Optimal Fat Suppression in Head and Neck MRI: Comparison of Multipoint Dixon with 2 Different Fat-Suppression Techniques, Spectral Presaturation and Inversion Recovery, and STIR.

BACKGROUND AND PURPOSE: Uniform complete fat suppression is essential for identification and characterization of most head and pathology. Our aim was to compare the multipoint Dixon turbo spin-echo fat-suppression technique with 2 different fat-suppression techniques, including a hybrid spectral presaturation with inversion recovery technique and an inversion recovery STIR technique, in head and neck fat-suppression MR imaging. MATERIALS AND METHODS: Head and neck MR imaging datasets of 72 consecutive patients were retrospectively reviewed. All patients were divided into 2 groups based on the type of fat-suppression techniques used (group A: STIR and spectral presaturation with inversion recovery gadolinium-T1WI; group B: multipoint Dixon T2 TSE and multipoint Dixon gadolinium-T1WI TSE). Objective and subjective image quality and scan acquisition times were assessed and compared between multipoint Dixon T2 TSE versus STIR and multipoint Dixon gadolinium-T1WI TSE versus spectral presaturation with inversion recovery gadolinium-T1WI using the Mann-Whitney U test. RESULTS: A total of 64 patients were enrolled in the study (group A, n = 33 and group B, n = 31). Signal intensity ratios were significantly higher for multipoint Dixon T2 and gadolinium-T1WI techniques compared with STIR (P < .001) and spectral presaturation with inversion recovery gadolinium-T1WI (P < .001), respectively. Two independent blinded readers revealed that multipoint Dixon T2 and gadolinium-T1WI techniques had significantly higher overall image quality (P = .022 and P < .001) and fat-suppression grades (P < .013 and P < .001 across 3 different regions) than STIR and spectral presaturation with inversion recovery gadolinium-T1WI, respectively. The scan acquisition time was relatively short for the multipoint Dixon technique (2 minutes versus 4 minutes 56 seconds for the T2-weighted sequence and 2 minutes versus 3 minutes for the gadolinium-T1WI sequence). CONCLUSIONS: The multipoint Dixon technique offers better image quality and uniform fat suppression at a shorter scan time compared with STIR and spectral presaturation with inversion recovery gadolinium-T1WI techniques.

Dynamic Contrast-Enhanced MR Imaging in Head and Neck Cancer: Techniques and Clinical Applications.

In the past decade, dynamic contrast-enhanced MR imaging has had an increasing role in assessing the microvascular characteristics of various tumors, including head and neck cancer. Dynamic contrast-enhanced MR imaging allows noninvasive assessment of permeability and blood flow, both important features of tumor hypoxia, which is a marker for treatment resistance for head and neck cancer. Dynamic contrast-enhanced MR imaging has the potential to identify early locoregional recurrence, differentiate metastatic lymph nodes from normal nodes, and predict tumor response to treatment and treatment monitoring in patients with head and neck cancer. Quantitative analysis is in its early stage and standardization and refinement of technique are essential. In this article, we review the techniques of dynamic contrast-enhanced MR imaging data acquisition, analytic methods, current limitations, and clinical applications in head and neck cancer.

Impact of model-based iterative reconstruction on image quality of contrast-enhanced neck CT.

BACKGROUND AND PURPOSE: Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS: Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1-5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed. RESULTS: Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 (P = .016), though there was no difference at level 2 (P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx (P < .001) and oropharynx (P < .001) and for overall image quality (P < .001) and was scored lower at the vocal cords (P < .001) and sternoclavicular junction (P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction. CONCLUSIONS: Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction.