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Diffusion Weighted Magnetic Resonance Imaging for the Characterization of Solitary Pulmonary Lesions.

ABSTRACT: We evaluated the differential diagnosis of solitary pulmonary lesions on magnetic resonance imaging.To investigate the value of diffusion weighted imaging on the differential diagnosis of solitary pulmonary lesions.Randomized prospective study.This prospective study included 48 solitary pulmonary nodules and masses (18 benign, 30 malignant). Single shot echo planar spin echo diffusion weighted imaging (DWI) was performed with two b factors (0 and 1000 s/mm(2)). Apparent diffusion coefficients (ADCs) were calculated. On diffusion weighted (DW) trace images, the signal intensities (SI) of the lesions were visually compared to the SI of the thoracic spinal cord using a 5-point scale: 1: hypointense, 2: moderately hypointense, 3: isointense, 4: moderately hyperintense, 5: significantly hyperintense. For the quantitative evaluation, the lesion to thoracic spinal signal intensity ratios and the ADCs of the lesions were compared between groups.On visual evaluation, taking the density of the spinal cord as a reference, most benign lesions were found to be hypointense, while most of the malignant lesions were evaluated as hyperintense on DWI with a b factor of 1000 s/mm(2). In contrast, on T2 weighted images, it was seen that the distinction of malignant lesions from benign lesions was not statistically significant. The ADCs of the malignant lesions were significantly lower than those of benign lesions (mean ADC was 2.02×10(-3) mm(2)/s for malignant lesions, and 1.195×10(-3)±0.3 mm(2)/s for benign lesions). Setting the cut-off value at 1.5×10(-3), ADC had a sensitivity of 86.7% and a specificity of 88.9% for the differentiation of benign lesions from malignant lesions.DWI may aid in the differential diagnosis of solitary pulmonary lesions. (ClinicalTrials.gov Identifier: NCT02482181).

SUBMITTER: Cak?r C

PROVIDER: S-EPMC4692341 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Diffusion Weighted Magnetic Resonance Imaging for the Characterization of Solitary Pulmonary Lesions.

Çakır Çağlayan Ç Gençhellaç Hakan H Temizöz Osman O Polat Ahmet A Şengül Ersin E Duygulu Gökhan G

Balkan medical journal 20151001 4

<h4>Background</h4>We evaluated the differential diagnosis of solitary pulmonary lesions on magnetic resonance imaging.<h4>Aims</h4>To investigate the value of diffusion weighted imaging on the differential diagnosis of solitary pulmonary lesions.<h4>Study design</h4>Randomized prospective study.<h4>Methods</h4>This prospective study included 48 solitary pulmonary nodules and masses (18 benign, 30 malignant). Single shot echo planar spin echo diffusion weighted imaging (DWI) was performed with t ...[more]

PMID: 26740901

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Project description:BackgroundThe apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla.MethodsThis retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status.ResultsThree classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.ConclusionsThe combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.

Project description:OBJECTIVES:Diffusion-weighted imaging (DWI) and the introduction of the intravoxel incoherent motion (IVIM) model have provided a unique method for evaluating perfusion and diffusion within a tissue without the need for a contrast agent. Despite its relevance, cardiac DWI has thus far been limited by low b values because of signal loss induced by physiological motion. The goal of this study was to develop a methodology for estimating IVIM parameters of in vivo cardiac magnetic resonance imaging using an efficient DWI acquisition framework. This was achieved by investigating various acquisition strategies (principal component analysis [PCA] filtering and temporal maximum intensity projection [PCATMIP] and single trigger delay [TD]) and fitting methods. MATERIAL AND METHODS:Simulations were performed on a synthetic dataset of diffusion-weighted signal intensity (SI) to determine the fitting method that would yield IVIM parameters with the greatest accuracy. The required number of b values to correctly estimate IVIM parameters was also investigated. Breath-hold DWI scans were performed for 12 volunteers to collect several TD values during diastole. Thirteen b values ranging from 0 to 550 s/mm were used. The IVIM parameters derived using the data from all the acquired TDs (PCATMIP technique) were compared with those derived using a single acquisition performed at an optimized diastolic time point (1TD). RESULTS:The main result of this study was that PCATMIP, when combined with a fitting model that accounted for T1 and T2 relaxation, provided IVIM parameters with less variability. However, an acquisition performed with 1 optimized diastolic TD provided results that were as good as those provided using PCATMIP if the R-R variability during the acquisition was sufficiently low (± 5%). Furthermore, the use of only 9 b values (that could be acquired in 2 breath-holds), instead of 13 b values (requiring 3 breath-holds), was sufficient to determine the IVIM parameters. CONCLUSIONS:This study demonstrates that IVIM is technically feasible in vivo and reports for the first time the perfusion fraction, f, and the diffusion coefficients, D and D*, for the cardiac DWI of healthy volunteers. Motion-induced signal loss, which is the main problem associated with cardiac DWI, could be avoided with the combined use of sliding acquisition during the cardiac cycle and image postprocessing with the PCATMIP algorithm. This study provides new perspectives for perfusion imaging without a contrast agent and demonstrates that IVIM parameters can act as promising tools to further characterize microvascular abnormalities or dysfunction.

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Diffusion Weighted Magnetic Resonance Imaging for the Characterization of Solitary Pulmonary Lesions.

Publications

Diffusion Weighted Magnetic Resonance Imaging for the Characterization of Solitary Pulmonary Lesions.

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