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  • 1
    In: Magnetic Resonance in Medicine, Wiley, Vol. 87, No. 4 ( 2022-04), p. 1771-1783
    Abstract: To develop a methodology to simultaneously perform single echo Dixon water‐fat imaging and susceptibility‐weighted imaging (SWI) based on a single echo time (TE) ultra‐short echo time (UTE) (sUTE) scan to assess vertebral fractures and degenerative bone changes in the thoracolumbar spine. Methods A methodology was developed to solve the smoothness‐constrained inverse water‐fat problem to separate water and fat while removing unwanted low‐frequency phase terms. Additionally, the corrected UTE phase was used for SWI. UTE imaging (TE: 0.14 ms, 3T MRI) was performed in the lumbar spine of nine patients with vertebral fractures and bone marrow edema (BME). All images were reviewed by two radiologists. Water‐ and fat‐separated images were analyzed in comparison with short‐tau inversion recovery (STIR) and with respect to BME visibility. The visibility of fracture lines and cortical outlining of the UTE magnitude images were analyzed in comparison with computed tomography. Results Unwanted phase components, dominated by the B 1 phase, were removed from the UTE phase images. The rating of the diagnostic quality of BME visualization showed a high preference for the sUTE‐Dixon water‐ and fat‐separated images in comparison with STIR. The UTE magnitude images enabled better visualizing fracture lines compared with STIR and slightly better visibility of cortical outlining. With increasing SWI weighting osseous structures and fatty tissues were enhanced. Conclusion The proposed sUTE‐Dixon‐SWI methodology allows the removal of unwanted low‐frequency phases and enables water‐fat separation and SWI processing from a single complex UTE image. The methodology can be used for the simultaneous assessment of vertebral fractures and BME of the thoracolumbar spine.
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1493786-4
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  • 2
    In: European Radiology, Springer Science and Business Media LLC, Vol. 33, No. 7 ( 2023-02-18), p. 4875-4884
    Abstract: To evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR images in patients with shoulder pain. Methods Prospectively, thirty-eight patients (14 women, mean age 40.0 ± 15.2 years) with shoulder pain underwent morphological MRI using a pseudo-random, density-weighted k -space scheme with an acceleration factor of 2.5 using CS only. An automated DL-based algorithm (CS DL) was used to create reconstructions of the same k -space data as used for CS reconstructions. Images were analyzed by two radiologists and assessed for pathologies, image quality, and visibility of anatomical landmarks using a 4-point Likert scale. Results Overall agreement for the detection of pathologies between the CS DL reconstructions and CS images was substantial to almost perfect ( κ 0.95 (95% confidence interval 0.82–1.00)). Image quality and the visibility of the rotator cuff, articular cartilage, and axillary recess were overall rated significantly higher for CS DL images compared to CS ( p   〈  0.03). Contrast-to-noise ratios were significantly higher for cartilage/fluid (CS DL 198 ± 24.3, CS 130 ± 32.2, p  = 0.02) and ligament/fluid (CS DL 184 ± 17.3, CS 141 ± 23.5, p  = 0.03) and SNR values were significantly higher for ligaments and muscle of the CS DL reconstructions ( p   〈  0.04). Conclusion Evaluation of shoulder pathologies was feasible using a DL-based algorithm for MRI reconstruction and denoising. In clinical routine, CS DL may be beneficial in particular for reducing image noise and may be useful for the detection and better discrimination of discrete pathologies. Summary statement Assessment of shoulder pathologies was feasible with improved image quality as well as higher SNR using a compressed sensing deep learning–based framework for image reconstructions and denoising. Key Points • Automated deep learning–based reconstructions showed a significant increase in signal-to-noise ratio and contrast-to-noise ratio (p  〈  0.04) with only a slight increase of reconstruction time of 40 s compared to CS. • All pathologies were accurately detected with no loss of diagnostic information or prolongation of the scan time. • Significant improvements of the image quality as well as the visibility of the rotator cuff, articular cartilage, and axillary recess were detected.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1472718-3
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  • 3
    In: European Radiology, Springer Science and Business Media LLC
    Abstract: To evaluate and compare the diagnostic performance of CT-like images based on a 3D T1-weighted spoiled gradient-echo sequence (T1 GRE), an ultra-short echo time sequence (UTE), and a 3D T1-weighted spoiled multi-echo gradient-echo sequence (FRACTURE) with conventional CT in patients with suspected osseous shoulder pathologies. Materials and methods Patients with suspected traumatic dislocation of the shoulder ( n  = 46, mean age 40 ± 14.5 years, 19 women) were prospectively recruited and received 3-T MR imaging including 3D T1 GRE, UTE, and 3D FRACTURE sequences. CT was performed in patients with acute fractures and served as standard of reference ( n  = 25). Agreement of morphological features between the modalities was analyzed including the glenoid bone loss, Hill-Sachs interval, glenoid track, and the anterior straight-line length. Agreement between the modalities was assessed using Bland-Altman plots, Student’s t -test, and Pearson’s correlation coefficient. Inter- and intrareader assessment was evaluated with weighted Cohen’s κ and intraclass correlation coefficient. Results All osseous pathologies were detected accurately on all three CT-like sequences ( n  = 25, κ  = 1.00). No significant difference in the percentage of glenoid bone loss was found between CT (mean ± standard deviation, 20.3% ± 8.0) and CT-like MR images (FRACTURE 20.6% ± 7.9, T1 GRE 20.4% ± 7.6, UTE 20.3% ± 7.7, p  〉 0.05). When comparing the different measurements on CT-like images, measurements performed using the UTE images correlated best with CT. Conclusion Assessment of bony Bankart lesions and other osseous pathologies was feasible and accurate using CT-like images based on 3-T MRI compared with conventional CT. Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT. Clinical relevance statement In an acute trauma setting, CT-like images based on a T1 GRE, UTE, or FRACTURE sequence might be a useful alternative to conventional CT scan sparing associated costs as well as radiation exposure. Key Points • No significant differences were found for the assessment of the glenoid bone loss when comparing measurements of CT-like MR images with measurements of conventional CT images. • Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT whereas the FRACTURE sequence appeared to be the most robust regarding motion artifacts. • The T1 GRE sequence had the highest resolution with high bone contrast and detailed delineation of even small fractures but was more susceptible to motion artifacts.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1472718-3
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  • 4
    In: European Radiology, Springer Science and Business Media LLC, Vol. 33, No. 2 ( 2022-08-18), p. 1445-1455
    Abstract: To evaluate the performance of single-echo Dixon water-fat imaging and computed tomography (CT)–like imaging based on a single ultrashort echo time (sUTE) MR sequence for imaging of vertebral fractures as well as degenerative bone changes of the spine in comparison to conventional CT and MR sequences. Methods Thirty patients with suspected acute vertebral fractures were examined using a 3-T MRI, including an sUTE sequence as well as short-tau inversion recovery (STIR) and T1-weighted sequences. During postprocessing, water-fat separation was performed by solving the smoothness-constrained inverse water-fat problem based on a single-complex UTE image. By removing the unwanted low-frequency phase terms, additional MR-based susceptibility-weighted-like (SW-like) images with CT-like contrast were created. Two radiologists evaluated semi-quantitative and quantitative features of fractures and degenerative changes independently and separately on CT and MR images. Results In total, all 58 fractures were accurately detected of whom 24 were correctly classified as acute fractures with an edema detected on the water-fat-separated UTE images, using STIR and T1w sequences as standard of reference. For the morphological assessment of fractures and degenerative changes, the overall agreement between SW-like images and CT was substantial to excellent (e.g., Genant: κ 0.90 (95% confidence interval 0.54–1.00); AO/Magerl: κ 0.75 (95% confidence interval 0.43–1.00)). Overall inter-reader agreement for water-fat-separated UTE images and SW-like images was substantial to almost perfect. Conclusion Detection and assessment of vertebral fractures and degenerative bone changes of the spine were feasible and accurate using water-fat-separated images as well as SW-like images, both derived from the same sUTE-Dixon sequence. Key Points • The detection of acute vertebral fractures was feasible using water-fat-separated images and CT-like images reconstructed from one sUTE sequence. • Assessment of the vertebral fractures using SW-like images with CT-like contrast was found to be comparable to conventional CT. • sUTE imaging of the spine can help reduce examination times and radiation exposure.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1472718-3
    Library Location Call Number Volume/Issue/Year Availability
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