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Easy Diagnosis of Aortic Invasion in Patients with Lung Cancer Using Cine Magnetic Resonance Imaging.

ABSTRACT: Selecting the proper treatment strategy for locally advanced lung cancer, such as T4 tumors, is difficult. Therefore, obtaining an accurate diagnosis of T4 tumors is required. It can be difficult to determine whether the tumor invades adjacent structures. We describe the case of a patient easily diagnosed to be without aortic invasion using cine magnetic resonance imaging (MRI). We herein report the case of an 80-year-old male who presented a lung tumor. The transbronchial lung washing cytology findings were consistent with those of adenocarcinoma. In addition, the computed tomography findings indicated suspected aortic invasion of the lung tumor, as the mass girdled the descending aorta beyond 120° adjoining at a length of 10 cm. However, cine MRI display clearly demonstrated a clear area of isolation between the aorta and lung tissue based on differences in the heart rhythm from the patient's respiratory movements. Therefore, the lesion was clinically diagnosed as a stage IIB (T3N0M0) tumor. Radiation was administered due to the patient's advanced age and comorbidities such as chronic obstructive pulmonary disease. He remains alive without disease progression 6 months after the therapy. Our findings, therefore, indicate the usefulness of easily diagnosing the absence of aortic invasion in patients with lung cancer using cine MRI without the need for a special software program.

SUBMITTER: Uramoto H

PROVIDER: S-EPMC4560307 | biostudies-literature | 2015 May-Aug

REPOSITORIES: biostudies-literature

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Easy Diagnosis of Aortic Invasion in Patients with Lung Cancer Using Cine Magnetic Resonance Imaging.

Uramoto Hidetaka H Kinoshita Hiroyasu H Nakajima Yuki Y Akiyama Hirohiko H

Case reports in oncology 20150501 2

Selecting the proper treatment strategy for locally advanced lung cancer, such as T4 tumors, is difficult. Therefore, obtaining an accurate diagnosis of T4 tumors is required. It can be difficult to determine whether the tumor invades adjacent structures. We describe the case of a patient easily diagnosed to be without aortic invasion using cine magnetic resonance imaging (MRI). We herein report the case of an 80-year-old male who presented a lung tumor. The transbronchial lung washing cytology ...[more]

PMID: 26351439

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Project description:IntroductionAs the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish.MethodsNinety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements.ResultsThe semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml).DiscussionThe proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

Project description:BACKGROUND:Existing cine imaging techniques rely on balanced steady-state free precession (bSSFP) or spoiled gradient-echo readouts, each of which has limitations. For instance, with bSSFP, artifacts occur from rapid through-plane flow and off-resonance effects. We hypothesized that a prototype cine technique, radial fast interrupted steady-state (FISS), could overcome these limitations. The technique was compared with standard cine bSSFP for cardiac function, coronary artery conspicuity, and aortic valve morphology. Given its advantageous properties, we further hypothesized that the cine FISS technique, in combination with arterial spin labeling (ASL), could provide an alternative to phase contrast for visualizing in-plane flow patterns within the aorta and branch vessels. MAIN BODY:The study was IRB-approved and subjects provided consent. Breath-hold cine FISS and bSSFP were acquired using similar imaging parameters. There was no significant difference in biplane left ventricular ejection fraction or cardiac image quality between the two techniques. Compared with cine bSSFP, cine FISS demonstrated a marked decrease in fat signal which improved conspicuity of the coronary arteries, while suppression of through-plane flow artifact on thin-slice cine FISS images improved visualization of the aortic valve. Banding artifacts in the subcutaneous tissues were reduced. In healthy subjects, dynamic flow patterns were well visualized in the aorta, coronary and renal arteries using cine FISS ASL, even when the slice was substantially thicker than the vessel diameter. CONCLUSION:Cine FISS demonstrates several benefits for cardiovascular imaging compared with cine bSSFP, including better suppression of fat signal and reduced artifacts from through-plane flow and off-resonance effects. The main drawback is a slight (~?20%) decrease in temporal resolution. In addition, preliminary results suggest that cine FISS ASL provides a potential alternative to phase contrast techniques for in-plane flow quantification, while enabling an efficient, visually-appealing, semi-projective display of blood flow patterns throughout the course of an artery and its branches.

Project description:Objective3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study the anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly undersampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly undersampled data.ApproachSTINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model which deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis (PCA). The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as MR data acquired clinically from a healthy human subject. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS).Main resultsSTINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean±S.D. center-of-mass error of 1.0±0.4 mm, compared to 3.4±1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.SignificanceSTINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Project description:To analyze and optimize the signal-to-noise ratio (SNR) for the "Noquist" method for acceleration of cine magnetic resonance imaging in the presence of partially static field of view, designing practical methods for selection of optimal or near-optimal sample sets to allow reliable application of the method for variable image dimensions.To investigate the impact of the Noquist method and its experimental parameters on the SNR in the image reconstructed from reduced data, and to explore optimization of methods for highest SNR stability, three different optimization parameters have been selected: the condition of the forward matrix (R(cond)) as it defines the propagation of noise into the reconstructed image, and the maximum (Φ(maxD)) and the mean (Φ(meanD)) linear noise amplification factor of the dynamic field-of-view (FOV) region. As SNR in a Noquist reconstruction is often not uniform across the FOV and since dynamic regions may contain the part of the image more clinically relevant, primarily these noise levels are targeted for optimization. Using these three optimization parameters, three experiments were conducted: characterization of Noquist SNR properties as a function of important image size parameters; for sufficiently small image dimensions, employment of exhaustive search using lexicographical algorithms to visit all possibilities under the cine imaging constraint that equal numbers of views are acquired at each time point of the sequence; and, departing from an hypothetically optimal pattern, generation and evaluation of SNR characteristics of a series of random variations to that optimal pattern.The impact of favorable sparse data selection is illustrated, and SNR properties are characterized as a function of relevant acquisition parameters. Optimal data selection is investigated by exhaustive methods for small image sizes, and compared with algorithmic selection patterns. Observations from these experiments are confirmed by further studies on data selection for realistic image dimensions and an optimal selection algorithm is proposed. Sixty-four cases of small image sizes were analyzed through exhaustive search with a total of 527 984 141 matrix inversions called in the process, evaluating several SNR parameters for each case. An algorithm, named "Stairwell," that permits to design image dimensions with optimal SNR characteristics is presented, evaluated and compared with cases analyzed through exhaustive search. In 71.9% of the cases exhaustively studied, the Stairwell algorithm yielded optimal solutions. For no case did the deviation from optimum exceed 3.2% (R(cond)), 1.0% (Φ(meanD)), and 4.9% (Φ(maxD)).We have demonstrated SNR-optimality of the "Stairwell" selection algorithm for small image dimensions, and performed additional experiments which all support hypothesized optimality of the algorithm for any image dimensions that satisfy certain symmetry constraints for Noquist reduced-data cine MR imaging. Furthermore, we have presented overall SNR characteristics associated with use of the Noquist method by this algorithm for practical clinical image dimensions. Additionally, observations from our optimization experiments allow us to formulate recommendations for dimensioning Noquist image acquisition parameters which guarantee stable inversion. Moreover, these results allow prediction of the anticipated SNR properties of the reconstruction for given image dimensions (S,D,T), relative to SNR in a conventional full-grid acquisition.

Dataset Information

Easy Diagnosis of Aortic Invasion in Patients with Lung Cancer Using Cine Magnetic Resonance Imaging.

Publications

Easy Diagnosis of Aortic Invasion in Patients with Lung Cancer Using Cine Magnetic Resonance Imaging.

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