Ultra-short echo-time pulmonary MRI: Evaluation and reproducibility in COPD subjects with and without bronchiectasis

Ma, Weijing; Sheikh, Khadija; Svenningsen, Sarah; Pike, Damien; Guo, Fumin; Etemad-Rezai, Roya; Leipsic, Jonathan; Coxson, Harvey O.; McCormack, David G.; Párraga, Grace

doi:10.1002/jmri.24680

Cited by 62 publications

(67 citation statements)

References 19 publications

(24 reference statements)

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“…The same group has previously explored hyperpolarized 3 He for chronic obstructive pulmonary disease (COPD) 166 and application to this pathology is also likely feasible. Alternatively, an ultrashort echo time (UTE) approach has been used with compressed sensing for imaging COPD patients, where UTE MR signal intensity correlates to CT density and can be related to pulmonary function metrics such as FEV1/FRC 167 .…”

Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed.

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Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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“…However, it may be extended to other anatomic segments such as head/ neck, thorax, and pelvis with dedicated coils and can be applied for related applications, e.g., MR-based pulmonary function measurement for COPD and bronchiectasis 57,58 and radiation treatment planning for pelvis and prostate cancers. 48,59 Another limitation of this study is that the features used as inputs for clustering were selected based on the assumption that the Dixon-water, Dixon-fat, and R2 * properties of the target tissues are the appropriate features for identifying different tissue types.…”

Section: Discussionmentioning

confidence: 99%

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Stehning

et al. 2015

Medical Physics

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Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2 * images (1/T2 * ) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2 * , were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 µs (max: 1.350 µs; min: 0.180 µs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation. C 2015 American Association of Physicists in Medicine.

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“…Ultrashort echo time sequences (26) are usually used in voxel based methods to distinguish the signal of bone from air. As the signal is acquired very close to the end of the excitation in UTE imaging, almost no supplemental weighting of the signal is added to the proton density weighting, and a link could be identified between UTE data and hydrogen density (27) . This may open the way to a voxel‐based method to convert MRI data to hydrogen content, which could then be used for treatment planning by the method presented in this study.…”

Section: Discussionmentioning

confidence: 99%

Monte Carlo calculation based on hydrogen composition of the tissue for MV photon radiotherapy

Demol

Viard²,

Reynaert

2015

J Applied Clin Med Phys

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The purpose of this study was to demonstrate that Monte Carlo treatment planning systems require tissue characterization (density and composition) as a function of CT number. A discrete set of tissue classes with a specific composition is introduced. In the current work we demonstrate that, for megavoltage photon radiotherapy, only the hydrogen content of the different tissues is of interest. This conclusion might have an impact on MRI‐based dose calculations and on MVCT calibration using tissue substitutes. A stoichiometric calibration was performed, grouping tissues with similar atomic composition into 15 dosimetrically equivalent subsets. To demonstrate the importance of hydrogen, a new scheme was derived, with correct hydrogen content, complemented by oxygen (all elements differing from hydrogen are replaced by oxygen). Mass attenuation coefficients and mass stopping powers for this scheme were calculated and compared to the original scheme. Twenty‐five CyberKnife treatment plans were recalculated by an in‐house developed Monte Carlo system using tissue density and hydrogen content derived from the CT images. The results were compared to Monte Carlo simulations using the original stoichiometric calibration. Between 300 keV and 3 MeV, the relative difference of mass attenuation coefficients is under 1% within all subsets. Between 10 keV and 20 MeV, the relative difference of mass stopping powers goes up to 5% in hard bone and remains below 2% for all other tissue subsets. Dose‐volume histograms (DVHs) of the treatment plans present no visual difference between the two schemes. Relative differences of dose indexes D98,D95,D50,D05,D02, and Dmean were analyzed and a distribution centered around zero and of standard deviation below 2% (3σ) was established. On the other hand, once the hydrogen content is slightly modified, important dose differences are obtained. Monte Carlo dose planning in the field of megavoltage photon radiotherapy is fully achievable using only hydrogen content of tissues, a conclusion that might impact MRI dose calculation, but can also help selecting the optimal tissue substitutes when calibrating MVCT devices.PACS numbers: 87.55.D‐, 87.55.dk, 87.55.K‐, 87.57.Q‐

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Ultra-short echo-time pulmonary MRI: Evaluation and reproducibility in COPD subjects with and without bronchiectasis

Abstract: Pulmonary signal-intensity is reproducible and related to tissue density. In COPD subjects with and without bronchiectasis, signal-intensity was also related to pulmonary function and CT measurements.

Cited by 62 publications

References 19 publications

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Monte Carlo calculation based on hydrogen composition of the tissue for MV photon radiotherapy

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