Generalized min-max bound-based MRI pulse sequence design framework for wide-range T1 relaxometry: A case study on the tissue specific imaging sequence.

This paper proposes a new design strategy for optimizing MRI pulse sequences for T1 relaxometry. The design strategy optimizes the pulse sequence parameters to minimize the maximum variance of unbiased T1 estimates over a range of T1 values using the Cramér-Rao bound. In contrast to prior sequences optimized for a single nominal T1 value, the optimized sequence using our bound-based strategy achieves improved precision and accuracy for a broad range of T1 estimates within a clinically feasible scan time. The optimization combines the downhill simplex method with a simulated annealing process. To show the effectiveness of the proposed strategy, we optimize the tissue specific imaging (TSI) sequence. Preliminary Monte Carlo simulations demonstrate that the optimized TSI sequence yields improved precision and accuracy over the popular driven-equilibrium single-pulse observation of T1 (DESPOT1) approach for normal brain tissues (estimated T1 700-2000 ms at 3.0T). The relative mean estimation error (MSE) for T1 estimation is less than 1.7% using the optimized TSI sequence, as opposed to less than 7.0% using DESPOT1 for normal brain tissues. The optimized TSI sequence achieves good stability by keeping the MSE under 7.0% over larger T1 values corresponding to different lesion tissues and the cerebrospinal fluid (up to 5000 ms). The T1 estimation accuracy using the new pulse sequence also shows improvement, which is more pronounced in low SNR scenarios

The general pulse sequence scheme for tissue specific imaging (TSI).

<p>In each TR period, there are three imaging pulses (dark gray) followed by EPI acquisitions and interleaved by two inversion pulses (light gray) (After Fig 1 from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172573#pone.0172573.ref014" target="_blank">14</a>]). The three imaging pulses are characterized by their flip angles <i>α</i>₁, <i>α</i>₃, <i>α</i>₅. The dashed lines indicate the times <i>t</i> when each pulse is applied. The first imaging pulse is applied at the beginning of each TR (<i>t</i>₁ = 0). There are 8 pulse parameters to optimize for in the TSI sequence: times for the two inversion pulses <i>t</i>₂, <i>t</i>₄, times for the second and third imaging pulses <i>t</i>₃, <i>t</i>₅, flip angles of the three imaging pulses <i>α</i>₁, <i>α</i>₃, <i>α</i>₅ and the sequence repetition time TR.</p

Geometric interpretation of the Cramér-Rao Bound (CRB) of <i>T</i>₁ estimate in a linear space.

<p><b>h</b> is the signal weighting vector containing the measured signals at all acquisition times. <i>∂</i><b>h</b>/<i>∂T</i>₁ is the sensitivity vector, calculated as the derivative of the signal weighting vector with respect to <i>T</i>₁. Conceptually, increasing the norm of the sensitivity term ∥<i>∂</i><b>h</b>/<i>∂T</i>₁∥ will increase the impact of small changes in <i>T</i>₁ on the acquired signals. The orthogonality term sin <i>ϕ</i> is a consequence of the joint estimation of <i>T</i>₁ and <i>M</i>₀. The observed signal’s sensitivity for <i>M</i>₀ is <b>h</b>, while that of <i>T</i>₁ is <i>∂</i><b>h</b>/<i>∂T</i>₁. The more orthogonal these vectors are, the easier it becomes to ascribe changes in the observed signal to <i>M</i>₀ or <i>T</i>₁ unambiguously.</p

Optimized TSI pulse sequence parameters (TSI_new), compared against the original TSI pulse parameters from Table 2 of [14].

<p>Optimized TSI pulse sequence parameters (TSI_new), compared against the original TSI pulse parameters from Table 2 of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172573#pone.0172573.ref014" target="_blank">14</a>].</p

Comparing the <i>T</i>₁ estimates’ accuracy for four different approaches: NLSE with the new TSI sequence (blue), NLSE with the original TSI sequence (cyan), DESPOT1 (green), and NLSE with the SPGR sequence (black).

<p>The accuracy is measured in terms of the relative bias %, calculated as . This experiment uses a nominal <i>T</i>₁ value of 1500 ms. Simulated SNR levels are calibrated equivalently as 5 ≤ SNR ≤ 60 for the TSI sequences and 40 ≤ SNR ≤ 480 for the SPGR sequence. The new TSI sequence achieves the lowest overall relative bias and therefore highest accuracy among the four approaches.</p

Comparing <i>T</i>₁ estimates’ precision for four different approaches: NLSE with the new TSI sequence (blue) against its theoretical lower bound (red solid), NLSE with the original TSI sequence (cyan), DESPOT1 (green), and NLSE with the SPGR sequence (black) against its theoretical lower bound (magenta dashed).

<p>The precision is measured in terms of the relative mean estimation error, calculated as the standard deviation of <i>T</i>₁ estimates normalized by the true <i>T</i>₁. The theoretical lower bound of the relative mean estimation error is the relative error, calculated as the square root of the CRB on <i>T</i>₁ estimates normalized by the true <i>T</i>₁. SNR levels equalizing for both receiver bandwidths and echo times are calibrated as SNR = 125 for the TSI sequences and SNR = 1000 for the SPGR sequence. The new TSI sequence achieves the lowest mean estimation error and therefore highest precision for tested <i>T</i>₁ values.</p

Comparison of sensitivity (top panel) and orthogonality sin <i>ϕ</i> (bottom panel) of <i>T</i>₁ estimation for the new TSI sequence (blue), the original TSI sequence (cyan), and the SPGR sequence (black).

<p>The norm of <i>T</i>₁ sensitivity is calculated as the Euclidean norm of the derivative of the signal weighting vector with respect to <i>T</i>₁. Increasing the norm of the sensitivity will increase the impact of small changes in <i>T</i>₁ on the overall signal weighting vector <b>h</b>. sin <i>ϕ</i> is the orthogonality term defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172573#pone.0172573.g001" target="_blank">Fig 1</a>. The more orthogonal these vectors are, the easier it becomes to ascribe changes in the observed signal to <i>M</i>₀ or <i>T</i>₁ unambiguously. The top panel shows the new TSI sequence has the best sensitivity among the three sequences and SPGR has very poor sensitivity for <i>T</i>₁ estimation. The bottom panel shows the TSI-family sequences have greater orthogonality (above 0.7) than the SPGR sequence (equal to 0.58) for the tested <i>T</i>₁ range.</p

Validating Nonlinear Registration to Improve Subtraction Images for Lesion Detection and Quantification in Multiple Sclerosis

To propose and validate nonlinear registration techniques for generating subtraction images because of their ability to reduce artifacts and improve lesion detection and lesion volume quantification

Mapping resting-state functional connectivity using perfusion MRI

Resting-state, low-frequency (< 0.08\ua0Hz) fluctuations of blood oxygenation level-dependent (BOLD) magnetic resonance signal have been shown to exhibit high correlation among functionally connected regions. However, correlations of cerebral blood flow (CBF) fluctuations during the resting state have not been extensively studied. The main challenges of using arterial spin labeling perfusion magnetic resonance imaging to detect CBF fluctuations are low sensitivity, low temporal resolution, and contamination from BOLD. This work demonstrates CBF-based quantitative functional connectivity mapping by combining continuous arterial spin labeling (CASL) with a neck labeling coil and a multi-channel receiver coil to achieve high perfusion sensitivity. In order to reduce BOLD contamination, the CBF signal was extracted from the CASL signal time course by high frequency filtering. This processing strategy is compatible with sinc interpolation for reducing the timing mismatch between control and label images and has the flexibility of choosing an optimal filter cutoff frequency to minimize BOLD fluctuations. Most subjects studied showed high CBF correlation in bilateral sensorimotor areas with good suppression of BOLD contamination. Root-mean-square CBF fluctuation contributing to bilateral correlation was estimated to be 29 ± 19% (N = 13) of the baseline perfusion, while BOLD fluctuation was 0.26 ± 0.14% of the mean intensity (at 3\ua0T and 12.5\ua0ms echo time)

Impact of chemotherapy for childhood leukemia on brain morphology and function.

OBJECTIVE: Using multidisciplinary treatment modalities the majority of children with cancer can be cured but we are increasingly faced with therapy-related toxicities. We studied brain morphology and neurocognitive functions in adolescent and young adult survivors of childhood acute, low and standard risk lymphoblastic leukemia (ALL), which was successfully treated with chemotherapy. We expected that intravenous and intrathecal chemotherapy administered in childhood will affect grey matter structures, including hippocampus and olfactory bulbs, areas where postnatal neurogenesis is ongoing. METHODS: We examined 27 ALL-survivors and 27 age-matched healthy controls, ages 15-22 years. ALL-survivors developed disease prior to their 11th birthday without central nervous system involvement, were treated with intrathecal and systemic chemotherapy and received no radiation. Volumes of grey, white matter and olfactory bulbs were measured on T1 and T2 magnetic resonance images manually, using FIRST (FMRIB's integrated Registration and Segmentation Tool) and voxel-based morphometry (VBM). Memory, executive functions, attention, intelligence and olfaction were assessed. RESULTS: Mean volumes of left hippocampus, amygdala, thalamus and nucleus accumbens were smaller in the ALL group. VBM analysis revealed significantly smaller volumes of the left calcarine gyrus, both lingual gyri and the left precuneus. DTI data analysis provided no evidence for white matter pathology. Lower scores in hippocampus-dependent memory were measured in ALL-subjects, while lower figural memory correlated with smaller hippocampal volumes. INTERPRETATION: Findings demonstrate that childhood ALL, treated with chemotherapy, is associated with smaller grey matter volumes of neocortical and subcortical grey matter and lower hippocampal memory performance in adolescence and adulthood