Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches. RSNA, 2017 Online supplemental material is available for this article.

Recent studies suggest that Attention Deficit Hyperactivity Disorder (ADHD) is a common comorbid condition in childhood epilepsy, but little is known regarding the nature, frequency and timing of associated neurobehavioural/cognitive complications or the underlying aetiology of ADHD in epilepsy. This investigation examined: (i) the prevalence of ADHD and its subtypes; (ii) the association of ADHD with abnormalities in academic, neuropsychological, behavioural and psychiatric status and (iii) the aetiology of ADHD in paediatric epilepsy. Seventy-five children (age 8-18) with new/recent onset idiopathic epilepsy and 62 healthy controls underwent structured interview (K-SADS) to identify the presence and type of DSM-IV defined ADHD, neuropsychological assessment, quantitative MR volumetrics, characterization of parent observed executive function, review of academic/educational progress and assessment of risk factors during gestation and delivery. The results indicate that ADHD is significantly more prevalent in new onset epilepsy than healthy controls (31% versus 6%), characterized predominantly by the inattentive variant, with onset antedating the diagnosis of epilepsy in the majority of children. ADHD in childhood epilepsy is associated with significantly increased rates of school based remedial services for academic underachievement, neuropsychological consequences with prominent differences in executive function, and parent-reported dysexecutive behaviours. ADHD in paediatric epilepsy is neither associated with demographic or clinical epilepsy characteristics nor potential risk factors during gestation and birth. Quantitative MRI demonstrates that ADHD in epilepsy is associated with significantly increased gray matter in distributed regions of the frontal lobe and significantly smaller brainstem volume. Overall, ADHD is a prevalent comorbidity of new onset idiopathic epilepsy associated with a diversity of salient educational, cognitive, behavioural and social complications that antedate epilepsy onset in a significant proportion of cases, and appear related to neurodevelopmental abnormalities in brain structure.

Purpose In this article, we discuss dynamic whole-body (DWB) positron emission tomography (PET) as an imaging tool with significant clinical potential, in relation to conventional standard uptake value (SUV) imaging. Background DWB PET involves dynamic data acquisition over an extended axial range, capturing tracer kinetic information that is not available with conventional static acquisition protocols. The method can be performed within reasonable clinical imaging times, and enables generation of multiple types of PET images with complementary information in a single imaging session. Importantly, DWB PET can be used to produce multi-parametric images of (i) Patlak slope (influx rate) and (ii) intercept (referred to sometimes as Bdistribution volume^), while also providing (iii) a conventional 'SUV-equivalent' image for certain protocols. Results We provide an overview of ongoing efforts (primarily focused on FDG PET) and discuss potential clinically relevant applications. Conclusion Overall, the framework of DWB imaging [applicable to both PET/CT(computed tomography) and PET/MRI (magnetic resonance imaging)] generates quantitative measures that may add significant value to conventional SUV imagederived measures, with limited pitfalls as we also discuss in this work.

This paper presents the design and control of an MRI-compatible 1-DOF needle driver robot and its precise position control using pneumatic actuation with long transmission lines. MRI provides superior image quality compared to other imaging modalities such as CT or ultrasound, but imposes severe limitations on the material and actuator choice (to prevent image distortion) due to its strong magnetic field. We are primarily interested in developing a pneumatically actuated breast biopsy robot with a large force bandwidth and precise targeting capability during radio-frequency ablation (RFA) of breast tumor, and exploring the possibility of using long pneumatic transmission lines from outside the MRI room to the device in the magnet to prevent any image distortion whatsoever. This paper presents a model of the entire pneumatic system. The pneumatic lines are approximated by a first order system with time delay, because its dynamics are governed by the telegraph equation with varying coefficients and boundary conditions, which cannot be solved precisely. The slow response of long pneumatic lines and valve subsystems make position control challenging. This is further compounded by the presence of non-uniform friction in the device. Sliding mode control (SMC) was adopted, where friction was treated as an uncertainty term to drive the system onto the sliding surface. Three different controllers were designed, developed, and evaluated to achieve precise position control of the RFA probe. Experimental results revealed that all SMCs gave satisfactory performance with long transmission lines. We also performed several experiments with a 3-DOF fiber-optic force sensor attached to the needle driver to evaluate the performance of the device in the MRI under continuous imaging.

BackgroundTo develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously valued pseudo-computed tomography (CT) images from uncorrected 18F-fluorodeoxyglucose (18F-FDG) PET images. A deep convolutional encoder-decoder network was trained to identify tissue contrast in volumetric uncorrected PET images co-registered to CT data. A set of 100 retrospective 3D FDG PET head images was used to train the model. The model was evaluated in another 28 patients by comparing the generated pseudo-CT to the acquired CT using Dice coefficient and mean absolute error (MAE) and finally by comparing reconstructed PET images using the pseudo-CT and acquired CT for attenuation correction. Paired-sample t tests were used for statistical analysis to compare PET reconstruction error using deepAC with CT-based attenuation correction.ResultsdeepAC produced pseudo-CTs with Dice coefficients of 0.80 ± 0.02 for air, 0.94 ± 0.01 for soft tissue, and 0.75 ± 0.03 for bone and MAE of 111 ± 16 HU relative to the PET/CT dataset. deepAC provides quantitatively accurate 18F-FDG PET results with average errors of less than 1% in most brain regions.ConclusionsWe have developed an automated approach (deepAC) that allows generation of a continuously valued pseudo-CT from a single 18F-FDG non-attenuation-corrected (NAC) PET image and evaluated it in PET/CT brain imaging.