-99, also contribute to long-term contamination concerns. Strategies to mitigate these impacts include urine recycling, contrast separation, and advanced wastewater treatment. Sustainable practices in medical imaging require a multi-pronged approach, combining operational efficiency, renewable energy adoption, and stricter waste management protocols. Future efforts may also focus on promoting low-field MRI, AI-driven scan optimization, and alternative contrast agents, ensuring that radiology departments balance diagnostic efficacy with environmental responsibility.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"595-602"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CARJ Outstanding Reviewers Awards for 2025.","authors":"Michael N Patlas","doi":"10.1177/08465371251359853","DOIUrl":"https://doi.org/10.1177/08465371251359853","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":"76 4","pages":"559"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145350133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Contrast Boosting in Low-Contrast Media Pre-TAVR CT Imaging.","authors":"Jeaneun Park, Jung Im Jung, Kyunghwa Han, Suyon Chang","doi":"10.1177/08465371251322054","DOIUrl":"10.1177/08465371251322054","url":null,"abstract":"<p><p><b>Purpose:</b> This study investigates the impact of deep learning-based contrast boosting (DL-CB) on image quality and measurement reliability in low-contrast media (low-CM) CT for pre-transcatheter aortic valve replacement (TAVR) assessment. <b>Methods:</b> This retrospective study included TAVR candidates with renal dysfunction who underwent low-CM (30-mL: 15-mL bolus of contrast followed by 50-mL of 30% iomeprol solution) pre-TAVR CT between April and December 2023, along with matched standard-CM controls (n = 68). Low-CM images were reconstructed as conventional, 50-keV, and DL-CB images. Qualitative and quantitative image quality were compared among image sets. The aortic annulus was measured by 2 independent readers on low-CM CT images, and interobserver reliability was assessed. <b>Results:</b> DL-CB significantly improved contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) compared to conventional and 50-keV images (CNR: 12.5-13.4, 18-19.8, and 21.9-24; SNR: 10.8-15.5, 10.7-15.5, and 16.8-26.7 on conventional, 50-keV, and DL-CB images, respectively; <i>P</i> < .001). DL-CB achieved comparable CNR (21.9-24 vs 27-27.7, <i>P</i> = .39-.61) and comparable to slightly higher SNR (16.8-26.7 vs 15.7-20.2, <i>P</i> = .003-.80) to standard-CM images. For aortic annular measurement, DL-CB demonstrated high interobserver reliability, with an intraclass correlation coefficient (ICC) of .96 and small mean differences (area: 0.01 cm², limits of agreement [LoA]: -0.52 to 0.55 cm²; perimeter: 0.02 mm, LoA: -4.49 to 4.53 mm). <b>Conclusions:</b> DL-CB improves image quality and provides high measurement reliability in low-CM CT for pre-TAVR assessment in patients with renal dysfunction, without requiring dual-energy CT.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"772-781"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging-Based Approach to Venous-Origin Chronic Pelvic Pain.","authors":"Vaneeza Moosa, Tiffany Ni, Inbar Friedman, Andrew D Brown","doi":"10.1177/08465371251336121","DOIUrl":"10.1177/08465371251336121","url":null,"abstract":"<p><p>Chronic pelvic pain (CPP) is a debilitating condition affecting up to 26% of women worldwide. Among its many causes, pelvic venous disorders (PeVD) is increasingly recognized as an underdiagnosed contributor, often overlooked due to its non-specific presentation. PeVD results from venous reflux, or obstruction, leading to venous hypertension, congestion, and chronic pain. Advanced imaging techniques play a pivotal role in diagnosing PeVD, differentiating it from other etiologies of CPP. Ultrasound, particularly Doppler imaging, serves as the firstline modality for assessing venous reflux and dilation. Computed tomography and magnetic resonance venography provide detailed anatomical and haemodynamic evaluations, aiding in the identification of compressive syndromes and collateral pathways. Selective venography remains the gold standard, offering real-time visualization of reflux severity and guiding minimally invasive interventions such as venous embolization. Despite these advances, PeVD remains underrecognized in clinical practice, leading to delays in diagnosis and management. Increased awareness and standardized diagnostic criteria are crucial for improving patient outcomes. A multidisciplinary approach incorporating radiologists, gynecologists, and vascular specialists is essential for the comprehensive evaluation and treatment of PeVD. Emerging therapies, including endovascular techniques, offer promising options for symptom relief, reducing the need for invasive surgical procedures. This review highlights the pathophysiology, imaging modalities, and evolving management strategies for PeVD, emphasizing the importance of early recognition and intervention in patients with CPP.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"782-792"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Current and Future Landscape of Interventional Neuro-Oncology.","authors":"Eef J Hendriks, David Volders","doi":"10.1177/08465371251347833","DOIUrl":"10.1177/08465371251347833","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"800-801"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pelvic Puzzles: Imaging Non-Traumatic Emergencies of the Female Pelvis: A Comprehensive Review.","authors":"Namita Sharma, Kelly Harper, Mary Beth Bissell","doi":"10.1177/08465371251383509","DOIUrl":"https://doi.org/10.1177/08465371251383509","url":null,"abstract":"<p><p>Acute non-traumatic pelvic pain represents a frequent and complex diagnostic dilemma in pre-menopausal females presenting to the emergency department due to overlapping symptomatology across pregnancy and non-pregnancy related causes. Radiologists play a pivotal role in expediting accurate diagnosis and guiding appropriate management in these potentially life-threatening scenarios. This review provides an approach to workup and imaging selection in these patients, emphasizing the necessity of serum β-hCG testing and the central role of transabdominal and transvaginal pelvic ultrasound. An overview of female pelvic anatomy is provided. Using a multimodality imaging approach, early pregnancy related complications such as ectopic pregnancy, retained products of conception, and gestational trophoblastic disease and non-pregnancy related causes of acute pelvic pain such as ovarian torsion, ruptured ovarian cysts, pelvic inflammatory disease, endometriosis, uterine vascular malformation, ovarian vein thrombosis, ovarian hyperstimulation syndrome, and intrauterine device complications are reviewed. Finally, we propose an algorithmic approach to imaging selection and interpretation tailored to the clinical scenario, laboratory findings (notably β-hCG status), and patient demographics. This structured framework aims to support radiologists in efficiently narrowing the differential diagnosis and optimizing patient outcomes in acute, non-traumatic pelvic emergencies.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251383509"},"PeriodicalIF":3.7,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145369266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Japanese Radiology 2025 Updates.","authors":"Mami Iima, Tsukasa Saida, Yoshitake Yamada, Ryo Kurokawa, Daiju Ueda, Maya Honda, Kentaro Nishioka, Rintaro Ito, Keitaro Sofue, Shinji Naganawa","doi":"10.1177/08465371251374557","DOIUrl":"https://doi.org/10.1177/08465371251374557","url":null,"abstract":"<p><p>This review provides a comprehensive overview of recent transformative advancements in diagnostic imaging that position Japan at the forefront of radiological innovation. We highlight pivotal innovations that enhance diagnostic capabilities and redefine clinical workflows. The article begins with upright multidetector computed tomography (MDCT), a groundbreaking technology offering novel insights into posture-dependent anatomical and physiological changes. We then explore significant progress in breast and gynecologic imaging, including advancements in artificial intelligence computer-aided (AI-CAD) synthesized mammograms, automated breast ultrasound (ABUS), and abbreviated MRI protocols. These innovations address unique regional challenges in early cancer detection. Significant innovations in abdominal radiology, spanning advanced CT (including photon-counting detector CT), accelerated MRI, and AI applications, are also discussed. The review further delves into glymphatic system research, where advanced MRI techniques, particularly DTI-ALPS, are unraveling new insights into brain waste clearance and neurological disorders. Finally, we examine the future of Japanese radiology through the lens of AI, with a focus on Large Language Models (LLMs). We discuss their growing role in diagnostic support, report generation, and information extraction, as well as important societal and ethical considerations. These collective advancements underscore Japan's dynamic contributions to radiological innovation, poised to significantly impact global healthcare practices by improving disease detection, optimizing workflows, and extending healthy life expectancy in an aging society.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251374557"},"PeriodicalIF":3.7,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Burnout in the Canadian Radiology Trainee.","authors":"Aaditeya Jhaveri, Michael N Patlas","doi":"10.1177/08465371251375874","DOIUrl":"https://doi.org/10.1177/08465371251375874","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251375874"},"PeriodicalIF":3.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Screening Tool Based on X-Rays Improves Early Detection of Decreased Bone Density in a Clinical Setting.","authors":"Andrew N Jayarajah, Angela Atinga, Linda Probyn, Thiru Sivakumaran, Monique Christakis, Anastasia Oikonomou","doi":"10.1177/08465371251380240","DOIUrl":"https://doi.org/10.1177/08465371251380240","url":null,"abstract":"<p><strong>Purpose: </strong>Osteoporosis is an under-screened musculoskeletal disorder that results in diminished quality of life and significant burden to the healthcare system. We aimed to evaluate the ability of Rho, an artificial intelligence (AI) tool, to prospectively identify patients at-risk for low bone mineral density (BMD) from standard x-rays, its adoption rate by radiologists, and acceptance by primary care providers (PCPs).</p><p><strong>Methods: </strong>Patients ≥50 years were recruited when undergoing an x-ray of a Rho-eligible body part for any clinical indication. Questionnaires were completed at baseline and 6-month follow-up, and PCPs of \"Rho-Positive\" patients (those likely to have low BMD) were asked for feedback. Positive predictive value (PPV) was calculated in patients who returned within 6 months for a DXA.</p><p><strong>Results: </strong>Of 1145 patients consented, 987 had x-rays screened by Rho, and 655 were flagged as Rho-Positive. Radiologists included this finding in 524 (80%) of reports. Of all Rho-Positive patients, 125 had a DXA within 6 months; Rho had a 74% PPV for DXA T-Score <-1. From 51 PCP responses, 78% found Rho beneficial. Of 389 patients with follow-up questionnaire data, a greater proportion of Rho-Positive versus -negative patients had discussed bone health with their PCP since study start (36% vs 18%, <i>P</i> < .001), or were newly diagnosed with osteoporosis (11% vs 5%; <i>P</i> = .03).</p><p><strong>Conclusion: </strong>By identifying patients at-risk of low BMD, with acceptability of reporting by radiologists and generally positive feedback from PCPs, Rho has the potential to improve low screening rates for osteoporosis by leveraging existing x-ray data.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251380240"},"PeriodicalIF":3.7,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing Model Generalization With Local Performance: Insights From AI in Prostate Cancer Classification.","authors":"Emir A Syailendra, Zahra Rahmatullah, Felipe Lopez-Ramirez, Linda C Chu","doi":"10.1177/08465371251377467","DOIUrl":"https://doi.org/10.1177/08465371251377467","url":null,"abstract":"","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251377467"},"PeriodicalIF":3.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}