Temporal change in nodules and results of transformation in serial CT scans, with visual presentation in a subtraction image and a Jacobian map.

<p>The subtraction image shows gray for pixel value = 0, black for negative, and white for positive value. The color-coded Jacobian map shows green for Jacobian <1, red for Jacobian >1, and yellow for Jacobian = 1.</p

Detection of Time-Varying Structures by Large Deformation Diffeomorphic Metric Mapping to Aid Reading of High-Resolution CT Images of the Lung

<div><p>Objectives</p><p>To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM).</p><p>Methods</p><p>Fifteen cases of lung cancer with serial lung CT images (interval: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors.</p><p>Results</p><p>The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs.</p><p>Conclusions</p><p>LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.</p></div

Comparison between cascading<i>α</i> LDDMM and B-spline registration.

<p>A metastatic nodule in the right upper lung (red arrow) was growing slightly at clinical follow-up (A: 1st time point, B: 2nd time point). C: Subtracted image with B-spline registration shows slight mis-registration along the lung parenchyma and a thin, rim-like difference around the nodule. D: Jacobian map obtained from B-spline registration is inhomogeneous and volume expansion is not clear. E: Cascading LDDMM shows complete registration. F: Jacobian map from cascading LDDMM obviously shows a red-colored spot, which corresponds to the growing nodule.</p

Automated and quantitative growth measurement using the cascading <i>α</i> LDDMM transformation matrix.

<p>Linear registration (A) shows some mis-registration as black or white linear structures, whereas LDDMM transformation (B) shows complete nodule registration, as well as the surrounding lung parenchyma. Once the nodule at the first time point is defined (C), the nodule definition can be automatically transformed to the second time point (E), which enables automated volume measurement.</p

Comparison of growing and emerging tumor.

<p>A: The original first time point image. B: The linearly registered second time point image. C: A subtracted image between the first time point and the linearly registered second time point. D: A subtracted image with the first iteration of single <i>α</i> LDDMM (<i>α</i>/<i>γ</i> = 0.01). E: A subtracted image with the cascading <i>α</i> LDDMM (<i>α</i>/<i>γ</i> = 0.01−0.005−0.002). F: A Jacobian map calculated from the transformation matrix of the cascading <i>α</i> LDDMM. Orange, red, and blue arrows indicate locations of emerging tumor (orange), growing tumors (red), and misalignment artifacts (blue). Note that almost the entire misalignment is removed by LDDMM, clearly indicating the small nodule that appeared in the second image (orange arrow). This new nodule was not detected by the Jacobian map, which is a metric of growth.</p

Registration accuracy.

<p>Displacement of corresponding landmarks on the first time point images and the second time point images was measured as registration accuracy. All pair-wise comparisons showed a statistically significant difference (*<i>p<</i>0.05, post hoc test). Displacements of landmarks were almost zero in cascading <i>α</i> LDDMM.</p

Comparison of three registration results.

<p>A: The original first time point image. B: Linearly registered second time point image. Note that only the region of interest was normalized in the second time point. C: A subtracted image between the first time point and the linearly registered second time point. D: A subtracted image with the first iteration of LDDMM (single <i>α</i> LDDMM) (<i>α</i>/<i>γ</i> = 0.01). E: A subtracted image with the cascading <i>α</i> LDDMM (<i>α</i>/<i>γ</i> = 0.01−0.005−0.002). F: A Jacobian map calculated from the transformation matrix of the cascading <i>α</i> LDDMM. Red and blue arrows indicate locations of growing tumors (red) and misalignment artifacts (blue). The dotted lines show reciprocal positions.</p

Patient characteristics.

<p>NSCLC: Non-small cell lung carcinoma, SCLC: small cell lung carcinoma.</p

Computation time of the proposed model, together with computation times of several widely used pipelines using different tools like SPM, FSL, HAMMER, AIR, LDDMM, and Freesurfer.

<p>Computation time of the proposed model, together with computation times of several widely used pipelines using different tools like SPM, FSL, HAMMER, AIR, LDDMM, and Freesurfer.</p

Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools

<div><p>Multi-atlas brain segmentation of human brain MR images allows quantification research in structural neuroimaging. To achieve high accuracy and computational efficiency of segmentation relies on a custom subset of atlases for each target subject. However, the criterion for atlas pre-selection remains an open question. In this study, two atlas pre-selection approaches based on location-based feature matching were proposed and compared to random and mutual information-based methods using a database of 47 atlases. A varying number of atlases ranked top with hierarchical structural granularity were compared using Dice overlap. The results indicated that the proposed 4L approach consistently led to the highest level of accuracy at a given number of employed atlases in both adult and geriatric populations. In addition, the proposed two methods (4L and LV) can reduce 20 times computational time compared with the stereotypical mutual information-based method. Our pre-selection strategy would provide better segmentation performance in terms of both accuracy and efficiency. The proposed atlas pre-selection will be further implemented into our online automatic brain image segmentation system (<a href="http://www.mricloud.org/" target="_blank">www.mricloud.org</a>).</p></div