Magnetic Resonance Imaging Fusion Research Articles

Prostate volume on computed tomography correlates well with magnetic resonance imaging measurements and is reproducible across rater training levels.

Data are lacking for the accuracy of computed tomography (CT) in measuring prostate size, which can streamline care and prevent invasive procedures. We evaluate agreement and intra/inter-observer variability in prostate sizing between CT and magnetic resonance imaging (MRI) planimetry for a wide range of gland sizes. We retrospectively reviewed 700 patients who underwent MRI fusion biopsy at a single institution and identified 89 patients that had a CT within 2years of the MRI. Six reviewers from different training levels were categorized as student, resident, or attending and each measured prostate size on CT by the prolate ellipse method. Bland-Altman analysis determined the degree of agreement between CT and MRI. Inter- and intra-observer reliability was calculated for CT. Mean CT volume was higher than MRI volume in the < 60g group (51.5g vs. 44.5g, p = 0.004), but not in the ≥ 60g group (101g vs. 100g, p = 0.458). The bias for overestimation of prostate volume by CT was 4.1g across prostate volumes, but the proportional agreement between modalities improved with size. The Pearson correlation coefficient between CT/MRI and inter/intra-rater reliability for CT increased in the ≥ 60g vs. the < 60g group for all training levels. Our data show that there is greater clinical utility for prostate size estimation by CT than previously established, particularly for larger glands where accurate size estimation may influence therapeutic decisions. In larger glands, prostate size estimation by CT is also reproducible across various training levels.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

High-intensity focused ultrasound strategies for treating prostate cancer

Abstract Prostate cancer (PCa) is a significant health concern globally, necessitating effective treatment options. Typical treatment methods for early stage, particularly localized PCa, encompass radical procedures, such as radical prostatectomy (RP) and radiotherapy (RT), and nonradical focal therapy (FT). FT is a focused approach mainly used for treating small lesions limited to a specific zone of the prostate. Its objective is to achieve cancer control when minimizing damage to benign tissue. High-intensity focused ultrasound (HIFU) is one of the most used modalities in FT for the management of PCa. The progress in HIFU technology showcases continuous advancements, offering clinicians a variety of strategies to cater to diverse patient requirements. The advancements include the development of transrectal and transurethral HIFU machines that offer enhanced treatment distances, magnetic resonance imaging (MRI) fusion capabilities, real-time monitoring, and precise ablation. These improvements contribute to increased treatment effectiveness and better outcomes for patients. This narrative review aims to summarize the use of HIFU technology and its evolution, offering diverse options to clinicians, and explores the safety, effectiveness, and quality of different HIFU strategies, such as whole-gland ablation, hemigland ablation, and focal ablation. We conclude that nonwhole-gland HIFU offers similar cancer control with better short-term functional outcomes and fewer complications compared to whole-gland ablation. Combining HIFU with transurethral resection of the prostate (TURP) improves urinary function and reduces catheterization time. Focal ablation and hemigland ablation show promise in achieving cancer control when preserving continence and potency.

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer.

To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC). This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis. Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001). Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

The learning curve and experience of a novel multi-modal image fusion targeted transperineal prostate biopsy technique using electromagnetic needle tracking under local anesthesia.

Prostate cancer is the most common malignant tumor of male genitourinary system, and the gold standard for its diagnosis is prostate biopsy. Focusing on the methods and skills of prostate biopsy, we explored the learning curve and experience of a novel magnetic resonance imaging and transrectal ultrasound (mpMRI-TRUS) image fusion transperineal biopsy (TPB) technique using electromagnetic needle tracking under local anesthesia. The clinical and pathological data of 92 patients who underwent targeted TPB from January 2023 to July 2023 in our center were prospectively collected. The cumulative sum (CUSUM) analysis method and the best fitting curve were used to analyze the learning curve of this novel technique, and the clinical characteristics, perioperative data and tumor positive rate of prostate biopsy of patients at different stages of the learning curve were compared. With the increase of the number of surgical cases, the overall operative time showed a downward trend. The best fitting curve of CUSUM reached its peak at the twelfth case, which is the minimum cumulative number of surgical cases needed to cross the learning curve of the operation. Taking this as the boundary, the learning curve is divided into two stages: learning improvement stage (group A, 12 cases) and proficiency stage (group B, 80 cases). The surgical time and visual analog scale score during prostate biopsy in group A were significantly higher than those in group B. The visual numerical scale score during prostate biopsy in group A was significantly lower than that in group B. There was no statistically significant difference between group A and group B in the detection rate of csPCa and the incidence of perioperative complications. The novel targeted TPB technique is divided into learning improvement stage and proficiency stage, and 12 cases may be the least cumulative number.

Multi-modality MRI fusion with patch complementary pre-training for internet of medical things-based smart healthcare

Magnetic Resonance Imaging (MRI) is a pivotal neuroimaging technique capable of generating images with various contrasts, known as multi-modal images. The integration of these diverse modalities is essential for improving model performance across various tasks. However, in real clinical scenarios, acquiring MR images for all modalities is frequently hindered by factors such as patient comfort and scanning costs. Therefore, effectively fusing different modalities to synthesize missing modalities has become a research hot-spot in the field of smart healthcare, particularly in the context of the Internet of Medical Things (IoMT). In this study, we introduce a multi-modal coordinated fusion network (MCF-Net) with Patch Complementarity Pre-training. This network leverages the complementarity and correlation between different modalities to make the fusion of multi-modal MR images, addressing challenges in the IoMT. Specifically, we first employ a Patch Complementarity Mask Autoencoder (PC-MAE) for self-supervised pre-training. The complementarity learning mechanism is introduced to align masks and visual annotations between two modalities. Subsequently, a dual-branch MAE architecture and a shared encoder–decoder are adopted to facilitate cross-modal interactions within mask tokens. Furthermore, during the fine-tuning phase, we incorporate an Attention-Driven Fusion (ADF) module into the MCF-Net. This module synthesizes missing modal images by fusion of multi-modal features from the pre-trained PC-MAE encoder. Additionally, we leverage the pre-trained encoder to extract high-level features from both synthetic and corresponding real images, ensuring consistency throughout the training process. Our experimental findings showcase a notable enhancement in performance across various modalities with our fusion method, outperforming state-of-the-art techniques.

Magnetic resonance and computer tomography image fusion using novel weight maps obtained by using median and guided filters

An attempt is made in this paper to diagnose brain-related diseases like sarcoma, fatal stroke disease, cerebral disease, and Alzheimer's disease by using saliency information from magnetic resonance and computed tomography source images. The saliency information for each source image is computed using guided and median filters. The obtained saliency maps are used for computing the weight maps of each source image by using image statistics. The obtained weights are used to fuse the approximate and detailed layers of the source images by using the weighted average fusion technique. The proposed algorithm is simulated in MATLAB for various benchmark data sets of brains taken from the brain atlas provided by Harvard Medical School, available at https://www.med.harvard.edu/aanlib. In order to test the efficacy of the novel method, comparative analysis is performed in terms of image quality assessment metrics like mean, mutual information, average gradient, standard deviation, spatial frequency, etc. From the analysis, this paper concludes that the proposed algorithm improved gradient information in the fused image by 35.7%, entropy by 5.7%, spatial frequency by 32.7%, edge strength by 14.5%, and minimized the information loss by 43.6%. Therefore, the novel method of weight map computation produces a detailed and noise-free image, which is helpful for better diagnosis in clinical applications.

Multimodal Neuroimaging based Alzheimer's Disease Diagnosis using Evolutionary RVFL Classifier.

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Retracted: Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks.

[This retracts the article DOI: 10.1155/2022/6878783.].

Enhanced Precision and Safety in Thermal Ablation: O-Arm Cone Beam Computed Tomography with Magnetic Resonance Imaging Fusion for Spinal Column Tumor Targeting.

Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of cone beam computed tomography with magnetic resonance imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated the procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed for precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.

Characterizing Typhoon-related Posttraumatic Stress Disorder Based on Multimodal Fusion of Structural, Diffusion, and Functional Magnetic Resonance Imaging

Diagnosing posttraumatic stress disorder (PTSD) using only single-modality images is controversial. We aimed to use multimodal magnetic resonance imaging (MRI) combining structural, diffusion, and functional MRI to possibly provide a more comprehensive viewpoint on the decisive characteristics of PTSD patients. Typhoon-exposed individuals with (n = 26) and without PTSD (n = 32) and healthy volunteers (n = 30) were enrolled. Five MRI features from three modalities, including two resting-state functional MRI (rs-fMRI) features (amplitude of low-frequency fluctuation, ALFF; and regional homogeneity, ReHo), one structural MRI feature (gray matter density, GM), and two diffusion tensor imaging (DTI) features (fractional anisotropy, FA; and mean diffusivity, MD) were investigated simultaneously with a multimodal canonical correlation analysis + joint independent component analysis model to identify abnormalities in the PTSD brain. We identified statistical differences between PTSD patients and healthy controls in terms of 1 rs-fMRI (ALFF, ReHo) alterations in the superior frontal gyrus, precuneus, inferior parietal lobule (IPL), anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC), 2 DTI (FA, MD) changes in the pons, genu, and splenium of the corpus callosum, and 3 Structural MRI abnormalities in the precuneus, IPL, ACC, and PCC. A novel ReHo component was found to distinguish PTSD and trauma-exposed controls, including the precuneus, IPL, middle frontal gyrus, middle occipital gyrus, and cerebellum. This study reveals that PTSD individuals exhibit intertwined functional and structural anomalies within the default mode network. Some alterations within this network may serve as a potential marker to distinguish between PTSD patients and trauma-exposed controls.

Multimodal covarying brain patterns mediate genetic and psychological contributions to individual differences in pain sensitivity.

Individuals vary significantly in their pain sensitivity, with contributions from the brain, genes, and psychological factors. However, a multidimensional model integrating these factors is lacking due to their complex interactions. To address this, we measured pain sensitivity (ie, pain threshold and pain tolerance) using the cold pressor test, collected magnetic resonance imaging (MRI) data and genetic data, and evaluated psychological factors (ie, pain catastrophizing, pain-related fear, and pain-related anxiety) from 450 healthy participants with both sexes (160 male, 290 female). Using multimodal MRI fusion methods, we identified 2 pairs of covarying structural and functional brain patterns associated with pain threshold and tolerance, respectively. These patterns primarily involved regions related to self-awareness, sensory-discriminative, cognitive-evaluative, motion preparation and execution, and emotional aspects of pain. Notably, pain catastrophizing was negatively correlated with pain tolerance, and this relationship was mediated by the multimodal covarying brain patterns in male participants only. Furthermore, we identified an association between the single-nucleotide polymorphism rs4141964 within the fatty acid amide hydrolase gene and pain threshold, mediated by the identified multimodal covarying brain patterns across all participants. In summary, we suggested a model that integrates the brain, genes, and psychological factors to elucidate their role in shaping interindividual variations in pain sensitivity, highlighting the important contribution of the multimodal covarying brain patterns as important biological mediators in the associations between genes/psychological factors and pain sensitivity.

Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy.

The aim of this study is to evaluate the impact of radiologist and urologist variability on detection of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) with magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion prostate biopsies. The Prospective Loyola University MRI (PLUM) Prostate Biopsy Cohort (January 2015 to December 2020) was used to identify men receiving their first MRI and MRI/TRUS fusion biopsy for suspected PCa. Clinical, MRI and biopsy data were stratified by radiologist and urologist to evaluate variation in Prostate Imaging-Reporting and Data System (PI-RADS) grading, lesion number and cancer detection. Multivariable logistic regression (MVR) models and area under the curve (AUC) comparisons assessed the relative impact of individual radiologists and urologists. A total of 865 patients (469 biopsy-naïve) were included across 5 urologists and 10 radiologists. Radiologists varied with grading 15.4% to 44.8% of patients with MRI lesions as PI-RADS 3. PCa detection varied significantly by radiologist, from 34.5% to 66.7% (p= 0.003) for PCa and 17.2% to 50% (p= 0.001) for csPCa. Urologists' PCa diagnosis rates varied between 29.2% and 55.8% (p= 0.013) and between 24.6% and 39.8% (p= 0.36) for csPCa. After adjustment for case-mix on MVR, a fourfold to fivefold difference in PCa detection was observed between the highest-performing and lowest-performing radiologist (OR 0.22, 95%CI 0.10-0.47, p< 0.001). MVR demonstrated improved AUC for any PCa and csPCa detection when controlling for radiologist variation (p= 0.017 and p= 0.038), but controlling for urologist was not significant (p= 0.22 and p= 0.086). Any PCa detection (OR 1.64, 95%CI 1.06-2.55, p= 0.03) and csPCa detection (OR 1.57, 95%CI 1.00-2.48, p= 0.05) improved over time (2018-2020 vs. 2015-2017). Variability among radiologists in PI-RADS grading is a key area for quality improvement significantly impacting the detection of PCa and csPCa. Variability for performance of MRI-TRUS fusion prostate biopsies exists by urologist but with less impact on overall detection of csPCa.

Combination of DNA methylation biomarkers with multiparametric magnetic resonance and ultrasound imaging fusion biopsy to detect the local spread of prostate cancer.

This study aimed to investigate the extent of field cancerization adjacent to index lesions in prostate cancer (PCa) by measuring DNA methylation of selected tumor suppressor genes in the perifocal tissue of PCa not visible on multiparametric magnetic resonanse imaging (mpMRI) for the safe zone of focal therapy identification. A total of 272 patients were enrolled in this study, 44 patients' tissue biosamples were included in the field cancerization research, and 272 urine samples were included in the urine-based test development. Targeted biopsies were performed using the mpMRI/ultrasoundimage fusion system. Quantitative analysis revealed significantly higher DNA methylation levels of RARB, RASSF1, GSTP1 &APC genes in the index lesion compared with perifocal tissue samples 10 mm away from it (p < 0.0001). Notably, the RARB, GSTP1 & APC and RARB, RASSF1, GSTP1 & APC biomarker combinations exhibited the highest sensitivity and specificity comparing the extent of DNA methylation in index lesions and noncancerous prostate tissues 20 mm away (both area under the curve [AUC] = 0.98; p < 0.0001). The analysis of the potential urinary biomarkers showed that the combination of all four DNA methylation biomarkers with prostate-specific antigen (PSA) or PSA density (PSAD) in the blood significantly improves the detection of clinically significant PCa (csPCa). The combination of the four-biomarker test with PSAD allowed the identification of csPCa with ≥90% sensitivity and specificity. Thus, this study suggests that for focal therapy by region target hemi-ablation, the safe distance from the index lesion is no less than 10 mm. Noninvasive urine DNA methylation tests in combination with PSAD could be used for further follow-up of the patients, but larger prospective studies with external validation are needed.

Superior detection of significant prostate cancer by transperineal prostate biopsy using MRI-transrectal ultrasound fusion image guidance over cognitive registration.

The BioJet system allows the fusion of magnetic resonance imaging (MRI) images with real-time transrectal ultrasonography to accurately direct biopsy needles to the target lesions. To date, the superiority of targeted biopsy using the BioJet system over cognitive registration remains unknown. This retrospective study included 171 biopsy-naïve men with elevated prostate-specific antigen (2.5-20ng/mL) and MRI-positive lesions; 74 and 97 men underwent a four-core targeted biopsy per MRI-positive target lesion and a 14-core systematic biopsy transperineally using the BioJet system and cognitive registration, respectively. Detection rates of significant cancer, defined as grade group ≥ 2 or maximum cancer length ≥ 5mm, were compared between the BioJet system and cognitive registration using propensity score matching and a multivariate logistic regression model. After propensity score matching (67 men for each group), the detection rates of significant cancer were significantly higher in the BioJet group than in the cognitive group for both targeted (76% vs. 46%, P = 0.002) and systematic (70% vs. 46%, P = 0.018) biopsy. Multivariate analysis of the entire cohort also showed that the BioJet system was independently associated with significant cancer detection by targeted and systematic biopsy (P < 0.01), along with a higher prostate-specific antigen density and a higher prostate imaging reporting and data system score. Transperineal prostate biopsy using the BioJet system is superior to cognitive registration in detecting significant cancer for targeted and systematic biopsies.

Transperineal vs transrectal magnetic resonance and ultrasound image fusion prostate biopsy: a pair-matched comparison

The objective of this study was to compare transperineal (TP) versus transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx). Consecutive men who underwent prostate MRI followed by a systematic biopsy. Additional target biopsies were performed from Prostate Imaging Reporting & Data System (PIRADS) 3–5 lesions. Men who underwent TP PBx were matched 1:2 with a synchronous cohort undergoing TR PBx by PSA, Prostate volume (PV) and PIRADS score. Endpoint of the study was the detection of clinically significant prostate cancer (CSPCa; Grade Group ≥ 2). Univariate and multivariable analyses were performed. Results were considered statistically significant if p < 0.05. Overall, 504 patients met the inclusion criteria. A total of 168 TP PBx were pair-matched to 336 TR PBx patients. Baseline demographics and imaging characteristics were similar between the groups. Per patient, the CSPCa detection was 2.1% vs 6.3% (p = 0.4) for PIRADS 1–2, and 59% vs 60% (p = 0.9) for PIRADS 3–5, on TP vs TR PBx, respectively. Per lesion, the CSPCa detection for PIRADS 3 (21% vs 16%; p = 0.4), PIRADS 4 (51% vs 44%; p = 0.8) and PIRADS 5 (76% vs 84%; p = 0.3) was similar for TP vs TR PBx, respectively. However, the TP PBx showed a longer maximum cancer core length (11 vs 9 mm; p = 0.02) and higher cancer core involvement (83% vs 65%; p < 0.001) than TR PBx. Independent predictors for CSPCa detection were age, PSA, PV, abnormal digital rectal examination findings, and PIRADS 3–5. Our study demonstrated transperineal MRI/TRUS fusion PBx provides similar CSPCa detection, with larger prostate cancer core length and percent of core involvement, than transrectal PBx.

The Short-term Efficacy of Contrast-enhanced Ultrasound (CEUS) and Gd-EOB-DTPA-enhanced Magnetic Resonance Imaging (MRI) Fusion Imaging-guided Radiofrequency Ablation (RFA) for Colorectal Liver Metastasis (CRLM).

This study is to explore the efficacy of contrast-enhanced ultrasound (CEUS) / Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) fusion imaging-guided（fusion group）radiofrequency ablation (RFA) versus conventional ultrasound imaging-guided (conventional group) RFA for colorectal cancer liver metastases (CRLM) in a short-term. From December 2020 to December 2021, patients who underwent imaging-guided RFA of CRLM at our hospital with available CT/MRI images were enrolled consecutively. 22 patients with 46 lesions had undergone conventional group RFA whereas 29 patients with 63 lesions had undergone fusion group RFA. The lesion detection rate, technical success, local tumor progression (LTP) and complications were calculated. In this retrospective study, 51 patients with 130 lesions were diagnosed with CRLM. However, there were 12 lesions and 9 lesions invisible in the conventional group and fusion group, respectively. The lesion detection rate on the fusion imaging was significantly higher than on the US or CEUS in the fusion group (P＜0.05). There were no significant differences of the detection rate between the conventional group and the fusion group (P=0.207). In both groups, the technical success rate was 100%. For local tumor progression (LTP), there were no significant differences between the two groups (P＞0.05). The complications after ablation had no significant differences between the two groups (P=0.97). CEUS/ Gd-EOB-DTPA-enhanced MRI fusion imaging is a safe and effective method for RFA in the management of CRLM patients, and it may improve the therapeutic effect by detecting small lesions early.

Application of aptamer-functionalized nanomaterials in molecular imaging of tumors

Abstract Cancer is one of the most important causes of human death. Early diagnosis and treatment can make patients live longer. Therefore, there is an urgent need to develop early and accurate diagnosis method for tumors. Molecular imaging technology can be used for qualitative and quantitative analyses at cellular and molecular levels, which provides a new technology for accurate diagnosis of tumors. In recent years, various nanomaterials with unique properties have been used for tumor molecular imaging. Meanwhile, aptamers are becoming an indispensable element in the design of functional nanomaterials because of their small size, high stability, and convenient modification, especially giving nanomaterials the ability to recognize specific targets. Therefore, aptamer-functionalized nanomaterials (AFNs) provide unprecedented opportunities for the field of tumor diagnosis. Here we focus on the latest development of AFNs in the molecular imaging of tumors. First, we introduce the characteristics and advantages of common aptamer-modified organic nanomaterials and inorganic nanomaterials. Then, the applications of AFNs in fluorescence imaging, computed tomography, magnetic resonance imaging, radionuclide imaging, ultrasound imaging, photoacoustic imaging, and multimode fusion imaging are discussed. Finally, we provide some perspectives on the challenges and opportunities that have arisen from this promising area.

Functional and structural brain reorganization in patients with ischemic stroke: a multimodality MRI fusion study.

Understanding how structural and functional reorganization occurs is crucial for stroke diagnosis and prognosis. Previous magnetic resonance imaging (MRI) studies focused on the analyses of a single modality and demonstrated abnormalities in both lesion regions and their associated distal regions. However, the relationships of multimodality alterations and their associations with poststroke motor deficits are still unclear. In this study, 71 hemiplegia patients and 41 matched healthy controls (HCs) were recruited and underwent MRI examination at baseline and at 2-week follow-up sessions. A multimodal fusion approach (multimodal canonical correlation analysis + joint independent component analysis), with amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) as features, was used to extract the co-altered patterns of brain structure and function. Then compared the changes in patients' brain structure and function between baseline and follow-up sessions. Compared with HCs, the brain structure and function of stroke patients decreased synchronously in the local lesions and their associated distal regions. Damage to structure and function in the local lesion regions was associated with motor function. After 2 weeks, ALFF in the local lesion regions was increased, while GMV did not improve. Taken together, the brain structure and function in the local lesions and their associated distal regions were damaged synchronously after ischemic stroke, while during motor recovery, the 2 modalities were changed separately.