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An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition.

ABSTRACT: PURPOSE:To establish a mathematical model to guide prospective T2-weighted four-dimensional magnetic resonance imaging (4DMRI) acquisition and to propose an efficient solution to expedite prospective T2-weighted 4DMRI acquisition. METHODS:Prospective T2-weighted 4DMRI acquisition was characterized by a mathematical model with 4DMRI acquisition time as the objective function and completeness of the image set, acquisition timing, image contrast, and image artifacts as constraints. Given the irregular nature of human respiration, an efficient solution based on the greedy strategy (ESGS) was proposed. The efficiency of the ESGS method was validated using healthy human subjects. Comparisons were made with the previous 4DMRI method incorporating the prefixed-order respiratory state splitting (PO-RSS) technique. RESULTS:4DMRI image sets acquired using the ESGS and PO-RSS methods had similar image quality. The average time to acquire a 4DMRI image set covering 60 slices at 10 respiratory states was reduced by 30%, from 13.1 min using the PO-RSS method to 9.0 min using the ESGS method. It was demonstrated that high-quality T2-weighted 4DMRI could be obtained within a reasonable amount of time and all slices within each of the three-dimensional volumes were indeed acquired at the same respiratory state. CONCLUSIONS:The ESGS method substantially reduces the acquisition time for T2-weighted 4DMRI, making it ready for clinical evaluation to obtain abdominal tumor motion for radiotherapy treatment planning.

SUBMITTER: Du D

PROVIDER: S-EPMC5997551 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition.

Du Dongsu D Mutic Sasa S Li H Harold HH Hu Yanle Y

Medical physics 20180506 6

<h4>Purpose</h4>To establish a mathematical model to guide prospective T2-weighted four-dimensional magnetic resonance imaging (4DMRI) acquisition and to propose an efficient solution to expedite prospective T2-weighted 4DMRI acquisition.<h4>Methods</h4>Prospective T2-weighted 4DMRI acquisition was characterized by a mathematical model with 4DMRI acquisition time as the objective function and completeness of the image set, acquisition timing, image contrast, and image artifacts as constraints. G ...[more]

PMID: 29663412

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Project description:ObjectivesThe aim of this study was to develop and verify a method to obtain good temporal resolution T2-weighted 4-dimensional (4D-T2w) magnetic resonance imaging (MRI) by using motion information from T1-weighted 4D (4D-T1w) MRI, to support treatment planning in MR-guided radiotherapy.Materials and methodsTen patients with primary non-small cell lung cancer were scanned at 1.5 T axially with a volumetric T2-weighted turbo spin echo sequence gated to exhalation and a volumetric T1-weighted stack-of-stars spoiled gradient echo sequence with golden angle spacing acquired in free breathing. From the latter, 20 respiratory phases were reconstructed using the recently developed 4D joint MoCo-HDTV algorithm based on the self-gating signal obtained from the k-space center. Motion vector fields describing the respiratory cycle were obtained by deformable image registration between the respiratory phases and projected onto the T2-weighted image volume. The resulting 4D-T2w volumes were verified against the 4D-T1w volumes: an edge-detection method was used to measure the diaphragm positions; the locations of anatomical landmarks delineated by a radiation oncologist were compared and normalized mutual information was calculated to evaluate volumetric image similarity.ResultsHigh-resolution 4D-T2w MRI was obtained. Respiratory motion was preserved on calculated 4D-T2w MRI, with median diaphragm positions being consistent with less than 6.6 mm (2 voxels) for all patients and less than 3.3 mm (1 voxel) for 9 of 10 patients. Geometrical positions were coherent between 4D-T1w and 4D-T2w MRI as Euclidean distances between all corresponding anatomical landmarks agreed to within 7.6 mm (Euclidean distance of 2 voxels) and were below 3.8 mm (Euclidean distance of 1 voxel) for 355 of 470 pairs of anatomical landmarks. Volumetric image similarity was commensurate between 4D-T1w and 4D-T2w MRI, as mean percentage differences in normalized mutual information (calculated over all respiratory phases and patients), between corresponding respiratory phases of 4D-T1w and 4D-T2w MRI and the tie-phase of 4D-T1w and 3-dimensional T2w MRI, were consistent to 0.41% ± 0.37%. Four-dimensional T2w MRI displayed tumor extent, structure, and position more clearly than corresponding 4D-T1w MRI, especially when mobile tumor sites were adjacent to organs at risk.ConclusionsA methodology to obtain 4D-T2w MRI that retrospectively applies the motion information from 4D-T1w MRI to 3-dimensional T2w MRI was developed and verified. Four-dimensional T2w MRI can assist clinicians in delineating mobile lesions that are difficult to define on 4D-T1w MRI, because of poor tumor-tissue contrast.

Project description:Background/purposeAdequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images.Materials/methodsWe compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance.Results19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods.ConclusionsThe ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy.

Project description:BackgroundRadiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters.ObjectiveTo evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test-retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing.Study typeProspective.SubjectsFifteen healthy participants.Field strength/sequenceA 3.0 T, axial T2 -weighted 2D turbo spin-echo pulse sequence, 181 scans acquired (2 test/retest reference scans and 12 with systematic variations in contrast weighting, resolution, and acceleration per participant; removing scans with artifacts).AssessmentOne hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing.Statistical testsRepeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately.ResultsGood to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness.Data conclusionsMany radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test-retest settings, depending on feature formulation and postprocessing.Evidence level2 TECHNICAL EFFICACY: Stage 2.

Project description:Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age- and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

Project description:BackgroundMyocardial fibrosis is a common hallmark of many diseases of the heart. Late gadolinium enhanced MRI is a powerful tool to image replacement fibrosis after myocardial infarction (MI). Interstitial fibrosis can be assessed indirectly from an extracellular volume fraction measurement using contrast-enhanced T1 mapping. Detection of short T2* species resulting from fibrotic tissue may provide an attractive non-contrast-enhanced alternative to directly visualize the presence of both replacement and interstitial fibrosis.ObjectiveTo goal of this paper was to explore the use of a T2*-weighted radial sequence for the visualization of fibrosis in mouse heart.MethodsC57BL/6 mice were studied with MI (n = 20, replacement fibrosis), transverse aortic constriction (TAC) (n = 18, diffuse fibrosis), and as control (n = 10). 3D center-out radial T2*-weighted images with varying TE were acquired in vivo and ex vivo (TE = 21 ?s-4 ms). Ex vivo T2*-weighted signal decay with TE was analyzed using a 3-component model. Subtraction of short- and long-TE images was used to highlight fibrotic tissue with short T2*. The presence of fibrosis was validated using histology and correlated to MRI findings.ResultsDetailed ex vivo T2*-weighted signal analysis revealed a fast (T2*fast), slow (T2*slow) and lipid (T2*lipid) pool. T2*fast remained essentially constant. Infarct T2*slow decreased significantly, while a moderate decrease was observed in remote tissue in post-MI hearts and in TAC hearts. T2*slow correlated with the presence of diffuse fibrosis in TAC hearts (r = 0.82, P = 0.01). Ex vivo and in vivo subtraction images depicted a positive contrast in the infarct co-localizing with the scar. Infarct volumes from histology and subtraction images linearly correlated (r = 0.94, P<0.001). Region-of-interest analysis in the in vivo post-MI and TAC hearts revealed significant T2* shortening due to fibrosis, in agreement with the ex vivo results. However, in vivo contrast on subtraction images was rather poor, hampering a straightforward visual assessment of the spatial distribution of the fibrotic tissue.

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An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition.

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An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition.

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