Magnetic Resonance Imaging Fusion Research Articles

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.

Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning

Dear Editor, In recent years, multi-modal medical image fusion has received widespread attention in the image processing community. However, existing works on medical image fusion methods are mostly devoted to pursuing high performance on visual perception and objective fusion metrics, while ignoring the specific purpose in clinical applications. In this letter, we propose a glioma segmentation-oriented multi-modal magnetic resonance (MR) image fusion method using an adversarial learning framework, which adopts a segmentation network as the discriminator to achieve more meaningful fusion results from the perspective of the segmentation task. Experimental results demonstrate the advantage of the proposed method over some state-of-the-art medical image fusion methods.

A Novel MRI And CT Image Fusion Based on Discrete Wavelet Transform and Principal Component Analysis for Enhanced Clinical Diagnosis

A large percentage of healthcare resources, including imaging tools, like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been dedicated to the management of affected patients in this pandemic of Coronavirus disease 2019 (COVID-19). The diagnostic modalities in medical research are improving at a rapid pace with an objective to acquire maximum information with as little data as possible without any artifacts. That is where image fusion comes into the picture. It is a technique of merging source medical pictures to maximize the necessary information. CT is generally used for bony structures, whereas MRI is more appropriate for soft tissues. A fusion of MRI and CT images would lead to enhancement of the overall image quality while giving comprehensive information, at the same time artifacts are also eliminated. Image fusion methods are applied in medical science and various other sectors. Several image processing techniques are used in medical diagnostics, like Principal Component analysis (PCA), Intensity-Hue-Saturation, Discrete Wavelet Transform (DWT), and others. This study suggests an image fusion algorithm utilising the principal component averaging and the DWT along with the performance analysis of the fusion of the MRI and CT images of brain. The technique used in our study significantly enhances the image quality in terms of various fusion performance measures that helps the medical practitioners to diagnose any infection and aids in its treatment.&#x0D;

Optimized Deep Brain Stimulation Surgery to Avoid Vascular Damage: A Single-Center Retrospective Analysis of Path Planning for Various Deep Targets by MRI Image Fusion.

Co-registration of stereotactic and preoperative magnetic resonance imaging (MRI) images can serve as an alternative for trajectory planning. However, the role of this strategy has not yet been proven by any control studies, and the trajectories of commonly used targets have not been systematically studied. The purpose of this study was to analyze the trajectories for various targets, and to assess the role of trajectories realized on fused images in preventing intracranial hemorrhage (ICH). Data from 1019 patients who underwent electrode placement for deep brain stimulation were acquired. Electrode trajectories were not planned for 396 patients, whereas trajectories were planned for 623 patients. Preoperative various MRI sequences and frame-placed MRI images were fused for trajectory planning. The patients’ clinical characteristics, the stereotactic systems, intracranial hemorrhage cases, and trajectory angles were recorded and analyzed. No statistically significant differences in the proportions of male patients, patients receiving local anesthesia, and diseases or target distributions (p > 0.05) were found between the trajectory planning group and the non-trajectory planning group, but statistically significant differences were observed in the numbers of both patients and leads associated with symptomatic ICH (p < 0.05). Regarding the ring and arc angle values, statistically significant differences were found among various target groups (p < 0.05). The anatomic structures through which leads passed were found to be diverse. Trajectory planning based on MRI fusion is a safe technique for lead placement. The electrode for each given target has its own relatively constant trajectory.

Prospective analysis of pain expectancy and experience during MR-fusion prostate biopsy: does reality match patients’ expectancy?

PurposeMultiparametric magnetic resonance imaging fusion targeted prostate biopsy (MR-TB) has emerged to the biopsy technique of choice for evaluation of patients with suspected prostate cancer (PCA). The study aimed to determine expected and experienced pain during MR-TB depending on patients’ psychological state.MethodsWe prospectively enrolled 108 men with suspicion of PCA who underwent MR-TB. All patients completed self-reported validated questionnaires assessing pain, stress, self-efficacy, anxiety and study-specific questionnaires on expected and experienced pain before, during and after MR-TB. Patient characteristics and survey scores were obtained.ResultsOverall, pain levels during MR-TB were low (mean 2.8/10 ± 2.5 Numerical Rating Scale, NRS). 10/86 (11.6%) participants reported severe pain (≥ 7/10 NRS). Pain correlated significantly with anxiety (r = 0.42), stress (r = 0.22) and pain expectancy (r = 0.58). High self-efficacy did not show increased pain resilience. Participants anticipated more pain than experienced during each step of MR-TB with significant differences concerning local anesthesia and core sampling (both p < 0.001), among others. Expectancy and actual pain did not match regarding severity and impact of the total amount of cores taken (p < 0.05). Independent predictors of increased pain at biopsy were prostate volume > 50 ml (p = 0.0179) and expected pain during rectal manipulation (p < 0.001).ConclusionPain during MR-TB can be positively influenced by reducing men’s anxiety, stress and pain expectancy. To meet the needs of the audience, clinicians should address concrete pain levels of each procedural step and consider special treatment for patients with prostate volume > 50 ml and men reporting on increased rectal sensitivity.

Multi-parametric Magnetic Resonance Imaging Fusion for Automatic Classification of Prostate Cancer.

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mp-MRI) has recently gained great research interest. In this work, a fully automatic CAD pipeline of PCa using mp-MRI data is presented. In order to fully explore the mp-MRI data, we systematically investigate three multi-modal medical image fusion strategies in convolutional neural networks, namely input-level fusion, feature-level fusion, and decision-level fusion. Extensive experiments are conducted on two datasets with different PCa-related diagnostic tasks. We identify a pipeline that works relatively the best for both diagnostic tasks, two important components of which are stacking three adjacent slices as the input and performing decision-level fusion with specific loss weights. Clinical relevance- This work provides a practical method for automated diagnosis of PCa based on multi-parametric MRI.

MRI and FUNDUS image fusion for improved ocular biometry in Ocular Proton Therapy

IntroductionOcular biometry in Ocular Proton Therapy (OPT) currently relies on a generic geometrical eye model built by referencing surgically implanted markers. An alternative approach based on image fusion of volumetric Magnetic Resonance Imaging (MRI) and panoramic fundus photography was investigated. Materials and methodsEighteen non-consecutive uveal melanoma (UM) patients, who consented for an MRI and had their tumour base visible on panoramic fundus photography, were included in this comparative analysis. Through generating digitally-reconstructed projections from MRI images using the Lambert azimuthal equal-area projection, 2D-3D image fusion between fundus photography and an eye model delineated on MRI scans was achieved and allowed for a novel definition of the target base (MRI + FCTV). MRI + FCTV was compared with MRI-only delineation (MRIGTV) and the conventional (EyePlan) target definition (EPCTV). ResultsThe combined use of fundus photography and MRI to define tumour volumes reduced the average discrepancies by almost 65% with respect to the MRI only tumour definitions when comparing with the conventionally planned EPCTV. With the proposed method, shallow sub-retinal tumour infiltration, otherwise invisible on MRI, can be included in the target volume definition. Moreover, a novel definition of the fovea location improves the accuracy and personalisation of the 3D eye model. ConclusionMRI and fundus image fusion overcomes some of the limitations of ophthalmological MRI for tumour volume definition in OPT. This novel eye tumour modelling method might improve treatment planning personalisation, allowing to better anticipate which patients could benefit from prophylactic treatment protocols for radiation induced maculopathy.

Experience in utilizing a novel 3D digital model with CT and MRI fusion data in sarcoma evaluation and surgical planning.

To assess sarcoma margins with more accuracy and aid surgical planning, we constructed three-dimensional (3D) digital models with computed tomography(CT) and magnetic resonance imaging(MRI) image fusion data and validated the preciseness of the models by comparing them with 3D models constructed with CT only data. We retrospectively reviewed a consecutive set of patients treated in our center who were preoperatively evaluated with the fusion image model. Models based on fusion images or CT-onlydata were constructed. Volumes of both tumors were calculated and the tumors were overlapped to see the location of differences between the two models. A consecutive 12 cases (4 male vs. 8 female) were included in this study. Most of the tumors were located in the pelvic bone or spine. The volume of the two tumor models was different and the differences were mainly in the peripheral region of the tumor. CT and MRI fusion image 3D models are more accurate than models with CT-onlydata and can be very helpful in preoperative planning of sarcoma patients.

Percutaneous radiofrequency trigeminal rhizotomy benefits in patients with refractory trigeminal neuralgia.

A significant number of patients suffers from refractory trigeminal neuralgia (TN) after receiving microvascular decompression (MVD) or other neuro-destructive procedure such as gamma knife radiosurgery (GKRS). This study aims to demonstrate a remediable, reproducible approach to treating refractory pain effectively by percutaneous radiofrequency trigeminal rhizotomy (RF-TR).A total of 392 patients with TN were treated by RF-TR during the past 10 years. Among these patients, 48 cases who had received either MVD, GKRS alone, or a combination of both were assigned to group A. Those who had not received any form of treatment (125 patients) or failed to respond medically (130 patients) were assigned as the control group (group B). All the RF-TR were performed by a single surgeon with the aid of intraoperative computed tomography (iCT)-based neuronavigation with magnetic resonance (MR) image fusion. The outcome measure was the numerical rating scale (NRS) expressed subjectively by patients. The paired Student t test and the analysis of covariance (ANCOVA) were used for statistical analysis.In group A, 21 of 24 patients (88%) had significant improvement (NRS change ≥5) in facial pain after RF-TR. The average NRS score was 9.75 ± 0.53 before the procedure and 1.92 ± 3.35 post-treatment (significant NRS decrease [P = .000]). On the other hand, in group B, 226 of 255 patients (89%) also had dramatic amelioration of facial pain after RF-TR. The average NRS score was 9.46 ± 0.69 before the procedure and 1.62 ± 2.85 post-treatment (7.84 ± 2.82 in NRS decrease [P = .008]). By using a univariate ANCOVA, no statistical significance was found in NRS score improvement between the two groups.Repeated MVD and GKRS for refractory TN may be less desirable due to a greater risk of mortality (up to 0.8%) and morbidity (4% of serious complications). Conversely, RF-TR administration with the novel navigation technique by using iCT and MR image fusion is free from any remarkable and irreversible morbidities. In this study, RF-TR not only provided an alternative and effective strategy if TN recurred but also resulted in the same NRS score improvement regardless of the status of prior treatment.

Intraoperative Integration of Multimodal Imaging to Improve Neuronavigation: A Technical Note

Brain shift may cause significant error in neuronavigation, leading the surgeon to possible mistakes. Intraoperative magnetic resonance imaging (MRI) is the most reliable technique in brain tumor surgery. Unfortunately, it is highly expensive and time consuming and, at the moment, it is available only in few neurosurgical centers. In this case series the surgical workflow for brain tumor surgery is described where neuronavigation of preoperative MRI, intraoperative computed tomography (CT) scan, and ultrasound (US) as well as rigid and elastic image fusion between preoperative MRI and intraoperative US and CT, respectively, was applied to 4 brain tumor patients in order to compensate for surgically induced brain shift by using a commercially available software (Elements Image Fusion 4.0 with Virtual iMRI Cranial; Brainlab AG, München, Germany). Four illustrative cases demonstrated successful integration of different components of the described intraoperative surgical workflow. The data indicate that intraoperative navigation update is feasible by applying intraoperative 3-dimensional US and CT scanning as well as rigid and elastic image fusionapplied depending on the degree of observed brain shift. Integration of multiple intraoperative imaging techniques combined with rigid and elastic image fusion of preoperative MRI may reduce the risk of incorrect neuronavigation during brain tumor resection. Further studies are needed to confirm the present findings in a larger population.

Future Prospects of Positron Emission Tomography-Magnetic Resonance Imaging Hybrid Systems and Applications in Psychiatric Disorders.

A positron emission tomography (PET)–magnetic resonance imaging (MRI) hybrid system has been developed to improve the accuracy of molecular imaging with structural imaging. However, the mismatch in spatial resolution between the two systems hinders the use of the hybrid system. As the magnetic field of the MRI increased up to 7.0 tesla in the commercial system, the performance of the MRI system largely improved. Several technical attempts in terms of the detector and the software used with the PET were made to improve the performance. As a result, the high resolution of the PET–MRI fusion system enables quantitation of metabolism and molecular information in the small substructures of the brainstem, hippocampus, and thalamus. Many studies on psychiatric disorders, which are difficult to diagnose with medical imaging, have been accomplished using various radioligands, but only a few studies have been conducted using the PET–MRI fusion system. To increase the clinical usefulness of medical imaging in psychiatric disorders, a high-resolution PET–MRI fusion system can play a key role by providing important information on both molecular and structural aspects in the fine structures of the brain. The development of high-resolution PET–MR systems and their potential roles in clinical studies of psychiatric disorders were reviewed as prospective views in future diagnostics.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

BackgroundThe global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score.MethodsWe enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model’s performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score.ResultsThe area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936.ConclusionsRadiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

MP55-03 USING MAGNETIC RESONANCE IMAGING TO DEFINE FOCAL THERAPY TEMPLATES IN PATIENTS WITH INTERMEDIATE-RISK PROSTATE CANCER

You have accessJournal of UrologyCME1 May 2022MP55-03 USING MAGNETIC RESONANCE IMAGING TO DEFINE FOCAL THERAPY TEMPLATES IN PATIENTS WITH INTERMEDIATE-RISK PROSTATE CANCER Alberto Pieretti, Hyunseon Kang, Haesun Choi, Miao Zhang, Brian Chapin, John Ward, Patricia Troncoso, and Justin Gregg Alberto PierettiAlberto Pieretti More articles by this author , Hyunseon KangHyunseon Kang More articles by this author , Haesun ChoiHaesun Choi More articles by this author , Miao ZhangMiao Zhang More articles by this author , Brian ChapinBrian Chapin More articles by this author , John WardJohn Ward More articles by this author , Patricia TroncosoPatricia Troncoso More articles by this author , and Justin GreggJustin Gregg More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002634.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Focal therapy has emerged as a possible treatment option in well-selected patients with localized prostate cancer. However, currently, there is a paucity of data on the long-term oncologic outcomes of focal therapy and on the risk of undertreating patients by using biopsy results and prostate magnetic resonance imaging (MRI) with PI-RADS 3-5 lesions to define focal therapy treatment templates. This study compared focal therapy templates with final pathology results after radical prostatectomy. METHODS: We retrospectively reviewed our institutional database and identified all patients with a biopsy-confirmed MRI lesion with grade group (GG)2 or GG3, and systematic biopsies or other targeted biopsies with <GG2 from 2014-2020; patients with possible extracapsular extension on MRI were excluded. Ablation templates using a 1-cm margin or “hockey stick” were used to assess the elimination of clinically significant prostate cancer. Possible undertreatment was described as “in-field” (on the basis of the MRI lesion), “out-of-field” (a secondary clinically significant prostate cancer that was missed on the template but identified on final pathology), or any failure. RESULTS: Sixty-eight patients met inclusion criteria. The mean tumor size was 1.24 cc on MRI compared to 1.79 cc on final pathology. Using the 1-cm ablation margin, in field failure was present in 31% of patients, with 76% on the craniocaudal axis; out of field failure in 45%; and any failure in 56%. Using the hockey stick template, any failure was present in 26% of patients. Among patients with posterior-predominant lesions, 63% of cases were associated with any failure using the 1-cm margin compared to 26% using the hockey stick template. In anterior-predominant lesions, 48% of cases had any failure using the 1-cm margin compared to 21% using the hockey stick template. CONCLUSIONS: In prostate cancer patients with isolated GG2 or GG3 disease following MRI fusion biopsy, MRI underestimates tumor diameter. 1cm margins have relatively high rates of in-field and out-of-field failure. These data can be used in the planning of prospective focal therapy interventions. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e937 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alberto Pieretti More articles by this author Hyunseon Kang More articles by this author Haesun Choi More articles by this author Miao Zhang More articles by this author Brian Chapin More articles by this author John Ward More articles by this author Patricia Troncoso More articles by this author Justin Gregg More articles by this author Expand All Advertisement PDF DownloadLoading ...