Assessment of dual-energy computed tomography derived virtual monoenergetic imaging for target volume delineation of brain metastases.

BACKGROUND: Objective and subjective assessment of image quality of brain metastases on dual-energy computed tomography (DECT) virtual monoenergetic imaging (VMI) and its impact on target volume delineation. MATERIALS AND METHODS: 26 patients with 37 brain metastases receiving Magnetic Resonance Imaging (MRI) and DECT for stereotactic radiotherapy planning were included in this retrospective analysis. Lesion contrast (LC), contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were assessed for reconstructed VMI at 63 keV and artificial 120 kV Computed Tomography (CT). Image contrast and demarcation of metastases between 120 kV CT, VMI and MRI were subjectively assessed. Brain metastases were delineated by four radiation oncologists on VMI with a fixed or free brain window and contours were compared to solely MRI-based delineation using the Dice similarity coefficient. RESULTS: LC, CNR and SNR were significantly higher in VMI than in 120 kV CT (p < 0.0001). Image contrast and lesion demarcation were significantly better on VMI compared to 120 kV CT (p < 0.0001). Mean gross tumor volume (GTV)/planning target volume (PTV) Dice similarity coefficients were 0.87/0.9 for metastases without imaging uncertainties (no artifacts, calcification or impaired visibility with MRI) but worse for metastases with imaging uncertainties (0.71/0.74). Target volumes delineated on VMI were around 5-10% smaller compared to MRI. CONCLUSION: Image quality of VMI is objectively and subjectively superior to conventional CT. VMI provides significant advantages in stereotactic radiotherapy planning with improved visibility of brain metastases and geometrically distortion-free representation of brain metastases. Beside a plausibility check of MRI-based target volume delineation, VMI might improve reliability and accuracy in target volume definition particularly in cases with imaging uncertainties with MRI.

Non-rigid image registration of 4D-MRI data for improved delineation of moving tumors.

BACKGROUND: To increase the image quality of end-expiratory and end-inspiratory phases of retrospective respiratory self-gated 4D MRI data sets using non-rigid image registration for improved target delineation of moving tumors. METHODS: End-expiratory and end-inspiratory phases of volunteer and patient 4D MRI data sets are used as targets for non-rigid image registration of all other phases using two different registration schemes: In the first, all phases are registered directly (dir-Reg) while next neighbors are successively registered until the target is reached in the second (nn-Reg). Resulting data sets are quantitatively compared using diaphragm and tumor sharpness and the coefficient of variation of regions of interest in the lung, liver, and heart. Qualitative assessment of the patient data regarding noise level, tumor delineation, and overall image quality was performed by blinded reading based on a 4 point Likert scale. RESULTS: The median coefficient of variation was lower for both registration schemes compared to the target. Median dir-Reg coefficient of variation of all ROIs was 5.6% lower for expiration and 7.0% lower for inspiration compared with nn-Reg. Statistical significant differences between the two schemes were found in all comparisons. Median sharpness in inspiration is lower compared to expiration sharpness in all cases. Registered data sets were rated better compared to the targets in all categories. Over all categories, mean expiration scores were 2.92 ± 0.18 for the target, 3.19 ± 0.22 for nn-Reg and 3.56 ± 0.14 for dir-Reg and mean inspiration scores 2.25 ± 0.12 for the target, 2.72 ± 215 0.04 for nn-Reg and 3.78 ± 0.04 for dir-Reg. CONCLUSIONS: In this work, end-expiratory and inspiratory phases of a 4D MRI data sets are used as targets for non-rigid image registration of all other phases. It is qualitatively and quantitatively shown that image quality of the targets can be significantly enhanced leading to improved target delineation of moving tumors.

Feasibility of 4D T2* quantification in the lung with oxygen gas challenge in patients with non-small cell lung cancer.

Blood oxygen level-dependent (BOLD) MRI is a non-invasive diagnostic method for assessing tissue oxygenation level, by changes in the transverse relaxation time T2*. 3D BOLD imaging of lung tumours is challenging, because respiratory motion can lead to significant image quality degradation. The purpose of this work was to explore the feasibility of a three dimensional (3D) Cartesian multi gradient echo (MGRE) sequence for T2* measurements of non-small cell lung tumours during free-breathing. A non-uniform quasi-random reordering of the pahse encoding lines that allocates more sampling points near the k-space origin resulting in efficient undersampling pattern for parallel imaging was combined with multi echo acquisition and self-gating. In a series of three patients 3D T2* maps of lung carcinomas were generated with isotropic spatial resolution and full tumour coverage at air inhalation and after hyperoxic gas challenge in arbitrary respiratory phases using the proposed self-gated MGRE acquisition. The changes in T2* on the inhalation of hyperoxic gas relative to air were quantified. Significant changes in T2* were observed following oxygen inhalation in the tumour (p < 0.02). Thus, the self-gated MGRE sequence can be used for assessment of BOLD signal with isotropic resolution and arbitrary respiratory phases in non-small cell lung cancer.