Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases

BACKGROUND AND PURPOSE

MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading.

MATERIALS AND METHODS

One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks.

RESULTS

The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P < .01) when using automated color-coding, and the median Likert score for diagnostic certainty improved from 2 (interquartile range, 2-3) to 4 (interquartile range, 3-5) (P < .05) compared with the conventional reading. Interrater agreement was excellent (κ = 0.80; 95% CI, 0.71-0.89) with automated color-coding compared with a moderate agreement (κ = 0.46; 95% CI, 0.34-0.58) with the conventional reading approach. When considering the time required for image preprocessing, the overall average time for reading an examination was longer in the automated color-coding approach (91.5 [SD, 23.1] seconds versus 79.4 [SD, 34.7 ] seconds, P < .001).

CONCLUSIONS

Compared with the conventional reading, automated color-coding of lesion changes in follow-up examinations of patients with brain metastases significantly increased the rate of correct diagnoses and resulted in higher diagnostic certainty.

Reduction of artifacts caused by orthopedic hardware in the spine in spectral detector CT examinations using virtual monoenergetic image reconstructions and metal-artifact-reduction algorithms

OBJECTIVE

Aim of this study was to assess the artifact reduction in patients with orthopedic hardware in the spine as provided by (1) metal-artifact-reduction algorithms (O-MAR) and (2) virtual monoenergetic images (MonoE) as provided by spectral detector CT (SDCT) compared to conventional iterative reconstruction (CI).

METHODS

In all, 28 consecutive patients with orthopedic hardware in the spine who underwent SDCT-examinations were included. CI, O-MAR and MonoE (40-200 keV) images were reconstructed. Attenuation (HU) and noise (SD) were measured in order to calculate signal-to-noise ratio (SNR) of paravertebral muscle and spinal canal. Subjective image quality was assessed by two radiologists in terms of image quality and extent of artifact reduction.

RESULTS

O-MAR and high-keV MonoE showed significant decrease of hypodense artifacts in terms of higher attenuation as compared to CI (CI vs O-MAR, 200 keV MonoE: -396.5HU vs. -115.2HU, -48.1HU; both p ≤ 0.001). Further, artifacts as depicted by noise were reduced in O-MAR and high-keV MonoE as compared to CI in (1) paravertebral muscle and (2) spinal canal-CI vs. O-MAR/200 keV: (1) 34.7 ± 19.0 HU vs. 26.4 ± 14.4 HU, p ≤ 0.05/27.4 ± 16.1, n.s.; (2) 103.4 ± 61.3 HU vs. 72.6 ± 62.6 HU/60.9 ± 40.1 HU, both p ≤ 0.001. Subjectively both O-MAR and high-keV images yielded an artifact reduction in up to 24/28 patients.

CONCLUSION

Both, O-MAR and high-keV MonoE reconstructions as provided by SDCT lead to objective and subjective artifact reduction, thus the combination of O-MAR and MonoE seems promising for further reduction.