Mesoscopic Quantification of Cortical Architecture in the Living Human Brain

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for a comprehensive understanding of brain structure and function. However, in vivo techniques for mesoscopic imaging have been hampered by the sensitivity challenges of acquiring data at very high resolutions and the lack of analysis tools that can retain fine-scale detail while also accurately positioning measurements relative to the complex folded structure of the cerebral cortex. Here, we present an experimental dataset in which we image the anatomical structure of the visual and auditory cortices of five participants at 0.35 × 0.35 × 0.35 mm3 resolution. To analyze this challenging dataset, we design and implement two sets of novel methodology: a method for mitigating imaging artifacts related to blood motion and a suite of software tools for accurate quantification and visualization of the mesoscopic structure of the cortical surface. Applying these methods, we demonstrate the ability to clearly identify structures that are visible only at the mesoscopic scale, including cortical layers and intracortical blood vessels. We freely share our dataset and tools with the research community, thereby enabling investigations of fine-scale neurobiological structures in both the current and future datasets. Overall, our results demonstrate the viability of mesoscopic imaging as a quantitative tool for studying the living human brain.

Imaging Based Spleen Volume Assessments in Myelofibrosis Clinical Trials - Do We Need a Double Read Model?

Myelofibrosis (MF) is a type of chronic blood cancer characterized by bone marrow fibrosis, extramedullary hematopoiesis and splenomegaly. Approximately 89% of patients present palpable splenomegaly with a compromised quality of life and reduced survival. The International Working Group Myeloproliferative Neoplasm Research and Treatment (IWG-MRT) criteria utilize spleen volume (SV) as part of clinical improvement (CI) response in MF trials. These include evaluation of spleen response as a primary/ secondary endpoint - defined as ≥35% SV reduction from baseline. Progressive disease is defined as ≥25% increase in SV from baseline level. Magnetic resonance imaging (MRI) and Computed tomography (CT) provide a non-invasive way to assess change in spleen size both spatially and temporally in a clinical study. While image acquisition with optimized and harmonized protocols is key, a central independent review of images also plays a critical role in correct patient outcome determination. The aim of this study is to determine if a double read model for central independent review is necessary to maintain a high accuracy of SV estimation. To this effect, alignment among independent readers over review criteria was assessed: inter-reader variability (IRV), in addition to assessment of consistency in review approach: intra-reader variability (ARV). Retrospective analysis was implemented on imaging data across 12 multi-center MF trials-MRI/CT images of two time-points (baseline and 1 follow-up) from 142 trial participants for ARV and 85 trial participants for IRV analysis. All images passed image quality checks and were processed for manual segmentation of the spleen by image analysts, followed by an over-read by trained radiologists. The spleen volume was calculated as the sum of spleen cross-sectional area across all slices multiplied by slice interval. For ARV analysis, the images were presented to the same readers in a blinded fashion at least three weeks after the initial review. For IRV analysis, images read by a primary reader were then presented to the secondary reader. The percent discrepancy for ARV and IRV were calculated as the ratio of difference between primary and secondary spleen volumes, divided by the average of the two. The average ARV discrepancy was 0.37±0.55 % (mean±standard deviation) as shown in Fig 1a. Zero subjects had an ARV discrepancy of more than 5%. As shown in Fig 2a, majority of the cases were under an ARV discrepancy of 1%. These results show excellent consistency in approach of readers over time in comparison to 2.8±3.5% reportedby Harris et al (European Journal of Radiology, 2010). For IRV, the average discrepancy was 0.62±0.85 % as shown in Fig 1b. 1.1% of cases had an IRV discrepancy of more than 5%. As shown in Fig 2b, most of the cases were within an ARV discrepancy of 1%. These results show a high level of alignment between readers in their imaging review approach in comparison to 6.4±9.8% reportedby Harris et al (European Journal of Radiology, 2010). The high level of reliability and repeatability seen across radiological reads suggests that a single read model is sufficient to assess imaging volumetrics-based endpoints. It is important to note that a multi-step approach was used to thoroughly train, test and monitor independent readers throughout the study duration. Readers were chosen based on high level of experience with the indication and analysis application. Reader onboarding involved an accurate overview and clear instruction on the review assessments. Multiple MRI/ CT imaging cases were utilized for reader testing and training. Since image quality can be a significant factor influencing the confidence level of a reader, these cases reflected examples of imaging artifacts expected on such trials, such as motion artifacts, low image resolution, ghosting and low contrast to noise ratio. Routine quality checks and variability assessments were done throughout the trial duration, with prompt corrective action taken to prevent inaccuracy of study results. These actions included issuing training points or re-read of cases that contained established error. Further work is necessary on assessing how variables such as spleen size, imaging artifacts and change in imaging modality affect reader variability. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

PyDesigner: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Tensor and Diffusional Kurtosis Imaging

PyDesigner is an open-source and containerized Python software package, adapted from the DESIGNER pipeline, for diffusion weighted magnetic resonance imaging preprocessing and tensor estimation. PyDesigner combines tools from FSL and MRtrix3 to reduce the effects of signal noise and imaging artifacts on multiple diffusion measures that can be derived from the diffusion and kurtosis tensors. This publication describes the main features of PyDesigner and highlights its ease of use across platforms, while examining its accuracy and robustness in deriving commonly used diffusion and kurtosis metrics.

Magnetic Resonance Imaging Feature Analysis and Evaluation of Tubal Patency under Convolutional Neural Network in the Diagnosis of Infertility

To explore the diagnostic value of MRI image features based on convolutional neural network for tubal unobstructed infertility, 30 infertile female patients were first selected as the research objects, who admitted to the hospital from May 2018 to January 2020. They all underwent routine MRI examinations and CNN-based MR-hysteron-salpingography (HSG) examinations, in order to discuss the diagnostic accuracy of the two examinations. In the research, it was necessary to observe the patients’ imaging results, calculate the diagnosis rate of the two examination results, and analyze the application effect of the CNN algorithm, thereby selecting the best reconstruction method. In this study, the analysis was conducted on the basis of no statistical difference in the baseline data of the included patients. The results of undersampling reconstruction at 2-fold, 4-fold, and 6-fold showed that CNN for data consistency layer (CNN_DC) had a better effect, and its peak signal-to-noise ratio (PSNR) was lower sharply than that of the other two reconstruction methods, while the normalized mean square error (NMSE) and structural similarity index measure (SSIM) were higher markedly than the values of the other two reconstruction methods. The diagnostic rate of routine MRI examination of the fallopian tube and other parts of the uterus was lower than or equal to that of MR-HSG examination by CNN. Routine MRI examinations of fallopian tube imaging artifacts were large, and the definition was reduced, which increased the difficulty of identification. However, MR-HSG examination by CNN indicated that the imaging artifacts were low, the clarity was high, and the influence of noise was small, which was conducive to clinical diagnosis and identification. For endometriosis, the accuracy of MR-HSG was 33.33% and the accuracy of MRI was 46.67%. CNN MR-HSG inspection method was significantly better than the conventional MRI inspection method P < 0.05 . Therefore, the results of this study revealed that MR-HSG examination by CNN had a clear imaging effect and obvious inhibition effect on background signals and rapid image generation without the need for reconstruction with the same spatial resolution, which improved the imaging quality and could provide a reference value for clinical diagnosis and subsequent related studies.

Recent Advances and Challenges in the Development of Radiofrequency HTS Coil for MRI

Radiofrequency (RF) coils fashioned from high-temperature superconductor (HTS) have the potential to increase the sensitivity of the magnetic resonance imaging (MRI) experiment by more than a dozen times compared to conventional copper coils. Progress, however, has been slow due to a series of technological hurdles. In this article, we present the developments that recently led to new perspectives for HTS coil in MRI, and challenges that still need to be solved. First, we recall the motivations for the implementations of HTS coils in MRI by presenting the limits of cooled copper coil technology, such as the anomalous skin effect limiting the decrease of the electric resistance of normal conductors at low temperature. Then, we address the progress made in the development of MRI compatible cryostats. New commercially available low-noise pulsed-tube cryocoolers and new materials removed the need for liquid nitrogen-based systems, allowing the design of cryogen-free and more user-friendly cryostats. Another recent advance was the understanding of how to mitigate the imaging artifacts induced by HTS diamagnetism through field cooling or temperature control of the HTS coil. Furthermore, artifacts can also originate from the RF field coupling between the transmission coil and the HTS reception coil. Here, we present the results of an experiment implementing a decoupling strategy exploiting nonlinearities in the electric response of HTS materials. Finally, we discuss the potential applications of HTS coils in bio-imaging and its prospects for further improvements. These include making the technology more user-friendly, implementing the HTS coils as coil arrays, and proposing solutions for the ongoing issue of decoupling. HTS coil still faces several challenges ahead, but the significant increase in sensitivity it offers lends it the prospect of being ultimately disruptive.

Research on optimization sparse method for capacitive micromachined ultrasonic transducer array: heuristic algorithm

Purpose Because of the small size and high integration of capacitive micromachined ultrasonic transducer (CMUT) component, it can be made into large-scale array, but this lead to high hardware complexity, so the purpose of this paper is to use less elements to achieve better imaging results. In this research, an optimized sparse array is studied, which can suppress the side lobe and reduce the imaging artifacts compared with the equispaced sparse array with the same number of elements. Design/methodology/approach Genetic algorithm is used to sparse the CMUT linear array, and Kaiser window apodization is added to reduce imaging artifacts, the beam pattern and peak-to-side lobe ratio are calculated, point targets imaging comparisons are performed. Furthermore, a 256-elements CMUT linear array is used to carry out the imaging experiment of embedded mass and forearm blood vessel, and the imaging results are compared quantitatively. Findings Through the imaging comparison of embedded mass and forearm blood vessel, the feasibility of optimized sparse array of CMUT is verified, and the purpose of reducing the hardware complexity is achieved. Originality/value This research provides a basis for the large-scale CMUT array to reduce the hardware complexity and the amount of calculation. At present, the CMUT array has been used in medical ultrasound imaging and has huge market potential.

Metallic Implants in MRI – Hazards and Imaging Artifacts

Background Magnetic resonance imaging (MRI) is an examination method for noninvasive soft tissue imaging without the use of ionizing radiation. Metallic implants, however, may pose a risk for the patient and often result in imaging artifacts. Due to the increasing number of implants, reducing these artifacts has become an important goal. In this review, we describe the risks associated with implants and provide the background on how metal-induced artifacts are formed. We review the literature on methods on how to reduce artifacts and summarize our findings. Method The literature was searched using PubMed and the keywords “MRI metal artifact reduction”, “metallic implants” and “MRI artefacts/artifacts”. Results and Conclusion The MRI compatibility of implants has to be evaluated individually. To reduce artifacts, two general approaches were found: a) parameter optimization in standard sequences (echo time, slice thickness, bandwidth) and b) specialized sequences, such as VAT, OMAR, WARP, SEMAC and MAVRIC. These methods reduced artifacts and improved image quality, albeit at the cost of a (sometimes significantly) prolonged scan time. New developments in accelerated imaging will likely shorten the scan time of these methods significantly, such that routine use may become feasible. Key Points: Citation Format

MAUI (MBI Analysis User Interface)—An image processing pipeline for Multiplexed Mass Based Imaging

Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.