Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of Ktrans and ve values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.
This work addresses the challenging problem of parsing 2D radiographs into salient anatomical regions such as
the left and right lungs and the heart. We propose the integration of an automatic detection of a constellation
of landmarks via rejection cascade classifiers and a learned geometric constellation subset detector model with
a multi-object active appearance model (MO-AAM) initialized by the detected landmark constellation subset.
Our main contribution is twofold. First, we propose a recovery method for false positive and negative landmarks
which allows to handle extreme ranges of anatomical and pathological variability. Specifically we (1) recover
false negative (missing) landmarks through the consensus of inferences from subsets of the detected landmarks,
and (2) choose one from multiple false positives for the same landmark by learning Gaussian distributions for the
relative location of each landmark. Second, we train a MO-AAM using the true landmarks for the detectors and
during test, initialize the model using the detected landmarks. Our model fitting allows simultaneous localization
of multiple regions by encoding the shape and appearance information of multiple objects in a single model. The
integration of landmark detection method and MO-AAM reduces mean distance error of the detected landmarks
from 20.0mm to 12.6mm. We assess our method using a database of scout CT scans from 80 subjects with widely
Robust point matching (RPM) jointly estimates correspondences and non-rigid warps between unstructured
point-clouds. RPM does not, however, utilize information of the topological structure or group memberships of
the data it is matching. In numerous medical imaging applications, each extracted point can be assigned group
membership attributes or labels based on segmentation, partitioning, or clustering operations. For example,
points on the cortical surface of the brain can be grouped according to the four lobes. Estimated warps should
enforce the topological structure of such point-sets, e.g. points belonging to the temporal lobe in the two
point-sets should be mapped onto each other.
We extend the RPM objective function to incorporate group membership labels by including a Label Entropy
(LE) term. LE discourages mappings that transform points within a single group in one point-set onto points
from multiple distinct groups in the other point-set. The resulting Labeled Point Matching (LPM) algorithm
requires a very simple modification to the standard RPM update rules.
We demonstrate the performance of LPM on coronary trees extracted from cardiac CT images. We partitioned
the point sets into coronary sections without a priori anatomical context, yielding potentially disparate labelings
(e.g. [1,2,3] → [a,b,c,d]). LPM simultaneously estimated label correspondences, point correspondences, and a
non-linear warp. Non-matching branches were treated wholly through the standard RPM outlier process akin to
non-matching points. Results show LPM produces warps that are more physically meaningful than RPM alone.
In particular, LPM mitigates unrealistic branch crossings and results in more robust non-rigid warp estimates.