Semiautomated Multimodal Breast Image Registration

Consideration of information from multiple modalities has been shown to have increased diagnostic power in breast imaging. As a result, new techniques such as microwave imaging continue to be developed. Interpreting these novel image modalities is a challenge, requiring comparison to established techniques such as the gold standard X-ray mammography. However, due to the highly deformable nature of breast tissues, comparison of 3D and 2D modalities is a challenge. To enable this comparison, a registration technique was developed to map features from 2D mammograms to locations in the 3D image space. This technique was developed and tested using magnetic resonance (MR) images as a reference 3D modality, as MR breast imaging is an established technique in clinical practice. The algorithm was validated using a numerical phantom then successfully tested on twenty-four image pairs. Dice's coefficient was used to measure the external goodness of fit, resulting in an excellent overall average of 0.94. Internal agreement was evaluated by examining internal features in consultation with a radiologist, and subjective assessment concludes that reasonable alignment was achieved

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Fast Segmentation and High-Quality Three-Dimensional Volume Mesh Creation from Medical Images for Diffuse Optical Tomography

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform

Standardized Platform for Coregistration of Noncurrent Diffuse Optical and Magnetic Resonance Breast Images Obtained in Different Geometries

We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients

Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time

Combination of Single-Photon Emission Computed Tomography and Magnetic Resonance Imaging to Track ¹¹¹In-Oxine-Labeled Human Mesenchymal Stem Cells in Neuroblastoma-Bearing Mice

Homing is an inherent, complex, multistep process performed by cells such as human bone marrow mesenchymal stem cells (hMSCs) to travel from a distant location to inflamed or damaged tissue and tumors. This ability of hMSCs has been exploited as a tumor-targeting strategy in cell-based cancer therapy. The purpose of this study was to investigate the applicability of ¹¹¹In-oxine for tracking hMSCs in vivo by combining single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI). ¹¹¹In-labeled hMSCs (10⁶ cells) were infused intraperitoneally in neuroblastoma-bearing mice, whereas a control group received a dose of free ¹¹¹In-oxine. SPECT and MRI studies were performed 24 and 48 hours afterwards. Initially, the images showed similar activity in the abdomen in both controls and hMSC-injected animals. In general, abdominal activity decreases at 48 hours. hMSC-injected animals showed increased uptake in the tumor area at 48 hours, whereas the control group showed a low level of activity at 24 hours, which decreased at 48 hours. In conclusion, tracking ¹¹¹In-labeled hMSCs combining SPECT and MRI is feasible and may be transferable to clinical research. The multimodal combination is essential to ensure appropriate interpretation of the images.This work was funded in part by grants from Ministerio de Economía y Competitividad (PLE2009-0115), Red Tematica de Investigación Cooperativa en Cancer (RTICC/ISCIII; RD12/0036/0027), the Madrid Regional Government (S-BIO-0204-2006–MesenCAM and P2010/BMD-2420-CellCAM), and the Ministerio de Ciencia e Innovación (CEN-20101014 and TEC-2010-21619-C04-01).Publicad

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

Copyright © 2020 The Korean Society of Radiology.Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer11Nsciescopuskc