Investigative Radiology最新文献

{"title":"Gadolinium Elimination in a Gadolinium Deposition Disease Population After a Single Exposure to Gadolinium-Based Contrast Agents.","authors":"Joana Ramalho, Miguel Ramalho, Richard C Semelka","doi":"10.1097/RLI.0000000000001146","DOIUrl":"10.1097/RLI.0000000000001146","url":null,"abstract":"<p><strong>Purpose: </strong>This study documents the gadolinium (Gd) content in urine over time after the administration of a single dose of Gd-based contrast agent (GBCA) in patients diagnosed with Gd deposition disease.</p><p><strong>Materials and methods: </strong>In this retrospective observational study, 45 subjects with normal renal function who had performed 1 contrast-enhanced magnetic resonance imaging and had a nonprovoked (native) 24-hour urine test for Gd quantification after the examination were evaluated. The GBCA brand and the time interval in days between the GBCA administration and 24-hour urine Gd measurements were recorded. Log-log plot visualization of time points for urine Gd content was obtained.</p><p><strong>Results: </strong>Time points collected for urine Gd content showed that Gd was above the reference levels for 3 months postinjection. The urinary concentration of Gd was similar for all agents, including linear and macrocyclic. The urinary content decreased in a dog-leg fashion. Gd urine content was substantially elevated at 1 month and decreased to remain above the accepted normal range by 3 months.</p><p><strong>Conclusions: </strong>Gd is retained in the body and shows demonstrable continued spontaneous elimination in urine for at least several months after administration, including the most stable macrocyclic agents. The Gd elimination pattern shows a logarithmic decrease pattern between 1 and 3 months for all agents, regardless of their structure.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"429-433"},"PeriodicalIF":7.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.","authors":"Kang Wang, Matthew J Middione, Andreas M Loening, Ali B Syed, Ariel J Hannum, Xinzeng Wang, Arnaud Guidon, Patricia Lan, Daniel B Ennis, Ryan L Brunsing","doi":"10.1097/RLI.0000000000001148","DOIUrl":"10.1097/RLI.0000000000001148","url":null,"abstract":"<p><strong>Objectives: </strong>Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal. Thus, a method that combines msDWI with MCGs while minimizing the echo time penalty and maximizing signal would improve pancreatic DWI. In this work, we combine MCGs generated via convex-optimized diffusion encoding (CODE), which reduces the echo time penalty of motion compensation, with deep learning (DL)-based denoising to address residual signal loss. We hypothesize this method will qualitatively and quantitatively improve msDWI of the pancreas.</p><p><strong>Materials and methods: </strong>This prospective institutional review board-approved study included 22 patients who underwent abdominal MR examinations from August 22, 2022 and May 17, 2023 on 3.0 T scanners. Following informed consent, 2-shot spin-echo echo-planar DWI (b = 0, 800 s/mm 2 ) without (M0) and with (M1) CODE-generated first-order gradient moment nulling was added to their clinical examinations. DL-based denoising was applied to the M1 images (M1 + DL) off-line. ADC maps were reconstructed for all 3 methods. Blinded pair-wise comparisons of b = 800 s/mm 2 images were done by 3 subspecialist radiologists. Five metrics were compared: pancreatic boundary delineation, motion artifacts, signal homogeneity, perceived noise, and diagnostic preference. Regions of interest of the pancreatic head, body, and tail were drawn, and mean ADC values were computed. Repeated analysis of variance and post hoc pairwise t test with Bonferroni correction were used for comparing mean ADC values. Bland-Altman analysis compared mean ADC values. Reader preferences were tabulated and compared using Wilcoxon signed rank test with Bonferroni correction and Fleiss κ.</p><p><strong>Results: </strong>M1 was significantly preferred over M0 for perceived motion artifacts and signal homogeneity ( P < 0.001). M0 was significantly preferred over M1 for perceived noise ( P < 0.001), but DL-based denoising (M1 + DL) reversed this trend and was significantly favored over M0 ( P < 0.001). ADC measurements from M0 varied between different regions of the pancreas ( P = 0.001), whereas motion correction with M1 and M1 + DL resulted in homogeneous ADC values ( P = 0.24), with values similar to those reported for ssDWI with motion correction. ADC values from M0 were significantly higher than M1 in the head (bias 16.6%; P < 0.0001), body (bias 11.0%","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"444-453"},"PeriodicalIF":7.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Evaluation of 3D Motion-Correction Via Scout Accelerated Motion Estimation and Reduction Framework Versus Conventional T1-Weighted MRI at 1.5 T in Brain Imaging.","authors":"Laura S Leukert, Katya Hoffmannbeck Heitkötter, Andrea Kronfeld, Roman H Paul, Daniel Polak, Daniel Nicolas Splitthoff, Marc A Brockmann, Sebastian Altmann, Ahmed E Othman","doi":"10.1097/RLI.0000000000001156","DOIUrl":"10.1097/RLI.0000000000001156","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to investigate the occurrence of motion artifacts and image quality of brain magnetic resonance imaging (MRI) T1-weighted imaging applying 3D motion correction via the Scout Accelerated Motion Estimation and Reduction (SAMER) framework compared with conventional T1-weighted imaging at 1.5 T.</p><p><strong>Materials and methods: </strong>A preliminary study involving 14 healthy volunteers assessed the impact of the SAMER framework on induced motion during 3 T MRI scans. Participants performed 3 different motion patterns: (1) step up, (2) controlled breathing, and (3) free motion. The patient study included 82 patients who required clinically indicated MRI scans. 3D T1-weighted images (MPRAGE) were acquired at 1.5 T. The MRI data were reconstructed using either regular product reconstruction (non-Moco) or the 3D motion correction SAMER framework (SAMER Moco), resulting in 145 image sequences. For the preliminary and the patient study, 3 experienced radiologists evaluated the image data using a 5-point Likert scale, focusing on overall image quality, artifact presence, diagnostic confidence, delineation of pathology, and image sharpness. Interrater agreement was assessed using Gwet's AC 2 , and an exploratory analysis (non-Moco vs SAMER Moco) was performed.</p><p><strong>Results: </strong>Compared with non-Moco, the preliminary study demonstrated significant improvements across all imaging parameters and motion patterns with SAMER Moco ( P < 0.001). Odds ratios favoring SAMER Moco were >999.999 for freedom of artifact and overall image quality ( P < 0.0001). Excellent or good ratings for freedom of artifact were 52.4% with SAMER Moco, compared with 21.4% for non-Moco. Similarly, 66.7% of SAMER Moco images were rated excellent or good for overall image quality versus 21.4% for non-Moco. Multireader interrater agreement was excellent across all parameters.The patient study confirmed that SAMER Moco provided significantly superior image quality across all evaluated imaging parameters, particularly in the presence of motion ( P < 0.001). Diagnostic confidence was rated as excellent or good in 95.1% of SAMER Moco cases, compared with 78.1% for non-Moco cases. Similarly, overall image quality was rated as excellent or good in 89.8% of SAMER Moco cases versus 65.9% for non-Moco cases. The odds ratios for diagnostic confidence and for overall image quality were 6.698 and 6.030, respectively, both favoring SAMER Moco ( P < 0.0001). Multireader interrater agreement was excellent across all parameters.</p><p><strong>Conclusions: </strong>The application of SAMER in T1-weighted imaging datasets is feasible in clinical routine and significantly increases image quality and diagnostic confidence in 1.5 T brain MRI by effectively reducing motion artifacts.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"478-485"},"PeriodicalIF":7.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.","authors":"Tae Young Lee, Jeong Hee Yoon, Jin Young Park, So Hyun Park, HeeSoo Kim, Chul-Min Lee, Yunhee Choi, Jeong Min Lee","doi":"10.1097/RLI.0000000000001151","DOIUrl":"10.1097/RLI.0000000000001151","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based iterative reconstruction (MBIR) from a single CT using dual-split scan in patients with suspected liver metastasis via a noninferiority design.</p><p><strong>Materials and methods: </strong>This prospective study enrolled participants who met the eligibility criteria at 2 tertiary hospitals in South Korea from June 2022 to January 2023. The criteria included ( a ) being aged between 20 and 85 years and ( b ) having suspected or known liver metastases. Dual-source CT scans were conducted, with the standard radiation dose divided in a 2:1 ratio between tubes A and B (67% and 33%, respectively). The voltage settings of 100/120 kVp were selected based on the participant's body mass index (<30 vs ≥30 kg/m 2 ). For image reconstruction, MBIR was utilized for standard-dose (100%) images, whereas DLR was employed for both low-dose (67%) and ultra-low-dose (33%) images. Three radiologists independently evaluated FLL conspicuity, the probability of metastasis, and subjective image quality using a 5-point Likert scale, in addition to quantitative signal-to-noise and contrast-to-noise ratios. The noninferiority margins were set at -0.5 for conspicuity and -0.1 for detection.</p><p><strong>Results: </strong>One hundred thirty-three participants (male = 58, mean body mass index = 23.0 ± 3.4 kg/m 2 ) were included in the analysis. The low- and ultra-low- dose had a lower radiation dose than the standard-dose (median CT dose index volume: 3.75, 1.87 vs 5.62 mGy, respectively, in the arterial phase; 3.89, 1.95 vs 5.84 in the portal venous phase, P < 0.001 for all). Median FLL conspicuity was lower in the low- and ultra-low-dose scans compared with the standard-dose (3.0 [interquartile range, IQR: 2.0, 4.0], 3.0 [IQR: 1.0, 4.0] vs 3.0 [IQR: 2.0, 4.0] in the arterial phase; 4.0 [IQR: 1.0, 5.0], 3.0 [IQR: 1.0, 4.0] vs 4.0 [IQR: 2.0, 5.0] in the portal venous phases), yet within the noninferiority margin ( P < 0.001 for all). FLL detection was also lower but remained within the margin (lesion detection rate: 0.772 [95% confidence interval, CI: 0.727, 0.812], 0.754 [0.708, 0.795], respectively) compared with the standard-dose (0.810 [95% CI: 0.770, 0.844]). Sensitivity for liver metastasis differed between the standard- (80.6% [95% CI: 76.0, 84.5]), low-, and ultra-low-doses (75.7% [95% CI: 70.2, 80.5], 73.7 [95% CI: 68.3, 78.5], respectively, P < 0.001 for both), whereas specificity was similar ( P > 0.05).</p><p><strong>Conclusions: </strong>Low- and ultra-low-dose CT with DLR showed noninferior FLL conspicuity and detection compared with standard-dose CT with MBIR. Caution is needed due to a potential decrease in sensitivity for metastasis ( clinicaltrials.gov/NCT05324046 ).</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"454-462"},"PeriodicalIF":7.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Different Glymphatic-Lymphatic Coupling in the Nasal Mucosa and Parasagittal Dura.","authors":"Horst Urbach, Murat Kavus, Niklas Lützen, Charlotte Zander, Theo Demerath, Alexander Rau, Katharina Wolf, Jürgen Beck, Ikram Eda Duman","doi":"10.1097/RLI.0000000000001220","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001220","url":null,"abstract":"<p><strong>Background: </strong>Glymphatic-lymphatic coupling is difficult to visualize in humans.</p><p><strong>Purpose: </strong>To evaluate the transport from the basal subarachnoid space to lymphatic vessels in the nasal mucosa and in the parasagittal dura, respectively.</p><p><strong>Methods: </strong>A highly resolved 3D compressed sensing black blood sequence with almost isotropic resolution (0.5 ×0.5 ×0.6 mm3) was acquired in 26 patients before and 2 to 4, 6 to 8, 24 to 48, and 72 to 96 hours after intrathecal injection of 0.5 mL gadobutrol. T1 signal intensities were measured in CSF spaces (perisylvian, above cribriform plate, midsylvian, and parasagittal), in the olfactory bulbs, fila olfactoria, and nasal mucosa, as well as in the cortex, white matter, and parasagittal dura.</p><p><strong>Results: </strong>In the perisylvian CSF, in the CSF above the cribriform plate, in the olfactory bulbs, fila olfactoria, nasal mucosa, and in the cortex, percentage T1 signal intensities showed a rapid increase, peaking at 2 to 4 hours and 6 to 8 hours, respectively. The midsylvian and parasagittal CSF exhibited a slower increase with peak enhancement at 24 to 48 hours. Similarly, in the white matter of the temporal lobe, T1 signal intensities increased gradually, reaching their peak at 24 to 48 hours, followed by a decline after 72 to 96 hours. In the parasagittal dura, T1 signal intensities continued to rise even beyond 72 to 96 hours.</p><p><strong>Conclusions: </strong>Intrathecally injected gadolinium reaches the lymphatic vessels in the nasal mucosa earlier than those in the parasagittal dura. Transport to the nasal mucosa takes place directly via the subarachnoid space. For the transport to the parasagittal dura, findings are compatible with a trans-parenchymal transport route.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies.","authors":"Lavanya Umapathy, Patricia M Johnson, Tarun Dutt, Angela Tong, Sumit Chopra, Daniel K Sodickson, Hersh Chandarana","doi":"10.1097/RLI.0000000000001218","DOIUrl":"10.1097/RLI.0000000000001218","url":null,"abstract":"<p><strong>Objectives: </strong>Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies.</p><p><strong>Materials and methods: </strong>This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases.</p><p><strong>Results: </strong>On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts.</p><p><strong>Conclusions: </strong>Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining compa","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12336996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a Pretrained Artificial Intelligence Model for Pancreatic Cancer Detection on Diagnosis and Prediagnosis Computed Tomography Scans.","authors":"Laura Degand, Clément Abi-Nader, Alexandre Bône, Rebeca Vetil, Davide Placido, Piotr Chmura, Marc-Michel Rohé, Federico De Masi, Søren Brunak","doi":"10.1097/RLI.0000000000001209","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001209","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate PANCANAI, a previously developed AI model for pancreatic cancer (PC) detection, on a longitudinal cohort of patients. In particular, aiming for PC detection on scans acquired before histopathologic diagnosis was assessed.</p><p><strong>Materials and methods: </strong>The model has been previously trained to predict PC suspicion on 2134 portal venous CTs. In this study, the algorithm was evaluated on a retrospective cohort of Danish patients with biopsy-confirmed PC and with CT scans acquired between 2006 and 2016. The sensitivity was measured, and bootstrapping was performed to provide median and 95% CI.</p><p><strong>Results: </strong>The study included 1083 PC patients (mean age: 69 y ± 11, 575 men). CT scans were divided into 2 groups: (1) concurrent diagnosis (CD): 1022 CT scans acquired within 2 months around histopathologic diagnosis, and (2) prediagnosis (PD): 198 CT scans acquired before histopathologic diagnosis (median 7 months before diagnosis). The sensitivity was 91.8% (938 of 1022; 95% CI: 89.9-93.5) and 68.7% (137 of 198; 95% CI: 62.1-75.3) on the CD and PD groups, respectively. Sensitivity on CT scans acquired 1 year or more before diagnosis was 53.9% (36 of 67; 95% CI: 41.8-65.7). Sensitivity on CT scans acquired at stage I was 82.9% (29 of 35; 95% CI: 68.6-94.3).</p><p><strong>Conclusion: </strong>PANCANAI showed high sensitivity for automatic PC detection on a large retrospective cohort of biopsy-confirmed patients. PC suspicion was detected in more than half of the CT scans that were acquired at least a year before histopathologic diagnosis.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144475251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal Pancreaticobiliary Junction in Children: Frequency on Magnetic Resonance Cholangiopancreatography and Associated Pancreaticobiliary Diseases.","authors":"Khuld A Saeedi, Govind B Chavhan, Tanja Gonska, Vicky L Ng, Blayne A Sayed, Caroline Rutten","doi":"10.1097/RLI.0000000000001217","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001217","url":null,"abstract":"<p><strong>Background and aims: </strong>There is scarcity of data on the prevalence of abnormal pancreaticobiliary junction (APBJ) in children. This study aimed to determine the frequency and clinical significance of APBJ including pancreas divisum (PD) and common channel (CC) using magnetic resonance cholangiopancreatography (MRCP).</p><p><strong>Materials and methods: </strong>Single-center, retrospective study of MRCPs in children aged 0 to 18 years from 2012 to 2022. Two independent readers assessed PBJ visibility, abnormalities (PD, CC, other), and CC length. Findings were correlated with presenting diagnoses of choledochal cyst, biliary lithiasis, and pancreatitis.</p><p><strong>Results: </strong>A total of 631 MRCPs were included (46.8% females; mean age: 12 ± 5 y). The PBJ was visible in 85.7% of cases. APBJ was observed in 114/631 (18.1%) children, with PD in 47 (7.4%) cases and CC in 61 (9.7%) cases, with an average length of 9 mm (range, 3 to 22 mm). There was a significant inverse association between PD and biliary lithiasis (P = 0.02). There was no association between PD and pancreatitis. CC was significantly associated with choledochal cyst (P < 0.0001), pancreatitis (P = 0.004) and biliary lithiasis (P < 0.0001), with 21/61 (34.4%) of CC demonstrating stones within (median age: 3.8 y). The CC length was also significantly associated with complications (P = 0.014), with complication-free cases having a median length of 7 mm (range, 4 to 8) compared with 11 mm (range, 3 to 25) in complicated cases.</p><p><strong>Conclusion: </strong>APBJ is a frequent finding on pediatric MRCP. CC is significantly associated with choledochal cyst, pancreatitis and lithiasis, and may show stones within them, particularly in small children. PD is inversely associated with biliary lithiasis. Careful PBJ assessment is important in children.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144302063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Free-Breathing Hybrid Technique for Simultaneous Morphological and Quantitative Abdominal Imaging at 0.55 T.","authors":"Mahesh B Keerthivasan, Mary Bruno, Eddy Solomon, Ryan Brown, Douglas Brantner, Kai Tobias Block, Hersh Chandarana","doi":"10.1097/RLI.0000000000001214","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001214","url":null,"abstract":"<p><strong>Objectives: </strong>Quantitative proton density fat fraction (PDFF) and R2* estimation at lower field strengths, such as 0.55 T, is challenging due to lower signal-to-noise ratio, reduced fat water chemical shift, and increased T2* relaxation times. In this study, we propose a 3D hybrid technique for abdominal imaging at 0.55 T that enables the simultaneous acquisition of T2-weighted and T1-weighted images and quantification of fat fraction and R2* parameters.</p><p><strong>Materials and methods: </strong>Numerical simulations were performed to optimize a prototype radial hybrid turbo spin echo gradient echo (TSE-GRE) acquisition scheme for improved PDFF and R2* estimation accuracy. Phantom imaging experiments with and without motion were performed to evaluate the sensitivity of the estimation to external motion. Eleven volunteers were imaged on a prototype 0.55 T system. Data were acquired using the proposed technique under free-breathing conditions, and motion-compensated reconstruction was performed using the respiratory signal from a pilot-tone device. Image contrast and estimation performance were compared with conventional acquisition schemes in vitro and in vivo.</p><p><strong>Results: </strong>Numerical simulations indicated R2* estimation accuracy was more sensitive to the choice of echo time compared with PDFF. Performing motion compensation reduced the mean error in R2* from 24 to 5 s-1 while the mean error in PDFF only reduced from 2.7% to 1.6%. The proposed technique generated T2-weighted images with comparable relative liver-spleen contrast as conventional imaging and there were no significant differences (P>0.05) in the PDFF and R2* values estimated from the hybrid technique compared with conventional multi-echo GRE. Further, the free-breathing acquisition allowed improved slice coverage while overcoming breath-hold limitations of conventional acquisition schemes.</p><p><strong>Conclusions: </strong>The use of a hybrid TSE-GRE acquisition technique can allow simultaneous morphological and quantitative PDFF and R2* estimation at 0.55 T under free-breathing conditions.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144284371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Decade of Advancements in Musculoskeletal Imaging.","authors":"Paul Wojack, Jan Fritz, Iman Khodarahmi","doi":"10.1097/RLI.0000000000001207","DOIUrl":"10.1097/RLI.0000000000001207","url":null,"abstract":"<p><p>The past decade has witnessed remarkable advancements in musculoskeletal radiology, driven by increasing demand for medical imaging and rapid technological innovations. Contrary to early concerns about artificial intelligence (AI) replacing radiologists, AI has instead enhanced imaging capabilities, aiding in automated abnormality detection and workflow efficiency. MRI has benefited from acceleration techniques that significantly reduce scan times while maintaining high-quality imaging. In addition, novel MRI methodologies now support precise anatomic and quantitative imaging across a broad spectrum of field strengths. In CT, dual-energy and photon-counting technologies have expanded diagnostic possibilities for musculoskeletal applications. This review explores these key developments, examining their impact on clinical practice and the future trajectory of musculoskeletal radiology.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}