Radiology advances最新文献

{"title":"Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients.","authors":"Nelson Gil, Azadeh Tabari, Dominik Nickel, Wei-Ching Lo, Bryan Clifford, Stephen Cauley, Min Lang, Sittaya Buathong, Azadeh Hajati, Shohei Fujita, Seonghwan Yee, John Conklin, Susie Huang","doi":"10.1093/radadv/umaf022","DOIUrl":"10.1093/radadv/umaf022","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate a physics-based deep-learning-accelerated super-resolution T1-weighted MPRAGE sequence (DL-MPRAGE) against standard 3-dimensional T1-weighted MPRAGE (STD-MPRAGE) for quantitative and qualitative regional cortical volume assessment.</p><p><strong>Materials and methods: </strong>This prospective single-center study included patients undergoing evaluation for memory loss on 3T MRI scanners (MAGNETOM Vida, Siemens Healthineers, Forchheim, Germany) from October 2023 to January 2024. The absolute symmetrized percent change in cortical volume and thickness was assessed on DL- and STD-MPRAGE images using the FreeSurfer brain segmentation algorithm. Bland-Altman analysis evaluated the agreement in volumetrics for each anatomical region. Additionally, 2 blinded radiologists independently qualitatively rated image quality metrics and cortical volume loss for anatomical regions based on standardized scales.</p><p><strong>Results: </strong>A total of 64 participants (29 women [45%], mean age 62 years ±16 [SD]) were evaluated. DL-MPRAGE increased spatial resolution from 1 mm to 0.5 mm while reducing scan time by more than half (2:11 vs. 5:21). Mean regional volumes for DL-MPRAGE were systematically lower than for STD-MPRAGE (eg, 17 226 ± 2011 vs. 17 923 ± 2185 mm³, corresponding to an absolute difference between the means of 697 mm³, for the cingulate gyrus, <i>P < </i>.004). Corresponding absolute symmetrized percent change values averaged 2.8% across brain regions, with the largest mean value being 5.08% for the cingulate gyrus. Bland-Altman analysis demonstrated high agreement in quantitative measurements for both volume and thickness. On reader assessment, DL-MPRAGE was noninferior to STD-MPRAGE across image quality metrics (<i>P < </i>.01) and equivalent in assessing volume loss.</p><p><strong>Conclusions: </strong>DL-MPRAGE offers quantitatively and qualitatively equivalent volumetric estimation compared to STD-MPRAGE while improving spatial resolution and acquisition speed for patients undergoing evaluation for memory loss.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 4","pages":"umaf022"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.","authors":"Natalia Konovalova, Aniket Tolpadi, Felix Liu, Zehra Akkaya, Johanna Luitjens, Felix Gassert, Paula Giesler, Rupsa Bhattacharjee, Misung Han, Emma Bahroos, Sharmila Majumdar, Valentina Pedoia","doi":"10.1093/radadv/umaf015","DOIUrl":"https://doi.org/10.1093/radadv/umaf015","url":null,"abstract":"<p><strong>Background: </strong>Accurate interpretation of meniscal anomalies on knee MRI is critical for diagnosis and treatment planning, with artificial intelligence emerging as a promising tool to support and enhance this process through automated anomaly detection.</p><p><strong>Purpose: </strong>To evaluate the impact of an artificial intelligence (AI) anomaly detection assistant on radiologists' interpretation of meniscal anomalies in undersampled, deep learning (DL)-reconstructed knee MRI and assess the relationship between reconstruction quality metrics and anomaly detection performance.</p><p><strong>Materials and methods: </strong>This retrospective study included 947 knee MRI examinations; 51 were excluded for poor image quality, leaving 896 participants (mean age, 44.7 ± 15.3 years; 472 women). Using 8-fold undersampled data, DL-based reconstructed images were generated. An object detection model was trained on original, fully sampled images and evaluated on 1 original and 14 DL-reconstructed test sets to identify meniscal lesions. Standard reconstruction metrics (normalized root mean square error, peak signal-to-noise ratio, and structural similarity index) and anomaly detection metrics (mean average precision, F1 score) were quantified and compared. Two radiologists independently reviewed a stratified sample of 50 examinations unassisted and assisted with AI-predicted anomaly boxes. McNemar's test evaluated differences in diagnostic performance; Cohen's kappa assessed interrater agreement.</p><p><strong>Results: </strong>On the original images, the anomaly detection model achieved the following: 70.53% precision, 72.17% recall, 63.09% mAP, and a 71.34% F1 score. Comparing performance among the undersampled reconstruction datasets, box-based reconstruction metrics showed better correlation with detection performance than traditional image-based metrics (mAP to box-based SSIM, <i>r</i> = 0.81, <i>P</i> < .01; mAP to image-based SSIM, <i>r</i> = 0.64, <i>P</i> = .01). In 50 participants, AI assistance improved radiologists' accuracy on reconstructed images. Sensitivity increased from 77.27% (95% CI, 65.83-85.72; 51/66) to 80.30% (95% CI, 69.16-88.11; 53/66), and specificity improved from 88.46% (95% CI, 83.73-91.95; 207/234) to 90.60% (95% CI, 86.18-93.71; 212/234) (<i>P</i> < .05).</p><p><strong>Conclusion: </strong>AI-assisted meniscal anomaly detection enhanced radiologists' interpretation of undersampled, DL-reconstructed knee MRI. Anomaly detection may serve as a complementary tool alongside other reconstruction metrics to assess the preservation of clinically important features in reconstructed images, warranting further investigation.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 2","pages":"umaf015"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12021832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing quantitative imaging biomarker alliance volumetric CT classifications with RECIST response categories.","authors":"Binsheng Zhao, Nancy Obuchowski, Hao Yang, Yen Chou, Hong Ma, Pingzhen Guo, Ying Tang, Lawrence Schwartz, Daniel Sullivan","doi":"10.1093/radadv/umaf001","DOIUrl":"10.1093/radadv/umaf001","url":null,"abstract":"<p><strong>Purpose: </strong>To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories.</p><p><strong>Materials and methods: </strong>Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools. Two types of predefined disagreements were assessed: Type I: substantive disagreement, where the disagreement between QIBA CTvol classifications and RECIST categories could not be attributed to the improved sensitivity of volumetry in detecting changes; and Type II: disagreement potentially arising from the improved sensitivity of volumetry in detecting changes. The proportion of lesions with disagreements between QIBA CTvol and RECIST, as well as the type of disagreements, was reported along with 95% CIs, both overall and within subgroups representing various factors.</p><p><strong>Results: </strong>A total of 2390 measurements from 478 lesions (158 lungs, 170 livers, 150 lymph nodes) in 281 patients were included. QIBA CTvol agreed with RECIST in 66.6% of interpretations. Of the 33.4% of interpretations with discrepancies, substantive disagreement (Type I) occurred in only 1.5% (95% CI: [0.8%, 2.1%]). Factors such as scanner vendor (<i>P</i> = .584), segmentation tool (<i>P</i> = .331), and lesion type (<i>P</i> = .492) were not significant predictors of disagreement. Significantly more disagreements were observed for larger lesions (≥50 mm, as defined in the QIBA Profile).</p><p><strong>Conclusion: </strong>We conclude that QIBA CTvol classifications agree with RECIST categories.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 1","pages":"umaf001"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease.","authors":"Aaron So, Ki Seok Choo, Ji Won Lee, Yun-Hyeon Kim, Mustafa Haider, Mahmud Hasan, Serag El-Ganga, Akshaye Goela, Patrick Teefy, Yeon Hyeon Choe","doi":"10.1093/radadv/umae031","DOIUrl":"10.1093/radadv/umae031","url":null,"abstract":"<p><strong>Purposes: </strong>The objective was to evaluate the accuracy of a novel CT dynamic angiographic imaging (CT-DAI) algorithm for rapid fractional flow reserve (FFR) measurement in patients with coronary artery disease (CAD).</p><p><strong>Materials and methods: </strong>This retrospective study included 14 patients (age 58.5 ± 10.6 years, 11 males) with CAD who underwent stress dynamic CT myocardial perfusion scanning with a dual-source CT scanner. The included patients had analyzable proximal and distal coronary artery segments adjacent to the stenosis in the perfusion images and had corresponding invasive catheter-based FFR measurements for that stenosis. An in-house software based on the CT-DAI algorithm was used to compute FFR using the pre- and post- lesion coronary time-enhancement curves obtained from the stress myocardial perfusion images. The CT-DAI derived FFR values were then compared to the corresponding catheter-based invasive FFR values. A coronary artery stenosis was considered functionally significant for FFR value <0.8.</p><p><strong>Results: </strong>The CT-DAI derived FFR values were in agreement with the invasive FFR values in all 15 coronary arteries in 14 patients, resulting in 100% per-vessel and per-patient diagnostic accuracy. FFR derived using CT-DAI (<i>M</i> = 0.768, SD = 0.156) showed an excellent linear correlation (<i>R</i> = 0.910, <i>P</i> < .001) and statistical indifference (<i>P</i>= .655) with that measured using invasive catheter-based method (<i>M</i> = 0.796, SD = 0.149). Bland-Altman analysis showed no significant proportional bias.</p><p><strong>Conclusion: </strong>The novel CT-DAI algorithm can reliably compute FFR across a coronary artery stenosis directly from dynamic CT myocardial perfusion images, facilitating rapid on-site hemodynamic assessment of the epicardial coronary artery stenosis in patients with CAD.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 4","pages":"umae031"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating time-to-total knee replacement on radiographs and MRI: a multimodal approach using self-supervised deep learning.","authors":"Ozkan Cigdem, Shengjia Chen, Chaojie Zhang, Kyunghyun Cho, Richard Kijowski, Cem M Deniz","doi":"10.1093/radadv/umae030","DOIUrl":"10.1093/radadv/umae030","url":null,"abstract":"<p><strong>Purpose: </strong>Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions. A survival analysis model for predicting time-to-TKR was developed using features from medical images and clinical measurements.</p><p><strong>Methods: </strong>From the Osteoarthritis Initiative dataset, all knees with clinical variables, MRI scans, radiographs, and quantitative and semiquantitative assessments from images were identified. This resulted in 895 knees that underwent TKR within the 9-year follow-up period, as specified by the Osteoarthritis Initiative study design, and 786 control knees that did not undergo TKR (right-censored, indicating their status beyond the 9-year follow-up is unknown). These knees were used for model training and testing. Additionally, 518 and 164 subjects from the Multi-Center Osteoarthritis Study and internal hospital data were used for external testing, respectively. Deep learning models were utilized to extract features from radiographs and MR scans. Extracted features, clinical variables, and image assessments were used in survival analysis with Lasso Cox feature selection and a random survival forest model to predict time-to-TKR.</p><p><strong>Results: </strong>The proposed model exhibited strong discrimination power by integrating self-supervised deep learning features with clinical variables (eg, age, body mass index, pain score) and image assessment measurements (eg, Kellgren-Lawrence grade, joint space narrowing, bone marrow lesion size, cartilage morphology) from multiple modalities. The model achieved an area under the curve of 94.5 (95% CI, 94.0-95.1) for predicting the time-to-TKR.</p><p><strong>Conclusions: </strong>The proposed model demonstrated the potential of self-supervised learning and multimodal data fusion in accurately predicting time-to-TKR that may assist physicians to develop personalize treatment strategies.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 4","pages":"umae030"},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11687945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of myocardial oxygen extraction fraction on noncontrast MRI enabled by deep learning.","authors":"Ran Li, Cihat Eldeniz, Keyan Wang, Natalie Nguyen, Thomas H Schindler, Qi Huang, Linda R Peterson, Yang Yang, Yan Yan, Jingliang Cheng, Pamela K Woodard, Jie Zheng","doi":"10.1093/radadv/umae026","DOIUrl":"10.1093/radadv/umae026","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a new deep learning enabled cardiovascular magnetic resonance (CMR) approach for noncontrast quantification of myocardial oxygen extraction fraction (mOEF) and myocardial blood volume (MBV) in vivo.</p><p><strong>Materials and methods: </strong>An asymmetric spin-echo prepared CMR sequence was created in a 3 T MRI clinical system. A UNet-based fully connected neural network was developed based on a theoretical model of CMR signals to calculate mOEF and MBV. Twenty healthy volunteers (20-30 years old, 11 females) underwent CMR scans at 3 short-axial slices (16 myocardial segments) on 2 different days. The reproducibility was assessed by the coefficient of variation. Ten patients with chronic myocardial infarction were examined to evaluate the feasibility of this CMR method to detect abnormality of mOEF and MBV.</p><p><strong>Results: </strong>Among the volunteers, the average global mOEF and MBV on both days was 0.58 ± 0.07 and 9.5% ± 1.5%, respectively, which agreed well with data measured by other imaging modalities. The coefficient of variation of mOEF was 8.4%, 4.5%, and 2.6%, on a basis of segment, slice, and participant, respectively. No significant difference in mOEF was shown among 3 slices or among different myocardial segments. Female participants showed significantly higher segmental mOEF than male participants (<i>P</i> < .001). Regional mOEF decrease 40% in CMR-confirmed myocardial infarction core, compared to normal myocardial regions.</p><p><strong>Conclusion: </strong>The new deep learning-enabled CMR approach allows noncontrast quantification of mOEF and MBV with good to excellent reproducibility. This technique could provide an objective contrast-free means to assess and serially measure hypoxia-relief effects of therapeutic interventional strategies to save viable myocardial tissues.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic particle imaging enables nonradioactive quantitative sentinel lymph node identification: feasibility proof in murine models.","authors":"Olivia C Sehl, Kelvin Guo, Abdul Rahman Mohtasebzadeh, Petrina Kim, Benjamin Fellows, Marcela Weyhmiller, Patrick W Goodwill, Max Wintermark, Stephen Y Lai, Paula J Foster, Joan M Greve","doi":"10.1093/radadv/umae024","DOIUrl":"10.1093/radadv/umae024","url":null,"abstract":"<p><strong>Background: </strong>Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic staging procedure. Conventional SLNB procedures with ^99mTc radiotracers and scintigraphy are constrained by tracer half-life and, in some cases, insufficient image resolution. Here, we explore an alternative magnetic (nonradioactive) image-guided SLNB procedure.</p><p><strong>Purpose: </strong>To demonstrate that magnetic particle imaging (MPI) lymphography can sensitively, specifically, and quantitatively identify and map sentinel lymph modes (SLNs) in murine models in multiple regional lymphatic basins.</p><p><strong>Materials and methods: </strong>Iron oxide nanoparticles were administered intradermally to healthy C57BL/6 mice (male, 12-week-old, n = 5). The nanoparticles (0.675 mg Fe/kg) were injected into the tongue, forepaw, base of tail, or hind footpad, then detected by 3-dimensional MPI at multiple timepoints between 1 hour and 4 to 6 days. In this mouse model, the SLN is represented by the first lymph node draining from the injection site. SLNs were extracted to verify the MPI signal ex vivo and processed using Perl's Prussian iron staining. Paired <i>t</i>-test was conducted to compare MPI signal from SLNs in vivo vs. ex vivo and considered significant if <i>P</i> < .05.</p><p><strong>Results: </strong>MPI lymphography identified SLNs in multiple lymphatic pathways, including the cervical SLN draining the tongue, axillary SLN draining the forepaw, inguinal SLN draining the tail, and popliteal SLN draining the footpad. MPI signal in lymph nodes was present after 1 hour and stable for the duration of the study (4-6 days). Perl's Prussian iron staining was identified in the subcapsular space of excised SLNs.</p><p><strong>Conclusion: </strong>Our data support the use of MPI lymphography to specifically detect SLN(s) using a magnetic tracer for a minimum of 4 to 6 days, thereby providing information required to plan the SLN approach in cancer surgery. As clinical-scale MPI is developed, translation will benefit from a history of using iron-oxide nanoparticles in human imaging and recent regulatory-approvals for use in SLNB.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 3","pages":"umae024"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Different sodium concentrations of noncancerous and cancerous prostate tissue seen on MRI using an external coil.","authors":"Josephine L Tan, Vibhuti Kalia, Stephen E Pautler, Glenn Bauman, Lena V Gast, Max Müller, Armin M Nagel, Jonathan D Thiessen, Timothy J Scholl, Alireza Akbari","doi":"10.1093/radadv/umae023","DOIUrl":"10.1093/radadv/umae023","url":null,"abstract":"<p><strong>Background: </strong>Sodium (²³Na) MRI of prostate cancer (PCa) is a novel but underdocumented technique conventionally acquired using an endorectal coil. These endorectal coils are associated with challenges (e.g., a nonuniform sensitivity profile, limited prostate coverage, patient discomfort) that could be mitigated with an external ²³Na MRI coil.</p><p><strong>Purpose: </strong>To quantify tissue sodium concentration (TSC) differences within the prostate of participants with PCa and healthy volunteers using an external ²³Na MRI radiofrequency coil at 3 T.</p><p><strong>Materials and methods: </strong>A prospective study was conducted from January 2022 to June 2024 in healthy volunteers and participants with biopsy-proven PCa. Prostate ²³Na MRI was acquired on a 3-T PET/MRI scanner using a custom-built 2-loop (diameter, 18 cm) butterfly surface coil tuned for the ²³Na frequency (32.6 MHz). The percent difference in TSC (ΔTSC) between prostate cancer lesions and surrounding noncancerous prostate tissue of the peripheral zone (PZ) and transition zone (TZ) was evaluated using a 1-sample <i>t</i>-test. TSC was compared to apparent diffusion coefficient (ADC) measurements as a clinical reference.</p><p><strong>Results: </strong>Six healthy volunteers (mean age, 54.5 years ± 12.7) and 20 participants with PCa (mean age, 70.7 years ± 8.3) were evaluated. A total of 31 lesions were detected (21 PZ, 10 TZ) across PCa participants. Compared to noncancerous prostate tissue, prostate cancer lesions had significantly lower TSC (ΔTSC, -14.1% ± 18.2, <i>P</i> = .0002) and ADC (ΔADC, -26.6% ± 18.7, <i>P</i> < .0001).</p><p><strong>Conclusion: </strong>We used an external ²³Na MRI coil for whole-gland comparison of TSC in PCa and noncancerous prostate tissue at 3 T. PCa lesions presented with lower TSC compared to surrounding noncancerous PZ and TZ tissue. These findings demonstrate the feasibility of an external ²³Na MRI coil to quantify TSC in the prostate and offer a promising, noninvasive approach to PCa diagnosis and management.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 3","pages":"umae023"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11578593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platelet-Rich Plasma for Patellar Tendinopathy: A randomized controlled trial correlating clinical outcomes and quantitative imaging","authors":"R. A. van der Heijden, Zachary Stewart, Robert Moskwa, Fang Liu, John Wilson, Scott J Hetzel, D. Thelen, Bryan C Heiderscheit, Richard Kijowski, Kenneth Lee","doi":"10.1093/radadv/umae017","DOIUrl":"https://doi.org/10.1093/radadv/umae017","url":null,"abstract":"\u0000 \u0000 \u0000 Patellar tendinopathy (PT) is a common overuse injury in active individuals, often with incomplete recovery. Recently, platelet-rich plasma (PRP) treatment has shown promising results. Traditional qualitative markers are not reliable indicators of treatment response. Advanced quantitative imaging, such as Ultrashort-TE (UTE) MRI and ultrasound (US) shear-wave elastography (SWE) may be valuable adjuncts.\u0000 \u0000 \u0000 \u0000 To investigate the clinical outcomes and quantitative imaging changes in adults with symptomatic patellar tendinopathy treated with PRP, needle tenotomy (NT) or sham injection (SH).\u0000 \u0000 \u0000 \u0000 Single-blinded prospective randomized controlled trial from April 2017 until July 2022 with three parallel interventions in athletes with symptomatic PT: PRP, NT and SH. VAS pain, VISA-P function, conventional US, shear wave speed (SWS), UTE T2* relaxation time (T2*single) and T2* fraction of fast-relaxing macromolecular-bound water (FF) were acquired at 0, 16 and 52-weeks. Longitudinal analyses were used to compare intra- and inter-group differences over time. Correlations were assessed by Pearson’s correlation coefficient.\u0000 \u0000 \u0000 \u0000 29 subjects (mean age, 26.1±5.3 years; 82.8% men) were randomized. At 52-weeks all groups demonstrated a significant improvement in pain, though most pronounced within the PRP group (ΔVAS=-5.9, 95% confidence interval (CI) [-7.8, -3.9], p<.001). SWS increased significantly only in the PRP group (Δ+2.3, [0.8, 3.9], p=.003). Change in SWS was moderately correlated with change in pain across all groups (r=-.52, [-.76, -.15], p=.009). FF significantly increased in all groups (Δ=0.10-0.11, p=.024-0.046); a significant decrease in T2*single was only seen in the PRP group (Δ=-8.07, [-14.6, -1.55], p=.014).\u0000 \u0000 \u0000 \u0000 Clinical improvement was evident irrespective of treatment but was greatest with PRP. SWS correlated with improvement in pain and may represent an adjunctive measure to assess healing in patellar tendinopathy. Correlative changes in T2* UTE quantitative markers suggest their potential for response assessment, but further research is needed to clarify their clinical applicability.\u0000","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":" 652","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Generative Model of the Distal Tibial Classic Metaphyseal Lesion in Infants: Assessment of Synthetic Images","authors":"Shaoju Wu, Sila Kurugol, Paul K Kleinman, Kirsten Ecklund, Michele Walters, Susan A Connolly, Patrick Johnston, Andy Tsai","doi":"10.1093/radadv/umae018","DOIUrl":"https://doi.org/10.1093/radadv/umae018","url":null,"abstract":"\u0000 \u0000 \u0000 The classic metaphyseal lesion (CML) is a distinctive fracture highly specific to infant abuse. To increase the size and diversity of the training CML database for automated deep-learning detection of this fracture, we developed a mask conditional diffusion model (MaC-DM) to generate synthetic images with and without CMLs.\u0000 \u0000 \u0000 \u0000 To objectively and subjectively assess the synthetic radiographic images with and without CMLs generated by MaC-DM.\u0000 \u0000 \u0000 \u0000 For retrospective testing, we randomly chose 100 real images (50 normals and 50 with CMLs; 39 infants, male = 22, female = 17; mean age = 4.1 months; SD = 3.1 months) from an existing distal tibia dataset (177 normal, 73 with CMLs), and generated 100 synthetic distal tibia images via MaC-DM (50 normals and 50 with CMLs). These test images were shown to three blinded radiologists. In the 1st session, radiologists determined if the images were normal or had CMLs. In the 2nd session, they determined if the images were real or synthetic. We analyzed the radiologists’ interpretations, and employed t-distributed stochastic neighbor embedding (t-SNE) technique to analyze the data distribution of the test images.\u0000 \u0000 \u0000 \u0000 When presented with the 200 images (100 synthetic, 100 with CMLs), radiologists reliably and accurately diagnosed CMLs (kappa = 0.90, 95% CI = [0.88, 0.92]; accuracy = 92%, 95% CI = [89%, 97%]). However, they were inaccurate in differentiating between real and synthetic images (kappa = 0.05, 95% CI = [0.03, 0.07]; accuracy = 53%, 95% CI = [49%, 59%]). The t-SNE analysis showed substantial differences in the data distribution between normal images and those with CMLs (AUC = 0.996, 95% CI = [0.992, 1.000], P < 0.01), but minor differences between real and synthetic images (AUC = 0.566, 95% CI = [0.486, 0.647], P = 0.11).\u0000 \u0000 \u0000 \u0000 Radiologists accurately diagnosed images with distal tibial CMLs but were unable to distinguish real from synthetically generated ones, indicating that our generative model could synthesize realistic images. Thus, MaC-DM holds promise as an effective strategy for data augmentation in training machine-learning models for diagnosis of distal tibial CMLs.\u0000","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}