Magnetic Resonance Imaging Fusion Research Articles

Preoperative Novel Magnetic Resonance Fusion Imaging for Carotid Endarterectomy Patients with Contrast Contraindications: A Pilot Study

For carotid endarterectomy (CEA) patients with renal dysfunction and allergies to contrast media, we developed a preoperative novel method of noncontrast 3-dimensional magnetic resonance fusion imaging (NC-3DMRFI) which could describe well blood vessels, plaques, and bony structures even in surgical position. In this study, we examined the usefulness of this method. We extracted noncontrast magnetic resonance images of bones, blood vessels, and plaques to create a 3-dimensionalusion image. An image acquired in the normal position and another in the surgical position during CEA were used to create a fusion image. We compared the fusion imaging results with the intraoperative findings of 6 patients with contrast contraindications received CEA. Preoperative NC-3DMRFI could clearly show the positions of the carotid bifurcation, the distal end of plaque, and the bony structure in 5 of the 6 cases. Intraoperative findings and preoperative fusion imaging results were comparable in all cases where fusion images could be created. The fusion imaging in the surgical position during CEA was useful for preoperative examination, and the surgical space could be secured in the case of a high cervical location. This pilot study showed our novel NC-3DMRFI method is useful for preoperative simulation in CEA, especially in patients with renal dysfunction and allergies to contrast media.

Multiscale feature fusion network for 3D head MRI image registration.

Image registration technology has become an important medical image preprocessing step with the wide application of computer-aided diagnosis technology in various medical image analysis tasks. We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI. Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non-rigid transformation, a deformable registration module composed of top-down and bottom-up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non-rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks. We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745±0.021, the Hausdorff distance was 3.441±0.935mm, the Average surface distance was 0.738±0.098mm, and the Standard deviation of the Jacobian matrix was 0.425±0.043. Our new algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. Our proposed multiscale feature fusion registration network can realize end-to-end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.

Промежностная фьюжн-биопсия предстательной железы. Диагностическая ценность в сравнении со стандартной биопсией.

Introduction. A significant improvement in the quality of prostate imaging by magnetic resonance (MRI), as well as the possibility of combining MRI and ultrasound images (fusion technology) for biopsy, open up new possibilities for the diagnosis of prostate cancer. The arsenal of methods of treating prostate cancer includes active surveillance and focal therapy for patients with low risk disease. That raises the question of improving the accuracy of diagnosis of tumor volume, stage and differentiation, which will largely determine the tactics of patient management. The aim of our work was to determine the diagnostic effectiveness of targeted MRI-assisted fusion biopsy of the prostate in comparison with standard biopsy. Materials and methods. We analyzed data of 356 patients who underwent tranperineal fusion biopsy. The age of the patients ranged from 48 to 72 years with an average value of 68.2 ± 2.1 years. The selection criteria for the study were an increase in the level of PSA (345 patients) – the range from 4 to 44.5 ng/ml (on average 6.02±0.21) or the detection of tumor-suspicious areas during finger rectal examination or transrectal ultrasound examination (TRUS) of the prostate gland (41 patients). Further methods of examination included multiparametric magnetic resonance imaging (MRI) and subsequent transperineal fusion biopsy in combination with standart biopsy. Results. In our study, the detection rate of prostate cancer was (82.6%) – the diagnosis was confirmed in 294 out of 356 examined patients. At the same time, with an increase in the PI-RADS gradation, the frequency of histological confirmation of cancer increased: 62.4% for PI-RADS 3 lesions, 89.6% for 4 and 96.7% for PI-RADS 5. Targeted biopsies confirmed the presence of prostate cancer in 246 cases out of 356 patients (69.1%). In relation to all cases of cancer detection, the proportion of positive targeted biopsies was 83.7%. Conclusion. Targeted MRI-assisted fusion biopsy allows detecting prostate cancer in 69.1%, but when supplemented with a polyfocal biopsy at standard points, the diagnostic accuracy increases to 82.6%. Thus, the data obtained allow us to conclude that based on the examination of patients with suspected prostate cancer using mpMRI with targeted fusion biopsy, supplemented by polyfocal biopsies at standard points, it is possible to determine the localization, extent and degree of malignancy of the tumor with a high degree of certainty.

Data-driven multimodal fusion: approaches and applications in psychiatric research.

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

Gleason Grade Group Concordance between Systematic Template Combining Magnetic Resonance Imaging Fusion Targeted Biopsy and Radical Prostatectomy Specimens: A Comparison of Transperineal and Transrectal Approaches

To evaluate the Gleason grade (GG) discrepancy between biopsy (Bx) techniques (transperineal (TP) /transrectal (TR) approaches or multiparametric magnetic resonance imaging (mpMRI) targeted biopsy (TBx) / standard template biopsies (SBx)) and prostatectomy (RP) specimens. We identified 310 PCa patients who underwent RP following either TP TBx combining SBx (20-core) (n=105) or TR TBx combining SBx (12-core) (n=205) from September 2019 to February 2021. The Bx GG was based on the core with the highest GG and csPCa was defined as grade group 2 or greater prostate adenocarcinoma. TP combined TBx and SBx (CBx) showed a better GG concordance (63.8% vs 57.1%) than the TR approach, but did not reach a statistical significance. TBx demonstrated a significantly higher csPCa detection than SBx in all patients including both approaches (70.2% vs 63.9%, p<0.001). TR TBx showed a significantly higher concordance than TR SBx (52.2% vs 41.5%, p=0.002) while TP TBx did not differ from TP SBx. TP CBx showed the highest Kappa coefficient (κ =0.48) followed by TR CBx (κ = 0.39). Thirty-eight of 69 (55.1%) cases with a GG1 diagnosis in CBx were upgraded to csPCa in RP. TR approach showed a trend of 2.8-fold risk to upgrade to RP csPCa than TP approach (p=0.065). The combination of SBx and TBx led to a better pathological concordance and lower upgrading rate for both TP and TR approaches to RP. With more SBx cores, TP CBx showed a better performance than TR CBx.

Cone Beam Computed Tomography Image Fusion with Cross Sectional Images for Percutaneous Renal Tumor Ablation: Preliminary Data.

Percutaneous ablative treatments in the kidney are now standard options for local cancer therapy. Multimodality image guidance, combining two 3D image sets, may improve procedural images and interventional strategies. We aimed to assess the value of intra-procedural cone beam computed tomography (CBCT) with magnetic resonance (MR) or CT imaging fusion technique in the guidance of percutaneous microwave ablation (MWA) of renal neoplasms. Fifteen patients (eight males, seven females, median age 65 years, median lesion size 20 mm) underwent percutaneous MWA for 15 renal tumors. All the procedures were performed in a dedicated angiography room setting; CBCT ablation planning capabilities included multimodality image fusion. Preoperative contrast-enhanced CT was available in 12 patients, whereas magnetic resonance imaging in the remaining. All patients were considered inoperable due to comorbidities, advanced age, and/or refusal to undergo surgery. Exclusion criteria were: tumors visible at unenhanced CBCT, metastatic disease, and uncorrected coagulopathy. Technical success and technical effectiveness were calculated. Procedural time, complications and recurrences were registered. MWA under CBCT-guidance with fusion technique was technically successful in 14 out of 15 cases (93%). The median procedural time was 45 min. No procedure-related complications were reported. No enhancing tissue was visualized in the area of ablation at 1-month follow-up. All 15 cases were recurrence-free at last follow-up assessment (median follow-up of 12 months); no cancer-specific deaths were registered. CBCT-CT/MR image fusion is technically feasible and safe in achieving correct targeting and complete ablation of renal lesions. This approach bears the potential to overcome most of the limitations of unenhanced CBCT guidance alone; larger series are needed to validate this technique.

Aberrant Intrinsic Neural Networks Strength in Individuals with "Smartphone Addiction": An MRI Data Fusion Study

Abstract Objectives: Excessive smartphone use, sometimes also referred to as "smartphone addiction", has increasingly attracted neuroscientific interest due to its similarities with other behavioral addictions, particularly internet gaming disorder. Little is known so far about the neural mechanisms underlying smartphone addiction. Here, we explored interrelationships between brain structure and function to specify neurobiological correlates of smartphone addiction on a neural system level. Methods: Gray matter volume and intrinsic neural activity was investigated in individuals with smartphone addiction (n = 20) compared to controls (n = 24), using multimodal magnetic resonance imaging and multivariate data fusion techniques, i.e., parallel Independent Component Analysis. Results: The joint analysis of both data modalities explored shared information between gray matter volume and intrinsic neural activity which were not revealed by previous modality-specific approaches. Two amplitude of low frequency fluctuations-based independent neural systems significantly differed between individuals with smartphone addiction and controls. A medial/dorsolateral prefrontal system exhibited lower functional network strength lower in smartphone addiction vs. controls, whereas the opposite pattern was detected in a parietal cortical/cerebellar system. Neural network strength was significantly related to duration of smartphone use and sleep difficulties. Conclusions: We show modality-specific associations of the brain's resting-state activity with distinct and shared smartphone addiction symptom dimensions. In particular, the data suggest contributions of aberrant prefrontal and parietal neural network strength as a possible signature of deficient executive control in smartphone addiction.

Multiparametric Magnetic Resonance Imaging of the Prostate and Prostate-specific Membrane Positron Emission Tomography Prior to Prostate Biopsy (MP4 Study)

BackgroundProstate-specific membrane antigen (PSMA) positron emission tomography/computerised tomography (PET/CT) is increasingly being utilised in the diagnostic pathway for prostate cancer (PCa). Recent publications have suggested that this might help identify those who can avoid biopsy. ObjectiveThe primary objective of this study was to determine whether PET magnetic resonance imaging (MRI) fusion could negate the need to biopsy prior to prostatectomy in a selected population of men. Design, setting, and participantMultiparametric MRI (mpMRI) for PCa is our standard of care prior to prostate biopsy. Biopsy-naïve men with one or more Prostate Imaging Reporting and Data System (PI-RADS) 4 or 5 lesions ≥10 mm on mpMRI were invited to undergo PSMA PET/CT prior to biopsy. Following ethics approval, 60 men were recruited between September 2020 and March 2021. The key exclusion criteria included a previous history of PCa and previous prostate surgery or biopsy. Outcome measurements and statistical analysisA positive PET MRI fusion scan was defined as “consistent with” as per the Memorial Sloan Kettering Cancer Center lexicon of certainty, and concordance with biopsy results was analysed. Clinically significant PCa (csPCa) was defined as grade group (GG) ≥2 on pathology. A chi-square analysis was performed with statistical significance defined at p < 0.05. Results and limitationsA total of 71 mpMRI lesions were positive on 61 (86%) PET MRI fusion scans. Fifty-nine of 61 lesions biopsied confirmed csPCa in 54 (92%). Of five of 59 lesions for which either biopsy was negative or low-grade cancer was found, three had rebiopsy of which two were confirmed to have csPCa corroborating with PET MRI fusion and one was reconfirmed to have GG1 only. For the remaining two, both had another lesion elsewhere in the gland confirming csPCa, and hence rebiopsy was not performed. Ultimately, 56 of 59 (95%) lesions with a positive PET MRI fusion scan were confirmed to have csPCa. All GG ≥3 cancers had a positive PET MRI fusion scan. ConclusionsThis prospective study of PET MRI fusion assessment of men with PI-RADS 4 or 5 lesion ≥10 mm on mpMRI confirms that the majority of men (95%) with a positive PET MRI fusion scan will have csPCa. This supports recently published retrospective data suggesting that selected men might avoid prostate biopsy prior to radical prostatectomy. Patient summaryIn this research, we have confirmed that prostate-specific membrane antigen positron emission tomography/computerised tomography in combination with magnetic resonance imaging could have an important role in enabling a diagnosis of prostate cancer. Using the combination of these scans, we could confidently predict the presence of aggressive prostate cancer in some men for which treatment is warranted. This means that there are some men who could possibility proceed directly to having prostate cancer surgery without the need for a confirmatory prostate biopsy.

Prediction of Alzheimer's progression based on multimodal Deep-Learning-based fusion and visual Explainability of time-series data

Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment and has no known treatment. The premise for delivering timely therapy is the early diagnosis of AD before clinical symptoms appear. Mild cognitive impairment is an intermediate stage in which cognitively normal patients can be distinguished from those with AD. In this study, we propose a hybrid multimodal deep-learning framework consisting of a 3D convolutional neural network (3D CNN) followed by a bidirectional recurrent neural network (BRNN). The proposed 3D CNN captures intra-slice features from each 3D magnetic resonance imaging (MRI) volume, whereas the BRNN module identifies the inter-sequence patterns that lead to AD. This study is conducted based on longitudinal 3D MRI volumes collected over a six-months time span. We further investigate the effect of fusing MRI with cross-sectional biomarkers, such as patients’ demographic and cognitive scores from their baseline visit. In addition, we present a novel explainability approach that helps domain experts and practitioners to understand the end output of the proposed multimodal. Extensive experiments reveal that the accuracy, precision, recall, and area under the receiver operating characteristic curve of the proposed framework are 96%, 99%, 92%, and 96%, respectively. These results are based on the fusion of MRI and demographic features and indicate that the proposed framework becomes more stable when exposed to a more complete set of longitudinal data. Moreover, the explainability module provides extra support for the progression claim by more accurately identifying the brain regions that domain experts commonly report during diagnoses.

Where Position Matters—Deep-Learning–Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation

IntroductionRecent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces. Here, we explored a deep-learning approach to directly relate positions (usually the lead position) in postoperative CT images to the native anatomy of the midbrain and group space. Materials and MethodsDeep learning is used to create derived tissue contrasts (white matter, gray matter, cerebrospinal fluid, brainstem nuclei) based on the CT image; that is, a convolution neural network (CNN) takes solely the raw CT image as input and outputs several tissue probability maps. The ground truth is based on coregistrations with MR contrasts. The tissue probability maps are then used to either rigidly coregister or normalize the CT image in a deformable way to group space. The CNN was trained in 220 patients and tested in a set of 80 patients. ResultsRigorous validation of such an approach is difficult because of the lack of ground truth. We examined the agreements between the classical and proposed approaches and considered the spread of implantation locations across a group of identically implanted subjects, which serves as an indicator of the accuracy of the lead localization procedure. The proposed procedure agrees well with current magnetic resonance imaging–based techniques, and the spread is comparable or even lower. ConclusionsPostoperative CT imaging alone is sufficient for accurate localization of the midbrain nuclei and normalization to the group space. In the context of group analysis, it seems sufficient to have a single postoperative CT image of good quality for inclusion. The proposed approach will allow researchers and clinicians to include cases that were not previously suitable for analysis.

MDRANet: A multiscale dense residual attention network for magnetic resonance and nuclear medicine image fusion

Magnetic resonance and nuclear medicine images are the two categories of multimodal medical images. Magnetic resonance images reveal physiological anatomical information of patients, and nuclear medicine images accurately show tissue lesion information. Through medical image fusion algorithms, these fusion images containing both tissue lesion information and physiological anatomical information are obtained to provide sufficient information for clinical medical technologies. However, most existing fusion algorithms are based on mathematical transform domains, and these fusion results have the weaknesses of blurred edges, color distortion and detail loss. To address these problems, a multiscale dense residual attention network (MDRANet) is proposed and applied to magnetic resonance and nuclear medicine image fusion. MDRANet combines multiscale dense network and multiscale residual attention network to extract and enhance deep features. Moreover, four different loss functions are used to optimize MDRANet and improve the fusion quality. The experimental results show that the fusion results of our proposed algorithm have richer details and better objective metrics compared with the reference algorithms.

Fusion of Diffusion-Weighted Magnetic Resonance Imaging and High-Resolution Computed Tomography Scan As a Preoperative Tool for Classification of Middle Ear Cholesteatoma.

The role of imaging in cholesteatoma continues to evolve with excellent bony details provided by high-resolution computed tomography and high soft tissue identification for cholesteatoma by diffusion-weighted magnetic resonance imaging. The fusion of highresolution computed tomography and diffusion-weighted magnetic resonance imaging combines the advantages of both imaging techniques. A random sample of 40 consecutive patients with chronic suppurative otitis media with cholesteatoma was included in this study. Both high-resolution computed tomography of the petrous bone and non-echoplanar diffusion-weighted magnetic resonance imaging were performed. This was followed by their fusion. Patients were classified according to The European Academy of Otology and Neurotology, in cooperation with the Japanese Otological Society Joint Consensus Statement on the Definitions, Classification, and Staging of Middle Ear Cholesteatoma. All patients were operated, and the technique was tailored according to the data obtained from the preoperative fusion of computed tomography and diffusion-weighted magnetic resonance imaging and the intraoperative findings. Patients were equally divided between males and females with a mean age of 26.8 years of which 52.5% were left-sided ears. The fusion of high-resolution computed tomography and diffusion-weighted magnetic resonance imaging had a 100% sensitivity and 88.9% specificity regarding The European Academy of Otology and Neurotology, in cooperation with the Japanese Otological Society classification. On the other hand, it showed 100% specificity and 100% sensitivity for all middle ear subsites except sinus tympani which obtained 55.56% sensitivity and 100% specificity. In all patients with preoperative fusion showing cholesteatoma not reaching the mastoid antrum (30%), exclusive endoscopic approach was employed, and no postauricular incision was needed. The fusion of high-resolution computed tomography and diffusion-weighted magnetic resonance imaging images is an accurate tool for localizing cholesteatoma in various middle ear cleft subsites. This makes it a valuable tool for cholesteatoma classification and staging and surgical planning preoperatively.

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders.

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.

Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer’s disease using fusion of MRI-PET imaging

Automated detection of dementia stage using multimodal imaging modalities will be helpful for improving the clinical diagnosis. In this study, we develop the Inception-ResNet wrapper model in differentiating the healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using conjoint magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. We use T1-weighted MR and PET images of individuals aged between 42 and 95 years, including HC, MCI and AD patients. We first perform 3D tissue segmentation of MR images after skull striping. The atlas-based segmented MR image tissue is fused with PET image. Then we transform PET images from RGB to HSI color space and apply fusion of MRI with PET images using two-dimensional Fourier and discrete wavelet transform (DWT) and then reconstruct the MR-PET fused image using inverse Fourier and DWT methods. After the fusion of MRI and PET imaging modalities, we used 60 % training, 20 % for validation and the remaining 20 % as a test set using various convolutional neural networks. We found the proposed model as the best classifier with an accuracy of 95.5 %, 94.1 % and 95.9 % in classifying HC vs MCI, MCI vs AD and AD vs HC respectively when compared to the existing methods. We conclude that the proposed deep learning model has potential in automated classification of healthy and dementia stages using combined MRI and PET modalities with good performance.

Reduction of Inter-Observer Variability Using MRI and CT Fusion in Delineating of Primary Tumor for Radiotherapy in Lung Cancer with Atelectasis

<h3>Purpose/Objective(s)</h3> To compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis. Accurately identify the outline volume of the tumor and reduce the probability of normal tissue complications. <h3>Materials/Methods</h3> Retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All patients were scanned with MRI and CT in the same supine position within one week. All positioning images were transferred to treatment planning systems. According to the anatomy of sternum, rib, vertebral body, pulmonary artery and tumor, each MRI sequence (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) was automatically registered with CT through the planning system, and the fusion stability was increased by manual correction. Five radiation oncologists with rich clinical experience delineate tumor boundary to obtain the gross tumor volume (GTV). The boundary between tumor and atelectasis was determined by referring to CT structure, coronal and sagittal MRI images when drawing CT-MRI fusion images. The target areas of CT and MRI images were sketched on the same day. Consistence index (CI) and dice coefficient (DC) were used to measure differences among observers. <h3>Results</h3> The differences of delineation mean volumes, CI and DC among CT and MRI were significant. Multiple comparisons were made between MRI sequences and CT. The mean volume of DWI, T2WI, T1WI + C 10 minutes sequence in the target area is significantly smaller than that on CT sequence, but the consistency is higher than that of CT sequences. <h3>Conclusion</h3> The recognition of atelectasis by MRI was better than that by CT, which could reduce inter-observer variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis. Abbreviations: CT: Computed tomography; GTV: Gross tumor volume; MRI: Magnetic resonance imaging; T1WI: WATER:BH AX LAVA-Flex; T1WI+C: WATER:BH AX LAVA-Flex-C; T1WI+C Delay: WATER:BH AX LAVA-Flex-C delay; T1WI+C 10 minutes: WATER:BH AX LAVA-Flex-C 10min; T2WI:RTr Ax T2 fs Propeller; CI: Consistence index; DC: Dice coefficient.

Accuracy of elastic fusion biopsy: Comparing prostate cancer detection between targeted and systematic biopsy.

When performing targeted biopsy (TBx), the need to add systematic biopsies (SBx) is often debated. Aim of the study is to evaluate the added value of SBx in addition to TBx in terms of prostate cancer (PCa) detection rates (CDR), and to test the concordance between multiparametric magnetic resonance imaging (mpMRI) findings and fusion biopsy results in terms of cancer location. We performed a retrospective, multicentric study that gathered data on 1992 consecutive patients who underwent elastic fusion biopsy between 2011 and 2020. A standardized approach was used, with TBx (2-4 cores per target) followed by SBx (12-14 cores). We assessed CDR of TBx, of SBx, and TBx+SBx for all cancers and clinically significant PCa (csPCa), defined as ISUP score ≥2. CDR was evaluated according to radiological and clinical parameters, with a particular focus on PI-RADS 3 lesions. In a subgroup of 1254 patients we tested the discordance between mpMRI findings and fusion biopsy results in terms of cancer location. Uni- and multivariable logistic regression analyses were performed to identify predictors of CDR. CDR of TBx+SBx was 63.0% for all cancers and 38.8% of csPCa. Per-patient analysis showed that SBx in addition to TBx improved CDR by 4.5% for all cancers and 3.4% for csPCa. Patients with lesions scored as PI-RADS 3, 4, and 5 were diagnosed with PCa in 27.9%, 72.8%, and 92.3%, and csPCa in 10.7%, 43.6%, and 69.3%, respectively. When positive, PI-RADS 3 lesions were ISUP grade 1 in 61.1% of cases. Per-lesion analysis showed that discordance between mpMRI and biopsy was found in 56.6% of cases, with 710 patients having positive SBx outside mpMRI targets, of which 414 (58.0%) were clinically significant. PSA density ≥0.15 was a strong predictor of CDR. The addition of systematic mapping to TBx contributes to a minority of per-patient diagnoses but detects a high number of PCa foci outside mpMRI targets, increasing biopsy accuracy for the assessment of cancer burden within the prostate. High PSA-density significantly increases the risk of PCa, both in the whole cohort and in PI-RADS 3 cases.

Multiparametric MRI Fusion-Guided Prostate Biopsy for Detection of Clinically Significant Prostate Cancer Eliminates the Systemic Prostate Biopsy

The primary objective of this study was to demonstrate the high accuracy of multiparametric magnetic resonance imaging and ultrasound fusion (mpMRI/US)-guided targeted prostate biopsy for the detection of clinically significant prostate cancer (PCa) and to show that adapted systematic biopsy (AdSB) does not provide additional benefit in detecting clinically significant prostate cancer (PCa). In total, 283 patients have been included in the study. All patients underwent the mpMRI/US biopsies, which have been performed with the “BioJet” fusion system (D&amp;K Technologies, Barum, Germany) using the transperineal approach by a single interventional radiologist. Lesion-targeted and systematic biopsies have been done when 2–4 cores have been taken from each PI-RADS 3–5 lesion, followed by AdSB. This study demonstrated that targeted prostate biopsy is sufficient for safe and sensitive identification of clinically significant PCa in primary biopsy-naïve cases without the need to perform adapted systematic biopsy.

Aberrant intrinsic neural network strength in individuals with "smartphone addiction": An MRI data fusion study.

Background and objectivesExcessive smartphone use, also referred to as “smartphone addiction” (SPA), has increasingly attracted neuroscientific interest due to its similarities with other behavioral addictions, particularly internet gaming disorder. Little is known about the neural mechanisms underlying smartphone addiction. We explored interrelationships between brain structure and function to specify neurobiological correlates of SPA on a neural system level.MethodsGray matter volume (GMV) and intrinsic neural activity (INA) were investigated in individuals with SPA (n = 20) and controls (n = 24), using multimodal magnetic resonance imaging and multivariate data fusion techniques, that is, parallel independent component analysis.ResultsThe joint analysis of both data modalities explored shared information between GMV and INA. In particular, two amplitudes of low frequency fluctuations‐based independent neural systems significantly differed between individuals with SPA and controls. A medial/dorsolateral prefrontal system exhibited lower functional network strength in individuals with SPA versus controls, whereas the opposite pattern was detected in a parietal cortical/cerebellar system. Neural network strength was significantly related to duration of smartphone use and sleep difficulties.Discussion and conclusionsWe show modality‐specific associations of the brain's resting‐state activity with distinct and shared SPA symptom dimensions. In particular, the data suggest contributions of aberrant prefrontal and parietal neural network strength as a possible signature of deficient executive control in SPA.Scientific significanceThis study suggests distinct neural mechanisms underlying specific biological and behavioral dimensions of excessive smartphone use.

Single-port Robotic Transvesical Partial Prostatectomy for Localized Prostate Cancer: Initial Series and Description of Technique

BackgroundPartial prostatectomy has been described as an alternative to focal therapy for the management of localized low- and intermediate-risk prostate cancer. ObjectiveTo describe early outcomes and technique for single-port (SP) transvesical partial prostatectomy. Design, setting, and participantsA retrospective analysis was performed for nine patients with low-volume, localized, low- to intermediate-risk prostate cancer (Gleason ≤7) undergoing SP transvesical partial prostatectomy replicating the inclusion criteria for focal therapy by a single surgeon from November 2020 to March 2022. Surgical procedureThe daVinci SP access port was inserted percutaneously into the bladder and pnuemovesicum was achieved. The camera, robotic instruments, assistant port, and flexible suction tubing were introduced through the access port. The Koelis transrectal ultrasound with preoperative prostate magnetic resonance imaging fusion was used for intraoperative guidance. MeasurementsDemographic information, intraoperative variables, and postoperative outcomes were collected in an institutional review board–approved database, and a descriptive statistical analysis was performed. Results and limitationsAll cases were completed without requiring extra ports or conversion. No intraoperative complications were noted, and all patients were discharged on the day of surgery. Pathology showed Gleason scores of 3 + 3 = 6 in one case, 3 + 4 = 7 in seven cases, and 4 + 3 = 7 in one case, all with negative intraoperative margin assessment. At 6 wk, the median prostate-specific antigen was 0.5 and the median Sexual Health Inventory for Men score was 17.5 from 23 preoperatively. All patients were continent at 6 wk. The limitations include a small number of patients, short follow-up, and single-surgeon experience. ConclusionsWe demonstrated the feasibility of the SP robotic transvesical partial prostatectomy. Early functional outcomes show impressive time to continence and erectile function. Continued follow-up will evaluate long-term oncologic outcomes. Patient summaryWe performed partial prostatectomies in selected patients as an alternative to focal therapy using a novel transvesical single-port approach. Our approach was safe and feasible, with fewer complications and promising initial return to continence and erectile function.

Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis

PurposeTo compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis.MethodA retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All positioning images were transferred to Eclipse treatment planning systems (TPSs). Six MRI sequences (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) were registered with positioning CT. Five radiation oncologists delineated the tumor boundary to obtain the gross tumor volume (GTV). Conformity index (CI) and dice coefficient (DC) were used to measure differences among observers.ResultsThe differences in delineation mean volumes, CI, and DC among CT and MRIs were significant. Multiple comparisons were made between MRI sequences and CT. Among them, DWI, T2WI, and T1WI+C 10 minutes sequences were statistically significant with CT in mean volumes, DC, and CI. The mean volume of DWI, T2WI, and T1WI+C 10 minutes sequence in the target area is significantly smaller than that on the CT sequence, but the consistency is higher than that of CT sequences.ConclusionsThe recognition of atelectasis by MRI was better than that by CT, which could reduce interobserver variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis.