Magnetic resonance imaging最新文献

{"title":"A lightweight adaptive spatial channel attention efficient net B3 based generative adversarial network approach for MR image reconstruction from under sampled data","authors":"Penta Anil Kumar,&nbsp;Ramalingam Gunasundari","doi":"10.1016/j.mri.2024.110281","DOIUrl":"10.1016/j.mri.2024.110281","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) stands out as a notable non-invasive method for medical imaging assessments, widely employed in early medical diagnoses due to its exceptional resolution in portraying soft tissue structures. However, the MRI method faces challenges with its inherently slow acquisition process, stemming from the sequential sampling in k-space and limitations in traversal speed due to physiological and hardware constraints. Compressed Sensing in MRI (CS-MRI) accelerates image acquisition by utilizing greatly under-sampled k-space information. Despite its advantages, conventional CS-MRI encounters issues such as sluggish iterations and artefacts at higher acceleration factors. Recent advancements integrate deep learning models into CS-MRI, inspired by successes in various computer vision domains. It has drawn significant attention from the MRI community because of its great potential for image reconstruction from undersampled k-space data in fast MRI. This paper proposes a lightweight Adaptive Spatial-Channel Attention EfficientNet B3-based Generative Adversarial Network (ASCA-EffNet GAN) for fast, high-quality MR image reconstruction from greatly under-sampled k-space information in CS-MRI. The proposed GAN employs a U-net generator with ASCA-based EfficientNet B3 for encoder blocks and a ResNet decoder. The discriminator is a binary classifier with ASCA-based EfficientNet B3, a fully connected layer and a sigmoid layer. The EfficientNet B3 utilizes a compound scaling strategy that achieves a balance amongst model depth, width, and resolution, resulting in optimal performance with a reduced number of parameters. Furthermore, the adaptive attention mechanisms in the proposed ASCA-EffNet GAN effectively capture spatial and channel-wise features, contributing to detailed anatomical structure reconstruction. Experimental evaluations on the dataset demonstrate ASCA-EffNet GAN's superior performance across various metrics, surpassing conventional reconstruction methods. Hence, ASCA-EffNet GAN showcases remarkable reconstruction capabilities even under high under-sampling rates, making it suitable for clinical applications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110281"},"PeriodicalIF":2.1,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment","authors":"Kaori Shiraishi ,&nbsp;Takeshi Nakaura ,&nbsp;Naoki Kobayashi ,&nbsp;Hiroyuki Uetani ,&nbsp;Yasunori Nagayama ,&nbsp;Masafumi Kidoh ,&nbsp;Junji Yatsuda ,&nbsp;Ryoma Kurahashi ,&nbsp;Tomomi Kamba ,&nbsp;Yuichi Yamahita ,&nbsp;Toshinori Hirai","doi":"10.1016/j.mri.2024.110308","DOIUrl":"10.1016/j.mri.2024.110308","url":null,"abstract":"<div><h3>Purposes</h3><div>This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) —a deep learning-based technique that enhances image resolution and quality during MRI reconstruction— in improving the image quality of thin-slice 3D T2-weighted imaging (T2WI) and Prostate Imaging-Reporting and Data System (PI-RADS) assessment in prostate Magnetic Resonance Imaging (MRI).</div></div><div><h3>Methods</h3><div>This retrospective study included 33 patients who underwent prostate MRI with SR-DLR between November 2022 and April 2023. Thin-slice 3D-T2WI of the prostate was obtained and reconstructed with and without SR-DLR (matrix: 720 × 720 and 240 × 240, respectively). We calculated the contrast and contrast-to-noise ratio (CNR) between the internal and external glands of the prostate, as well as the slope of pelvic bone and adipose tissue. Two radiologists evaluated qualitative image quality and assessed PI-RADS scores of each reconstruction.</div></div><div><h3>Results</h3><div>The final analysis included 28 male patients (age range: 47–88 years; mean age: 70.8 years). The CNR with SR-DLR was significantly higher than without SR-DLR (1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], <em>p</em> = 0.002). No significant difference in contrast was observed between images with and without SR-DLR (<em>p</em> = 0.864). The slope with SR-DLR was significantly higher than without SR-DLR (0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19], <em>p</em> &lt; 0.01). Qualitative scores for contrast, sharpness, artifacts, and overall image quality were significantly higher with SR-DLR than without SR-DLR (<em>p</em> &lt; 0.05 for all). The kappa values for 2D-T2WI and 3D-T2WI increased from 0.694 and 0.640 to 0.870 and 0.827 with SR-DLR for both readers.</div></div><div><h3>Conclusions</h3><div>SR-DLR has the potential to improve image quality and the ability to assess PI-RADS scores in thin-slice 3D-T2WI of the prostate without extending MRI acquisition time.</div></div><div><h3>Summary</h3><div>Super-Resolution Deep Learning Reconstruction (SR-DLR) significantly improved image quality of thin-slice 3D T2-weighted imaging (T2WI) without extending the acquisition time. Additionally, the PI-RADS scores from 3D-T2WI with SR-DLR demonstrated higher agreement with those from 2D-T2WI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110308"},"PeriodicalIF":2.1,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Superior arterial signal suppression in lower extremity magnetic resonance venography: A comparative study of tracking and fixed saturation pulses","authors":"Yuya Wada ,&nbsp;Wataru Jomoto ,&nbsp;Yoshitaka Furukawa ,&nbsp;Yusuke Kawanaka","doi":"10.1016/j.mri.2024.110307","DOIUrl":"10.1016/j.mri.2024.110307","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to compare the suppression of arterial signal intensity between tracking and fixed saturation pulses in lower extremity magnetic resonance venography (MRV).</div></div><div><h3>Methods</h3><div>Forty patients with varicose veins who underwent 2D true fast imaging with steady-state free precession using tracking and fixed saturation pulses on MRV were included. A fixed saturation pulse was applied from April 2020 to May 2021, and a tracking saturation pulse was applied from June 2021 to July 2022. The arterial, venous, and muscle signal intensities obtained at the femoral and popliteal levels were used to calculate the contrast ratios between veins and arteries (CR_VA) and veins and muscles (CR_VM). Two experienced radiologists graded the images based on vein-artery contrast, suppression of arterial signal intensity, and visualization of lower leg perforators using a 9-point scale.</div></div><div><h3>Results</h3><div>Tracking saturation pulse images yielded significantly superior CR_VA and CR_VM compared with fixed saturation pulse images at both the femoral and popliteal levels. For the same saturation pulse type, the CR_VA was higher at the femoral level than at the popliteal level, while the CR_VM was comparable between the two levels. MRV with a tracking saturation pulse showed significantly superior vein-artery contrast, arterial signal suppression, and lower leg perforator visualization. Most scores for vein-artery contrast and arterial signal suppression with the tracking saturation pulse were positive (3.5–5), whereas few scores with the fixed saturation pulse were positive.</div></div><div><h3>Conclusion</h3><div>Tracking saturation pulse was more effective in suppressing arterial signal intensity in lower extremity MRV.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110307"},"PeriodicalIF":2.1,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partition-based k-space synthesis for multi-contrast parallel imaging","authors":"Yuxia Huang ,&nbsp;Zhonghui Wu ,&nbsp;Xiaoling Xu ,&nbsp;Minghui Zhang ,&nbsp;Shanshan Wang ,&nbsp;Qiegen Liu","doi":"10.1016/j.mri.2024.110297","DOIUrl":"10.1016/j.mri.2024.110297","url":null,"abstract":"<div><h3>Purpose</h3><div>Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique. However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore, utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve better reconstruction quality of T2-weighted image by feature fusion.</div></div><div><h3>Methods</h3><div>Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image.</div></div><div><h3>Results</h3><div>Experimental results showed that the developed PKS scheme can achieve comparable or better results than using traditional k-space parallel imaging (SAKE) that processes each contrast independently. At the same time, our method showed good adaptability and robustness under different contrast-assisted and T1-T2 ratios. Efficient target modal image reconstruction under various conditions were realized and had excellent performance in restoring image quality and preserving details.</div></div><div><h3>Conclusions</h3><div>This work proposed a PKS multi-contrast method to assist in target mode image reconstruction. We have conducted extensive experiments on different multi-contrast, diverse ratios of T1 to T2 and different sampling masks to demonstrate the generalization and robustness of our proposed model.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110297"},"PeriodicalIF":2.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of inflammation in abdominal aortic aneurysm with reduced field-of-view and low-b-value diffusion-weighted imaging","authors":"Yi-Jun Pan ,&nbsp;Xiao-lang Jiang ,&nbsp;Yan Shan ,&nbsp;Peng-Ju Xu ,&nbsp;Zhi-hui Dong ,&nbsp;Jiang Lin","doi":"10.1016/j.mri.2024.110295","DOIUrl":"10.1016/j.mri.2024.110295","url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the performance of diffusion-weighted imaging (DWI) with an optimal b-value and field-of-view in identifying wall inflammation in abdominal aortic aneurysm (AAA) by comparing it to delayed enhancement T1-weighted imaging (DEI).</div></div><div><h3>Methods</h3><div>Twenty-five males with AAA were prospectively enrolled and underwent fat-suppressed T1-weighted dark-blood imaging (T1WI), full field-of-view (f-FOV) and reduced field-of-view (r-FOV) DWI (b values = 0, 100, 400 and 800 s/mm²), and DEI. Corresponding images on f-FOV, r-FOV DWI and DEI at the same level were evaluated qualitatively and quantitatively using the paired <em>t</em>-test and Wilcoxon signed-rank test. The agreement in detecting wall inflammation between DWI and DEI sequences was analyzed using weighted kappa statistics.</div></div><div><h3>Results</h3><div>For both r-FOV and f-FOV DWI, the scores of delineation of aneurysm wall and lesion conspicuity, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were highest on DWI₁₀₀ (Ps &lt; 0.05). The scores of delineation of aneurysm wall, geometric distortion, lesion conspicuity, and SNR, CNR were significantly higher on r-FOV DWI than those on f-FOV DWI (Ps &lt; 0.05). r-FOV DWI₁₀₀ showed comparable performance to DEI in detecting wall inflammation (κ = 0.715), with superior blood suppression and higher SNR and CNR (Ps &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>DWI with r-FOV and low b-value could be a promising alternative to DEI in identifying wall inflammation in AAA.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110295"},"PeriodicalIF":2.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between MRI radiomic phenotypes and clinical outcomes in endometrial cancer: Implications for preoperative risk stratification","authors":"Xiaoting Jiang ,&nbsp;Weiling Zhai ,&nbsp;Jiacheng Song ,&nbsp;Wenhui Shao ,&nbsp;Aining Zhang ,&nbsp;Shaofeng Duan ,&nbsp;Feifei Qu ,&nbsp;Wenjun Cheng ,&nbsp;Chengyan Luo ,&nbsp;Feiyun Wu ,&nbsp;Xisheng Liu ,&nbsp;Ting Chen","doi":"10.1016/j.mri.2024.110298","DOIUrl":"10.1016/j.mri.2024.110298","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to investigate the correlation between imaging phenotypes of endometrial cancer (EC) and clinical, pathologic, and molecular characteristics, as well as disease-free survival (DFS).</div></div><div><h3>Methods</h3><div>The clinical, pathologic, and molecular characteristics, along with MRI radiomics features, of 356 patients with EC were collected retrospectively. The patients were divided into 2 groups based on radiomics features using unsupervised machine learning. The obtained characteristics and DFS of patients were compared between the various imaging phenotypes.</div></div><div><h3>Results</h3><div>The lesions with deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), cervical stromal invasion (CSI), lymph node metastasis, aggressive histologic type, advanced postoperative International Federation of Gynecology and Obstetrics (FIGO) stage, overexpression of p53, and absent expression of estrogen receptor or progesterone receptor were associated with poor DFS. Two clusters were identified and defined as imaging phenotype 1 and 2, respectively. Compared with phenotype 2, phenotype 1 exhibited a higher correlation with DMI (33.7 % vs 13.0 %), LVSI (23.8 % vs 9.2 %), CSI (16.3 % vs 3.8 %), aggressive histologic type (36.0 % vs 17.4 %), and advanced FIGO stage (IB or higher, 43.6 % vs 22.3 %) (<em>p &lt; 0.</em>001). The incidence of p53 overexpression was higher in phenotype 1 than in phenotype 2 (20.2 % vs 8.5 %, <em>p</em> = 0.022). Survival analysis exhibited a higher risk of poor DFS in phenotype 1 than in phenotype 2 (log-rank <em>p</em> = 0.002).</div></div><div><h3>Conclusion</h3><div>EC imaging phenotypes identified through MRI radiomics features were associated with pathologic, molecular characteristics, and DFS, suggesting potential for preoperative risk stratification.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110298"},"PeriodicalIF":2.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The fuzzy MAD stroke conjecture, using Fuzzy C Means to classify multimodal apparent diffusion for ischemic stroke lesion stratification","authors":"Frederick C. Damen ,&nbsp;Changliang Su ,&nbsp;Jay Tsuruda ,&nbsp;Thomas Anderson ,&nbsp;Tibor Valyi-Nagy ,&nbsp;Weiguo Li ,&nbsp;Mehran Shaghaghi ,&nbsp;Rifeng Jiang ,&nbsp;Chuanmiao Xie ,&nbsp;Kejia Cai","doi":"10.1016/j.mri.2024.110294","DOIUrl":"10.1016/j.mri.2024.110294","url":null,"abstract":"<div><h3>Background</h3><div>In conjunction with an epidemiologically determined treatment window, current radiological acute ischemic stroke practice discerns two lesion (stage) types: core (dead tissue, identified by diffusion-weighted imaging (DWI)) and penumbra (tissue region receiving just enough blood flow to be potentially salvageable, identified by the perfusion diffusion mismatch). However, advancements in preclinical and clinical studies have indicated that this approach may be too rigid, warranting a more fine-grained patient-tailored approach. This study aimed to demonstrate the ability to noninvasively provide insights into the current <em>in vivo</em> stroke lesion cascade.</div></div><div><h3>Methods</h3><div>To elucidate a finer-grained depiction of the acute focal ischemic stroke cascade <em>in vivo</em>, we retrospectively applied our multimodal apparent diffusion (MAD) method to multi-b-value DWI, up to a b-value of 10,000 s/mm² in 34 patients with acute focal ischemic stroke. Fuzzy C Means was used to cluster the MAD parameters.</div></div><div><h3>Results</h3><div>We discerned 18 clusters consistent with normal appearing tissue (NAT) types and 14 potential ischemic lesion (stage) types, providing insights into the variability and aggressiveness of lesion progression and current anomalous stroke-related imaging features. Of the 529 ischemic stroke lesion instances previously identified by two radiologists, 493 (92 %) were autonomously identified; 460 (87 %) were identified as efficaciously or better than the radiologists.</div></div><div><h3>Conclusions</h3><div>The data analyzed included a small number of clinical patients without follow-up or contemporaneous histology; therefor, the findings and theorizing should be treated as conjecture. Nevertheless, each identified NAT and lesion type is consistent with the known underpinnings of physiological tissues and pathological ischemic stroke lesion (stage) types. Several findings should be considered in current clinical imaging: WM fluid accumulation, BBB compromise conundrum, b₁₀₀₀ identified core may not be dead tissue, and a practical reason for DWI (pseudo) normalization.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110294"},"PeriodicalIF":2.1,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of mean apparent propagator (MAP) MRI in patients with acute ischemic stroke: A comparative study with DTI and NODDI","authors":"Julia Diamandi ,&nbsp;Christian Raimondo ,&nbsp;Mahdi Alizadeh ,&nbsp;Adam Flanders ,&nbsp;Stavropoula Tjoumakaris ,&nbsp;M Reid Gooch ,&nbsp;Pascal Jabbour ,&nbsp;Robert Rosenwasser ,&nbsp;Nikolaos Mouchtouris","doi":"10.1016/j.mri.2024.110290","DOIUrl":"10.1016/j.mri.2024.110290","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the Mean Apparent Propagator (MAP) MRI for processing multi-shell diffusion imaging in patients with acute ischemic stroke (AIS) and correlate to diffusion tensor imaging (DTI) and neurite orientation and dispersion density imaging (NODDI).</div></div><div><h3>Methods</h3><div>We enrolled patients with AIS from 1/2022 to 4/2024 who underwent multi-shell diffusion imaging on a 3.0-Tesla scanner to generate DTI, NODDI and MAP measures. Mean intensity and standard deviation (SD) were calculated for the infarcted regions-of-interest in b0, fractional anisotropy (FA), mean diffusivity (MD), intra-cellular volume fraction (ICVF), free water fraction (FWF), and orientation dispersion index (ODI), return to the origin probability (RTOP), return to the plane probability (RTPP), return to the axis probability (RTAP), propagator anisotropy (PA), q-space Mean Square Displacement (QMSD), and non-Gaussianity (NG).</div></div><div><h3>Results</h3><div>Twenty-two patients were included with an average age of 69.5 ± 13.5, mean NIHSS of 12.4 ± 7.7, and median infarct of 73.3 ± 10.1 ml. ICVF was correlated with RTPP (ρ = 0.82, <em>p</em> &lt; 0.01), RTAP (ρ = 0.76, <em>p</em> &lt; 0.01) and RTOP (ρ = 0.79, <em>p</em> &lt; 0.01), ODI with PA (ρ = −0.83, <em>p</em> &lt; 0.01), FWF with RTOP (ρ = −0.73, <em>p</em> &lt; 0.01), RTAP (ρ = −0.69, <em>p</em> &lt; 0.01), and RTPP (ρ = −0.73, <em>p</em> &lt; 0.01), MD with RTPP (ρ = −0.80, p &lt; 0.01), RTOP (ρ = −0.79, <em>p</em> &lt; 0.01), and RTAP (ρ = −0.77, <em>p</em> &lt; 0.01), FA with RTAP (ρ = 0.77, p &lt; 0.01), RTOP (ρ = 0.67, <em>p</em> = 0.01), PA (ρ = 0.74, p &lt; 0.01), and SD PA (ρ = 0.85, p &lt; 0.01). Multivariable linear regression identified the SD QMSD (β = 0.406, <em>p</em> = 0.008), thrombectomy (β = 0.481, <em>p</em> = 0.002), and infarct volume (β = 0.292, <em>p</em> = 0.051) as predictive of stroke severity based on NIHSS.</div></div><div><h3>Conclusions</h3><div>Given its short processing time, MAP MRI is a valuable alternative with potential for clinical use in AIS.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110290"},"PeriodicalIF":2.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative risk stratification of early-stage endometrial cancer assessed by multimodal magnetic resonance functional imaging","authors":"Ruqi Ou,&nbsp;Yongjun Peng","doi":"10.1016/j.mri.2024.110283","DOIUrl":"10.1016/j.mri.2024.110283","url":null,"abstract":"<div><div>Endometrial cancer is a common disease in women. Stratifying the risk of early-stage endometrial cancer can aid in personalized treatment for patients. Risk stratification is primarily based on tumor grade, histological type, lymph node metastasis, and depth of myometrial invasion. Multimodal magnetic resonance functional imaging (including DCE-MRI, DWI, IVIM, DTI, DKI) has significant value in assessing the extent of myometrial and cervical infiltration, extrauterine involvement range, determining lymph node metastasis and tumor size. This article provides a brief overview of these techniques.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110283"},"PeriodicalIF":2.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of amide proton transfer imaging in predicting parametrial invasion and lymph-vascular space invasion of cervical cancer","authors":"Chongshuang Yang ,&nbsp;Hasyma Abu Hassan ,&nbsp;Nur Farhayu Omar ,&nbsp;Tze Hui Soo ,&nbsp;Ahmad Shuib Bin Yahaya ,&nbsp;Tianliang Shi ,&nbsp;Zhihong Qin ,&nbsp;Min Wu ,&nbsp;Jing Yang","doi":"10.1016/j.mri.2024.110282","DOIUrl":"10.1016/j.mri.2024.110282","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the value of amide proton transfer (APT) imaging in assessing parametrial invasion (PMI) and lymph-vascular space invasion (LVSI) of cervical cancer.</div></div><div><h3>Materials and methods</h3><div>We retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (−) groups, and PMI (+) and PMI (−) groups. Independent sample <em>t</em>-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures.</div></div><div><h3>Results</h3><div>A total of 83 patients were included, with 56 in the LVSI (−) group, 27 in the LVSI (+) group, 35 in the PMI (−) group, and 16 in the PMI (+) group. The ADC values for the LVSI (+) and PMI (+) groups were significantly lower than those for the LVSI (−) and PMI (−) groups (<em>P</em> &lt; 0.01). The APT values for the LVSI (+) and PMI (+) groups were significantly higher than those for the LVSI (−) and PMI (−) groups (<em>P</em> &lt; 0.01). The AUC values for ADC, APT, and the combination of ADC + APT in predicting LVSI were 0.839, 0.788, and 0.880, respectively, and in predicting PMI were 0.770, 0.764, and 0.796, respectively. There were no statistically significant differences in the diagnostic performance of ADC, APT, and ADC + APT in predicting PMI. However, the diagnostic performance of ADC + APT in predicting LVSI was significantly better than that of ADC and APT alone (<em>P</em> &lt; 0.01).</div></div><div><h3>Conclusion</h3><div>APT imaging can predict LVSI and PMI status in cervical cancer before surgery. When combined with ADC, its diagnostic accuracy for predicting LVSI is higher than that of APT or ADC alone. This suggests a novel approach for assessing LVSI in cervical cancer.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110282"},"PeriodicalIF":2.1,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142739838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}