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Background: As a breast cancer screening test, FAST MRI has the potential to combine the sensitivity of dynamic contrast enhanced breast MRI (DCE-MRI) with costs nearer to those of mammography. In the UK the NHS Breast Screening Programme (NHSBSP) screens women aged 50-70 with a mammogram every 3 years and has a workforce of radiologists, breast clinicians and advanced practitioner radiographers who interpret over 2 million screening mammograms annually. For FAST MRI to be used as a UK population screening tool it will need to be interpreted by the existing multi-professional workforce of mammogram readers. Our research team developed a single study day of standardised training in FAST MRI interpretation. In our single centre pilot study, a group of readers with no previous experience of DCE-MRI interpretation achieved an accuracy just 5% less than expert DCE-MRI readers (Br J Radiol 2019:92 DOI:10.1259/bjr.20190663). Our current study aimed to validate the feasibility of training the NHSBSP workforce in FAST MRI interpretation. We developed display software (MedXViewer) containing ground truth information for each FAST MRI case to train and assess mammogram readers, who represented multiple NHSBSP sites in the South West Region of England. Methods: The study was funded by the National Institute for Health Research (Research for Patient Benefit funding stream; ISRCTN 16624917). A per breast analysis of the frequency of results against the true outcome was obtained overall and for each reader. Differences in accuracy, sensitivity and specificity across reader groups (group 1 = mammogram readers experienced in DCE-MRI interpretation; group 2 = mammogram readers with no previous experience in DCE-MRI interpretation) were analysed using a multilevel generalised mixed model to account for multiple readers per case. Results: 37 NHSBSP mammogram readers at 6 NHSBSP sites attended the training day and completed interpretation of the summative assessment dataset (17 in group 1 and 20 in group 2). All 37 readers completed the reading task of 125 cases (250 breasts), for a total of 9250 reads. Median interpretation time was 99.08 seconds (interquartile range 66.83-150.54). The table below shows per breast analysis comparing the readers’ classification with the true outcome (cancer or normal): The concordance with the ground truth (accuracy) of 83% achieved by Group 2 (4129/5000 (95% CI 82-84%)) was significantly lower than that achieved by Group 1 (3814/4250 (90%; 89-91%); p Conclusions: This study validates the feasibility of training the workforce of mammogram readers to interpret FAST MRI with only one day of standardised training. Improvement in performance with experience by the group 2 readers indicates a learning curve and suggests the performance gap between the two groups might be narrowed by further training for group 2. TotalGroup 1Group 2MeasureConcordance (Accuracy)7943/9250 (86%)3814/4250 (90%)4129/5000 (83%)True positive rate (Sensitivity)1806/2109 (86%)858/969 (89%)948/1140 (83%)True negative rate (Specificity)6137/7141 (86%)2956/3281 (90%)3181/3860 (82%)False positive rate1004/7141 (14%)325/3281 (10%)679/3860 (18%)False negative rate303/2109 (14%)111/969 (11%)192/1140 (17%) Citation Format: Lyn IL Jones, Rebecca Geach, Sam A Harding, Andrea Marshall, Sadie McKeown-Keegan, Sian Taylor-Phillips, Premkumar Elangovan, Mark Halling-Brown, Sarah Vinnicombe, Elizabeth O'Flynn, Hesam Ghiasvand, Claire T Hulme, Janet A Dunn. Training multi-professional mammogram readers to interpret abbreviated breast MRI (FAST MRI): A UK multi-centre study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-31.

Non-Cartesian MRI k-space trajectories provide faster and more motion-robust data acquisitions than those of Cartesian trajectories. In this paper, we focus on the rosette trajectory and generalize the weighted rosette trajectories for fast undersampled k-space data acquisition. However, single-slice imaging using the rosette trajectory will be affected by the off-resonance effect. To reduce the artifacts from off-resonance slices, this paper introduced an adaptive iterative reconstruction method derived from the TV and nuclear norm regularization terms for raw MRI data reconstruction. This reconstruction problem was separated into two subproblems and solved with an adaptive regularization parameter. The results show that the weighted rosette trajectory that offers short acquisition times and good off-resonance behaviors with little blurring can be used for reconstruction with adaptive regularization parameters and achieve a superior performance.

BACKGROUND AND PURPOSE: Conventional single-shot FSE commonly used for fast MRI may be suboptimal for brain evaluation due to poor image contrast, SNR, or image blurring. We investigated the clinical performance of variable refocusing flip angle single-shot FSE, a variation of single-shot FSE with lower radiofrequency energy deposition and potentially faster acquisition time, as an alternative approach to fast brain MR imaging. MATERIALS AND METHODS: We retrospectively compared half-Fourier single-shot FSE with half- and full-Fourier variable refocusing flip angle single-shot FSE in 30 children. Three readers reviewed images for motion artifacts, image sharpness at the brain-fluid interface, and image sharpness/tissue contrast at gray-white differentiation on a modified 5-point Likert scale. Two readers also evaluated full-Fourier variable refocusing flip angle single-shot FSE against T2-FSE for brain lesion detectability in 38 children. RESULTS: Variable refocusing flip angle single-shot FSE sequences showed more motion artifacts (P

Objective The manuscript discusses landmark studies using abbreviated MRI for breast cancer screening. This includes abbreviated dynamic contrast enhanced MRI and diffusion weighted imaging. Our institutional experience with abbreviated MR protocol for breast cancer screening is also described. Conclusion Abbreviated MRI protocols were found to demonstrate value for screening of breast cancer. It has been shown that abbreviated protocol MRI provides similar diagnostic sensitivities to full protocol MRI for breast cancer in women with increased lifetime risk. Our institutional abbreviated MRI protocol for breast cancer offers improved time and workflow efficiencies and has the potential to increase the number of breast cancers detected and the detection of pathologically relevant invasive breast cancer at earlier stages.

Background Rapid magnetic resonance imaging (MRI) protocols may be effective in the emergency department (ED) to evaluate nontraumatic neurologic complaints. We evaluate neuroimaging (rapid MRI [rMRI]), head computerized tomography [HCT], and full MRI) use following widespread implementation of rMRI protocols in a pediatric emergency department (ED). Methods We conducted a retrospective study in a tertiary care pediatric ED of encounters with neuroimaging during two 9-month periods: one prior to (control period) and one after generalized availability of 4 rMRI protocols (rMRI period). The primary outcome was differences in neuroimaging rates between the two periods. Secondary outcomes included ED process measures, unsuccessful imaging, and undetected pathology, with full MRI within 14 days as the reference standard. Results There were 1052 encounters with neuroimaging during the control and 1308 during the rMRI periods. Differences in neuroimaging between periods were 27.7% for rMRI (95% CI, 24.4, 31.0), − 21.5% for HCT (95% CI, − 25.5, − 17.5), and − 6.2% for full MRI (95% CI, − 9.3, − 3.1%.) Time to imaging (182 [IQR 138–255] versus 86 [IQR 52–137] minutes) as well as ED length of stay (396 [IQR 304–484] versus 257 [IQR 196–334] minutes) was longer for rMRI versus HCT (p p p n = 2) and patient motion (n = 20). None of the rMRI studies with full MRI follow-up imaging had undetected pathology; the false negative rate for the HCT exams was as high as 25%. Conclusions After routine ED use of 4 rMRI protocols, there was a more than 20% decrease in HCT use without missed diagnoses. Time to neuroimaging and length of stay were longer for rMRI than HCT, with higher rates of unsuccessful imaging. Despite these limitations, rMRI may be an alternative to HCT for nontraumatic complaints in the ED.

OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.

To optimize operations for a newly implemented abbreviated MR (AB-MR) breast cancer screening protocol, activity times were compared with the full-protocol examination.Activity times from 70 AB-MR and 736 full MR screening studies were analyzed. Total study time was measured from opening to closing examinations and expected scan time by summing the time of acquisition for each imaging series for either protocol. Actual scan time was obtained from DICOM headers. Total technologist activity time was obtained by subtracting expected scan time from total study time. Technologist activity time included both scan-related and non-scan-related activities.The actual scan time for the AB protocol was 17.5 ± 0.5 min, compared with 28.8 ± 0.3 min (mean ± SE) for the full protocol (difference, 11.3 min; P.0001). The total study time was 36.0 ± 3.2 min for AB-MRI and 49.7 ± 0.8 min for the full protocol (difference, 13.7 min; P.0001), implying that the AB-MR protocol had only a 38% greater patient flow rate than the full protocol.The realized gains in patient flow rate were lower than expected based on scan times (65%) because of increased technologist activity time for the AB-MR protocol.

Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. K -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of k -space data can result in blurring and aliasing artefacts for the reconstructed images. Recently, several studies have been proposed to use deep learning-based data-driven models for MRI reconstruction and have obtained promising results. However, the comparison of these methods remains limited because the models have not been trained on the same datasets and the validation strategies may be different. The purpose of this work is to conduct a comparative study to investigate the generative adversarial network (GAN)-based models for MRI reconstruction. We reimplemented and benchmarked four widely used GAN-based architectures including DAGAN, ReconGAN, RefineGAN and KIGAN. These four frameworks were trained and tested on brain, knee and liver MRI images using twofold, fourfold and sixfold accelerations, respectively, with a random undersampling mask. Both quantitative evaluations and qualitative visualization have shown that the RefineGAN method has achieved superior performance in reconstruction with better accuracy and perceptual quality compared to other GAN-based methods. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

Introduction Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. Methods Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). Results The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. Conclusion The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

MRI je jedno od glavnih postignuća moderne znanosti i tehnologije. Različite znanstvene discipline sudjelovale su u razvoju MR-a od kojeg se u daljnjoj budućnosti očekuje napredak koristeči matematička i informatička dostignuća. Od svog izuma prije tri desetljeća, MRI razvila se u način snimanja koji se rutinski koristi za dijagnostičko snimanje i postao je nezaobilazan alat za biomedicinska istraživanja in vivo. Napredak u MRI obećava proširenje korisnosti na mnoga ranije nezamisliva područja. U ovom radu analizirani su neki od brojnih publikacija autora koji su iznijeli tehnike brzog oslikavanja, spiralne i paralelne tehnike snimanja i njihovu kliničku primjenu. MRI is one of the major achievements of modern science and technology. Various scientific disciplines have participated in the development of MR, which is expected to progress in the future with the help of mathematical and IT achievements. Since its invention three decades ago, MRI has evolved into an imaging modality hat is routinely used for diagnostic imaging and has become in vivo biomedical research. Advances in MRI promise to extend utility to many previously unimaginable areas. This work analyzes some of the numerous publications of the authors who presented fast imaging techniques, spiral and parallel imaging techniques and their clinical application.

OBJECTIVE. The purpose of this article is to provide a practice-focused review of accelerating musculoskeletal MRI with the use of widely accessible techniques and to assess the effects of such acceleration on the value of musculoskeletal MRI. CONCLUSION. Echo-train compaction with fast radiofrequency pulses, high gradient performance modes, and high receiver bandwidth, as well as basic phase undersampling techniques, affords at least twofold acceleration of musculoskeletal MRI examinations while retaining image quality, comprehensiveness, and diagnostic performance. Optimized efficiency is a cornerstone for adding value to musculoskeletal MRI.

In this paper, a deep learning method for accelerating magnetic resonance imaging (MRI) is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding. In particular, we focus on the reconstruction of MR images related to patients affected by multiple sclerosis (MS) and we propose a new multimodal deep learning architecture that is able to exploit the joint information deriving from the combination of different types of MR images and to accelerate the MRI, while providing high quality of the reconstructed image. Experimental results show the performance improvement of the proposed method with respect to existing models in reconstructing images with an MRI acceleration of 4 times.

Multiple sclerosis is one of the most common chronic neurological diseases affecting the central nervous system. Lesions produced by the MS can be observed through two modalities of magnetic resonance (MR), known as T2W and FLAIR sequences, both providing useful information for formulating a diagnosis. However, long acquisition time makes the acquired MR image vulnerable to motion artifacts. This leads to the need of accelerating the execution of the MR analysis. In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe brain lesions. The proposed method exploits the multimodal approach of neural networks and it also focuses on the data acquisition and processing stages to reduce execution time of the MR analysis. Results prove the effectiveness of the proposed method in reconstructing subsampled MR images while saving execution time.

Magnetic Resonance Imaging (MRI) in a specific Region Of Interest (ROI) is valuable in detecting biomarkers for diagnosis, treatment, prognosis accurately. However, few existing methods study ROI in both data acquisition and image reconstruction when accelerating MRI by partial k-space measurements. Aiming at utilizing limited sampling resources efficiently on most relevant and desirable imaging contents in fast MRI, we propose a deep network framework called ROICSNet. With a learnable k-space sampler, an ad-hoc sampling pattern is adapted to a certain type of ROI organ. A cascaded Convolutional Neural Network (CNN) is used as the MR image reconstructor. By using a ROI prediction branch and a three-phase training strategy, the reconstructor is better guided onto the regions where radiologists hope to look closer. Experiments are performed on T1-modality abdominal MRI to demonstrate its state-of-the-art reconstruction accuracy compared with recent general and ROI-based fast MRI approaches. Our model achieves accurate imaging on fine details in ROI under a high accelerator factor and showed promise in real-world MRI application.

Image reconstruction has been a field of active research since the dawn of MRI, and the quest for faster patient exams has triggered substantial innovations during this time. This chapter provides a chronologically ordered overview of image reconstruction methods for accelerated MR imaging, from phase-constrained partial Fourier imaging over parallel imaging to compressed sensing and recent developments in machine learning. Didactic numerical experiments are presented with a data set that is representative for a clinical neuroimaging exam.

Purpose of review Multiparametric MRI (mpMRI), composed of T2WI, DWI, and DCE sequences, is effective in identifying prostate cancer (PCa), but length and cost preclude its application as a PCa screening tool. Here we review abbreviated MRI protocols that shorten or omit conventional mpMRI components to reduce scan time and expense without forgoing diagnostic accuracy. Recent findings The DCE sequence, which plays a limited diagnostic role in PI-RADS, is eliminated in variations of the biparametric MRI (bpMRI). T2WI, the lengthiest sequence, is truncated by only acquiring the axial plane or utilizing 3D acquisition with subsequent 2D reconstruction. DW-EPISMS further accelerates DWI acquisition. The fastest protocol described to date consists of just DW-EPISMS and axial-only 2D T2WI and runs less than 5 min. Abbreviated protocols can mitigate scan expense and increase scan access, allowing prostate MRI to become an efficient PCa screening tool.

Please see the Editorial Comment by Hansel J. Otero discussing this article. BACKGROUND. MRI use and the need for monitored anesthesia care (MAC) in children have increased. However, MAC is associa...

Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts. Here we propose recording continuous EEG with temporally sparse fast fMRI (fast fMRI-EEG) to minimize the EEG artifacts caused by MRI gradient coil switching without significantly compromising the field-of-view and spatiotemporal resolution of fMRI. Using simultaneous multi-slice inverse imaging to achieve whole-brain fMRI with isotropic 5-mm resolution in 0.1 ​s, and performing these acquisitions once every 2 ​s, we have 95% of the duty cycle available to record EEG with substantially less gradient artifact. We found that the standard deviation of EEG signals over the entire acquisition period in fast fMRI-EEG was reduced to 54% of that in conventional concurrent echo-planar imaging (EPI) and EEG recordings (EPI-EEG) across participants. When measuring 15-Hz steady-state visual evoked potentials (SSVEPs), the baseline-normalized oscillatory neural response in fast fMRI-EEG was 2.5-fold of that in EPI-EEG. The functional MRI responses associated with the SSVEP delineated by EPI and fast fMRI were similar in the spatial distribution, the elicited waveform, and detection power. Sparsely interleaved fast fMRI-EEG provides high-quality EEG without substantially compromising the quality of fMRI in evoked response measurements, and has the potential utility for applications where the onset of the target stimulus cannot be precisely determined, such as epilepsy.

PURPOSE As gradient performance increases, peripheral nerve stimulation (PNS) is becoming a significant constraint for fast MRI. Despite its impact, PNS is not directly included in the coil design process. Instead, the PNS characteristics of a gradient are assessed on healthy subjects after prototype construction. We attempt to develop a tool to inform coil design by predicting the PNS thresholds and activation locations in the human body using electromagnetic field simulations coupled to a neurodynamic model. We validate the approach by comparing simulated and experimentally determined thresholds for 3 gradient coils. METHODS We first compute the electric field induced by the switching fields within a detailed electromagnetic body model, which includes a detailed atlas of peripheral nerves. We then calculate potential changes along the nerves and evaluate their response using a neurodynamic model. Both a male and female body model are used to study 2 body gradients and 1 head gradient. RESULTS There was good agreement between the average simulated thresholds of the male and female models with the experimental average (normalized root-mean-square error

This article does not include an abstract. Please see the accompanying Point by Naveen Subhas.

To compare diagnostic performance of a 5-min knee MRI protocol to that of a standard knee MRI. One hundred 3 T (100 patients, mean 38.8 years) and 50 1.5 T (46 patients, mean 46.4 years) MRIs, consisting of 5 fast, 2D multi-planar fast-spin-echo (FSE) sequences and five standard multiplanar FSE sequences, from two academic centers (1/2015–1/2016), were retrospectively reviewed by four musculoskeletal radiologists. Agreement between fast and standard (interprotocol agreement) and between standard (intraprotocol agreement) readings for meniscal, ligamentous, chondral, and bone pathology was compared for interchangeability. Frequency of major findings, sensitivity, and specificity was also tested for each protocol. Interprotocol agreement using fast MRI was similar to intraprotocol agreement with standard MRI (83.0–99.5%), with no excess disagreement (≤ 1.2; 95% CI, −4.2 to 3.8%), across all structures. Frequency of major findings (1.1–22.4% across structures) on fast and standard MRI was not significantly different (p ≥ 0.215), except more ACL tears on fast MRI (p = 0.021) and more cartilage defects on standard MRI (p

Purpose To develop a fast 2D method for MRI near metal with reduced B0 in-plane and through-slice artifacts. Methods Multi-spectral imaging (MSI) approaches reduce artifacts in MR images near metal, but require 3D imaging of multiple excited volumes regardless of imaging geometry or artifact severity. The proposed 2D MSI method rapidly excites a limited slice and spectral region using gradient reversal between excitation and refocusing pulses, then uses standard 2D imaging, with the process repeating to cover multiple spectral offsets that are combined as in other MSI techniques. 2D MSI was implemented in a spin-echo-train sequence and validated in phantoms and in vivo by comparing it with standard spin-echo imaging and existing MSI techniques. Results 2D MSI images for each spatial-spectral region follow isocontours of the dipole-like B0 field variation, and thus frequency variation, near metal devices. Artifact correction in phantoms and human subjects with metal is comparable to 3D MSI methods, and superior to standard spin-echo techniques. Scan times are reduced compared with 3D MSI methods in cases where a limited number of slices are needed, though signal-to-noise ratio is also reduced as expected. Conclusion 2D MSI offers a fast and flexible alternative to 3D MSI for artifact reduction near metal. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Objectives: To assess whether NHS breast screening programme (NHSBSP) mammogram readers could effectively interpret first post-contrast acquisition subtracted (FAST) MRI, for intended use in screening for breast cancer. Methods: Eight NHSBSP mammogram readers from a single centre (four who also read breast MRI (Group 1) and four who do not (Group 2)) were given structured FAST MRI reader training (median 4 h: 32 min). They then prospectively interpreted 125 FAST MRIs (250 breasts: 194 normal and 56 cancer) comprising a consecutive series of screening MRIs enriched with additional cancer cases from 2015, providing 2000 interpretations. Readers were blinded to other readers’ opinions and to clinical information. Categorisation followed the NHSBSP MRI reporting categorisation, with categories 4 and 5 considered indicative of cancer. Diagnostic accuracy (reference standard: histology or 2 years’ follow-up) and agreement between readers were determined. Results: The accuracy achieved by Group 2 (847/1000 (85%; 95% confidence interval (CI) 82–87%)) was 5% less than that of Group 1 (898/1000 (90%; 95% CI 88–92)). Good inter-reader agreement was seen between both Group 1 readers (κ = 0.66; 95% CI 0.61–0.71) and Group 2 readers (κ = 0.63; 95% CI 0.58–0.68). The median time taken to interpret each FAST MRI was Group 1: 34 s (range 3–351) and Group 2: 77 s (range 11–321). Conclusion: Brief structured training enabled multiprofessional mammogram readers to achieve similar accuracy at FAST MRI interpretation to consultant radiologists experienced at breast MRI interpretation. Advances in knowledge: FAST MRI could be feasible from a training-the-workforce perspective for screening within NHSBSP.

Development on new fast MRI scanners resulted in rising number of prostate examinations. High-spatial resolution of MRI examinations performed on 3T scanners allows recognition of very fine anatomical structures previously not demarcated on performed scans. We present current status of MR imaging in the context of recognition of most important anatomical structures.

The objective of our study was to prospectively investigate the diagnostic accuracy and added value of fast MRI for evaluating pulmonary hydatid disease in children by comparing fast MRI findings with MDCT findings.Twenty-eight consecutive children (24 boys and four girls; mean age ± SD, 8.93 ± 3.1 years; range, 5-17 years) with clinically suspected pulmonary hydatid disease were enrolled in this prospective research study for the period from October 2012 to July 2018. Fast MRI without contrast material and contrast-enhanced MDCT of the chest were performed within 48 hours of each other in all children. Two pediatric radiologists independently evaluated the lungs for the presence of consolidation, nodule, and mass (solid vs cyst). Cysts were further evaluated for the presence of fluid, air, and an internal membrane. The accuracies of fast MRI and MDCT for detecting pulmonary hydatid disease were obtained and compared. Interobserver agreement was measured with the kappa coefficient.The accuracy of fast MRI and MDCT for detecting pulmonary hydatid cyst was 92.86%. There was no difference between fast MRI and MDCT for accurately detecting pulmonary hydatid cyst (p0.001). Internal membranes were detected in 11 of 28 patients (39.28%) with fast MRI and three of 28 patients (10.71%) with MDCT. Almost perfect interobserver agreement was present between the two independent reviewers (κ = 1).Fast MRI without contrast material is comparable to contrast-enhanced MDCT for accurately detecting lung cysts in pediatric patients with pulmonary hydatid disease. However, fast MRI provides added diagnostic value by showing internal membranes of cysts, which is specific to pulmonary hydatid disease.

BACKGROUND AND PURPOSE: Children with shunted hydrocephalus have been undergoing surveillance neuroimaging, generally in the form of head CT, for evaluation of ventricular size. As the life expectancy of these children has improved due to better shunt technology and medical care, risks related to the ionizing radiation incurred during multiple head CT examinations that they are expected to undergo throughout their lifetime have become a concern. The purpose of this study is to estimate the LAR of developing fatal cancer due to head CT for ventricular size assessment in children with shunted hydrocephalus and to assess the impact of instituting a rapid brain MR imaging protocol in reducing radiation exposure. MATERIALS AND METHODS: Retrospective review of medical records yielded 182 patients who underwent neuroimaging for assessment of ventricular size. Available neuroimaging studies (head CT and rapid brain MR) were counted and annual neuroimaging frequency was calculated. It was assumed that these patients undergo a similar number of neuroimaging studies annually through 20 years of age. A risk estimate was calculated based on the BEIR VII report and effective doses obtained using the International Commission on Radiologic Protection Report 103 organ weighting factors. RESULTS: The mean annual neuroimaging study frequency was 2.1. Based on the average age of 1.89 years, it was assumed neuroimaging surveillance commences in the second year of life. LAR was calculated assuming that a patient undergoes neuroimaging in the form of head CT at this frequency (2/year) through 20 years of age. Assuming 2 scans are performed per year and the low-dose head CT protocol is used, approximately 1 excess lifetime fatal cancer would be generated per 230 patients; with standard head CT, there would be 1 excess lifetime fatal cancer per 97 patients. CONCLUSIONS: Children with shunted hydrocephalus are at increased risk of developing fatal cancer if they are to undergo surveillance using head CT. Implementation of a rapid brain MR imaging protocol with no radiation detriment will reduce this risk.

5513 Background: The prostate-specific antigen (PSA) test can lead to under- and over-diagnosis of prostate cancer and has not been recommended for population screening. A fast MRI scan might overcome the limitations of PSA. IP1-PROSTAGRAM is the first study to evaluate the performance of a 15-minute non-contrast MRI for prostate cancer screening in comparison to PSA. Methods: IP1-PROSTAGRAM was a prospective, population-based, screen-positive paired-cohort study. Men aged 50-69 years in the UK were invited for prostate cancer screening through seven primary care practices or community-based recruitment. Participants underwent a PSA and MRI scan (T2-weighted and diffusion). MRI was scored using PIRADS version 2.0 without knowledge of PSA; screen-positive MRI (defined as either PIRADS score 3-5 or 4-5) were compared against a screen-positive PSA defined as ≥3ng/ml. If any test was screen-positive, a systematic 12-core biopsy was performed with MRI-ultrasound image-fusion targeted biopsy to MRI suspicious areas, as appropriate. Clinically-significant cancer was defined as any Gleason score ≥3+4. The primary outcome was the proportion of screen-positive MRI at different scores; important secondary outcomes were the number of clinically-significant and insignificant cancers detected. Results: 2034 men were invited to participate of whom 408 consented and 406 were screened by both PSA and MRI (10/Oct/2018-15/May/2019). The proportion with a screen-positive MRI (score 3-5) was higher than the proportion with a screen-positive PSA (17.7% [95%CI 14.3-21.8] vs. 9.9% [95%CI 7.3-13.2]; p < 0.001). A screen-positive MRI (score 4-5) was similar to a screen-positive PSA (10.6% [95%CI 7.9-14.0] vs. 9.9% [95%CI 7.3-13.2], p = 0.71). An MRI score 3-5 or 4-5 used to denote a screen-positive MRI, compared to PSA alone, detected 14, 11 and 7 clinically-significant cancers, respectively. There were 7, 5 and 6 clinically-insignificant cancers detected, respectively. No serious adverse events occurred. Conclusions: When screening the general population for prostate cancer, MRI using a score of 4-5 to define a screen-positive test, compared to PSA alone at ≥3ng/ml, could lead to more men diagnosed with clinically-significant cancer without increasing the number of men biopsied or diagnosed with clinically-insignificant cancer. Clinical trial information: NCT03702439 .

The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the k-space (Fourier domain). Deep learning techniques have been recently received considerable interest for accelerating MR imaging (MRI). In this paper, a deep learning method for accelerating MRI is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding. In particular, we focus on the reconstruction of MR images related to patients affected by multiple sclerosis (MS) and we propose a new multimodal deep learning architecture that is able to accelerate the MRI up to 8 times, while providing a high quality of the reconstructed image, especially in the area of the brain lesions. Experiments have been performed on T2W and FLAIR images, both providing useful information for MS MRI, and have shown that the proposed multimodal network is able to achieve a higher reconstruction accuracy with respect to existing methods, while effectively reducing the execution time of the clinical analysis.