http://ieeexplore.ieee.org TOC Alert for Publication# 42 2012 February 10 31 2 C1 C4 115 31 2 C2 C2 43 31 2 153 163 2313 31 2 164 182 2633 31 2 183 191 1565 31 2 192 206 1900 31 2 207 216 921 31 2 217 230 1605 31 2 231 239 908 31 2 240 250 1501 $T^{2}$ test, applied to the Jacobian matrices of the surface parameterizations. A permutation test, along with the cumulative distribution of $p$-values, were used to estimate the overall statistical significance of differences. The results show our algorithm's power to detect subtle group differences in cortical surfaces.]]> 31 2 251 264 895 $T_{1}$ contrast, but conventional approaches inherently suffer from a 50% signal loss. The super stimulated-echo, which uses a specialized radio-frequency (RF) pulse train, has been proposed in order to improve the signal while preserving motion and $T_{1}$ sensitivity. This paper presents a novel and straightforward method for designing the super stimulated-echo pulse train using inversion pulse design techniques. This method can also create adiabatic designs with an improved response to RF transmit field variations. The scheme was validated in phantom experiments and shown in vivo to improve signal-to-noise ratio (SNR). We have applied a super stimulated-echo to metabolic MRI with hyperpolarized $^{13}{rm C}$-labeled molecules. For spectroscopic imaging of hyperpolarized agents, several repetition times are required but only a single stimulated-echo encoding is feasible, which can lead to unwanted motion blurring. To address this, a super stimulated-echo preparation scheme was used in which the diffusion weighting is terminated prior to the acquisition, and we observed a SNR increases of 60% in phantoms and 49% in vivo over a conventional stimulated-echo. Experiments following injection of hyperpolarized $[1{hbox{-}}^{13}{rm C}]$ -pyruvate in murine transgenic cancer models have shown improved delineation for tumors since signals from metabolites within tumor tissues are retained while those from the vasculature are suppressed by the diffusion preparation scheme.]]> 31 2 265 275 1202 31 2 276 286 1050 and was based on a 2-D polynomial fit performed on phase data from outside the heated region, to estimate the background phase inside the region of interest. While their approach was undeniably a fundamental progress in terms of robustness against tissue motion and magnetic perturbations, the underlying mathematical formalism requires a thick unheated border and may be subject to numerical instabilities with high order polynomials. A novel method of reference-free PRFS MRT is described here, using a physically consistent formalism, which exploits mathematical properties of the magnetic field in a homogeneous or near-homogeneous medium.]]> 31 2 287 301 3265 31 2 302 311 416 31 2 312 325 2339 31 2 326 340 2974 31 2 341 358 5529 as well as several other competing methods in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. For mitosis detection, an overall precision of 95.8% and a recall of 88.1% were achieved. For mitosis segmentation, the mean and standard deviation for the localization errors of the start and end points of all mitosis stages were well below 1 and 2 frames, respectively. In particular, an overall temporal location error of $0.73 pm 1.29$ frames was achieved for locating daughter cell birth events.]]> 31 2 359 369 1192 31 2 370 379 1509 31 2 380 390 1531 31 2 391 404 1253 $mu{rm SPECT}$ data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on $mu{rm SPECT}$ and $mu{rm CT}$ scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative $^{99{rm m}}{rm Tc}$ concentration. All tumor activity estimates achieved ${<}{3%}$ bias after ${sim}{15}$ ordered-subsets expectation maximization (OSEM) iterations $(times 10~{hbox {subsets}})$ , with better than 8% precision ( ${leq}{25%}$ greater than the Cramer–Rao lower bound). The projection-based fitting approach also outperformed three stand- rdized uptake value (SUV)-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean ${pm}$ standard deviation of the bias of VOI estimates of bead concentration were $0.9pm 9.5%$, comparable to those of a perturbation geometric transfer matrix (pGTM) approach $({-}{5.4}pm 8.6%)$; however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between $mu{rm CT}$ and $mu{rm SPECT}$ image volumes.]]> 31 2 405 416 1577 $1times 3~{hbox {cm}}$ region of interest. These findings indicate the potential of the proposed method for the improvement of range resolution in ultrasonography without deterioration in temporal resolution, resulting in enhanced detection of vessel stenosis.]]> 31 2 417 429 3053 $gamma=sigma+iomegavarepsilon$ inside the human body where $sigma$ and $varepsilon$ denote electrical conductivity and permittivity, respectively. Using B1 maps acquired by an magnetic resonance imaging scanner, it produces cross-sectional images of $sigma$ and $varepsilon$ at the Larmor frequency. Since current MREPT methods rely on an assumption of a locally homogeneous admittivity, there occurs a reconstruction error where this assumption fails. Rigorously analyzing the reconstruction error in MREPT, we showed that the error is fundamental and may cause technical difficulties in interpreting MREPT images of a general inhomogeneous object. We performed numerical simulations and phantom experiments to quantitatively support the error analysis. We compared the MREPT image reconstruction problem with that of magnetic resonance electrical impedance tomography (MREIT) to highlight distinct features of both methods to probe the same object in terms of its high- and low-frequency conductivity distributions, respectively. MREPT images showed large errors along boundaries where admittivity values changed whereas MREIT images showed no such boundary effects. Noting that MREIT makes use of the term neglected in MREPT, a novel MREPT admittivity image reconstruction method is proposed to deal with the boundary effects, which requires further investigation on the complex directional derivative in the real Euclidian space ${BBR}^3$.]]> 31 2 430 437 792 31 2 438 448 1174 $0.975pm 0.006$ and a mean absolute surface distance error of $0.84pm 0.23~{hbox {mm}}$, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.]]> 31 2 449 460 4362 31 2 461 473 4384 31 2 474 486 1859 31 2 487 503 347 $times$-accelerated human breast CS DCE-MRI acquisition with a 192$^{2}$ matrix size, the coefficients of variation (CoVs) in voxel-level parameters due to the random acquisition are 1.1%, 0.96%, and 1.5% for the tissue parameters $K^{rm trans}$, $v_{rm e}$, and $v_{rm p}$, with an average error in the mean of $-$2.5%, $-$2.0%, and $-$3.7%, respectively. Only 5% of the acquisition schemes had a systematic underestimation larger than than 4.2%, 3.7%, and 6.1%, respectively. For a 2$times$ -accelerated rat brain CS DSC-MRI study with a $64^{2}$ matrix size, the CoVs due to the random acquisition were 19%, 9.5%, and 15% for the cerebral blood flow and blood volume and mean transit time, respectively, and the average errors in the tumor mean were 9.2%, 0.49%, and $-$7.0%, respectively. Across 11 $thinspace$000 different - S reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance.]]> 31 2 504 511 751 31 2 512 522 833 31 2 523 523 345 31 2 524 524 210 31 2 C3 C3 26