Comparison of image quality of head and neck lesions between 3D gradient echo sequences with compressed sensing and the multi-slice spin echo sequence

Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence

Background

The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI).

Methods

Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted.

Results

Turbo factor 200 produced the best SNR (median = 17.01) and GM/WM CNR (median = 2.29), but turbo factor 100 offered the most reproducible SNR (IQR = 2.72) and GM/WM CNR (IQR = 0.14). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly (p > 0.05) with the range of turbo factors used in the study by one-way ANOVA analysis.

Conclusion

Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

Usefulness of reconstructed images of Gd-enhanced 3D gradient echo sequences with compressed sensing for mandibular cancer diagnosis: comparison with CT images and histopathological findings

OBJECTIVES

To compare the delineation of mandibular cancer by 3D T1 turbo field echo with compressed SENSE (CS-3D-T1TFE) images and MDCT images, and to compare both sets of images with histopathological findings, as the gold standard, to validate the accuracy and clinical usefulness of CS-3D-T1TFE reconstruction.

METHODS

Twenty-four patients with mandibular squamous cell carcinoma (SCC) who underwent MRI including CS-3D-T1TFE and MDCT examinations before surgery were retrospectively included. For both examinations, 0.5-mm-thick coronal plane images and 0.5-mm-thick plane images perpendicular and parallel to the dentition were constructed. Two radiologists rated bone invasion in three categories indexed by cortical bone, cancellous bone, and mandibular canal (MC), and inter-rater agreement was assessed by weighted kappa statistics. In 20 of the 24 patients who underwent surgery, the correlation of bone invasion with the histopathological evaluation by pathologists was assessed using Pearson's correlation coefficient. Soft-tissue invasion was assessed by diagnosing the presence of invasion into the mylohyoid muscle, gingivobuccal fold, and masticator space, and inter-rater agreement was assessed by kappa statistics.

RESULTS

The interobserver agreement for bone invasion assessment was almost perfect with CS-3D-T1TFE and substantial with MDCT. The image evaluations by both observers agreed with the pathological evaluations in 15 of the 20 cases, showing high correlation (r > 0.8). CS-3D-T1TFE also showed higher inter-rater agreement than MDCT for all measures of soft-tissue invasion.

CONCLUSIONS

CS-3D-T1TFE reconstructed images were clinically useful in accurately depicting the extent of mandibular cancer invasion and potentially solving the problem of lesion overestimation associated with conventional MRI.

KEY POINTS

• Reconstructed CS-3D-T1TFE images were useful for the diagnosis of mandibular cancer. • CS-3D-T1TFE images showed higher inter-rater agreement than MDCT and high correlation with pathological findings. • CS-3D-T1TFE images may solve the problem of overestimation of the tumor extent, which has been associated with MRI in the past.