$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and
robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and
$B_0$ calibration scans can mitigate this but add scan time and cannot be
applied retrospectively to previously collected data. Here, we proposed a
calibration-free sequence-adaptive deep-learning framework, to estimate and
correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its
capability on arbitrary MRF sequences at 3T, where no training data were
previously obtained. Such approach can be applied to any previously-acquired
and future MRF-scans. The flexibility in directly applying this framework to
other quantitative sequences is also highlighted.

提出了一种无需校准的自适应深度学习框架，用于估计和纠正任何 MRF 序列的 $B_1^+$ 和 $B_0$ 效应，展示了其在 3T 任意 MRF 序列上的能力，可应用于以前获取的和未来的 MRF 扫描。强调了将该框架直接应用于其他定量序列的灵活性。

序列自适应场扭曲估计（SAFE）：增强 MRF 定量中 $B_1^+$ 和 $B_0$ 不均匀性的回顾性估计和校正

Sequence adaptive field-imperfection estimation (SAFE): retrospective  estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced  MRF quantification

GBM (Glioblastoma multiforme) is the most aggressive type of brain tumor in
adults that has a short survival rate even after aggressive treatment with
surgery and radiation therapy. The changes on magnetic resonance imaging (MRI)
for patients with GBM after radiotherapy are indicative of either
radiation-induced necrosis (RN) or recurrent brain tumor (rBT). Screening for
rBT and RN at an early stage is crucial for facilitating faster treatment and
better outcomes for the patients. Differentiating rBT from RN is challenging as
both may present with similar radiological and clinical characteristics on MRI.
Moreover, learning-based rBT versus RN classification using MRI may suffer from
class imbalance due to lack of patient data. While synthetic data generation
using generative models has shown promise to address class imbalance, the
underlying data representation may be different in synthetic or augmented data.
This study proposes computational modeling with statistically rigorous repeated
random sub-sampling to balance the subset sample size for rBT and RN
classification. The proposed pipeline includes multiresolution radiomic feature
(MRF) extraction followed by feature selection with statistical significance
testing (p<0.05). The five-fold cross validation results show the proposed
model with MRF features classifies rBT from RN with an area under the curve
(AUC) of 0.8920+-.055. Moreover, considering the dependence between survival
time and censor time (where patients are not followed up until death), we
demonstrate the feasibility of using MRF radiomic features as a non-invasive
biomarker to identify patients who are at higher risk of recurrence or
radiation necrosis. The cross-validated results show that the MRF model
provides the best overall performance with an AUC of 0.770+-.032.

提出了一种基于 MRF 的计算模型，通过多分辨率放射性医学（MRF）特征提取和显著性检验筛选（P < 0.05）分类预测 GBM 患者的放射性坏死（RN）和复发性脑肿瘤（rBT），并探讨了其作为非创伤性生物标志物识别高风险复发或放射坏死患者的可行性。

脑肿瘤复发与放射性坏死分类及患者生存预测

Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient  Survivability Prediction

This paper proposes a learning-based approach to scene parsing inspired by
the deep Recursive Context Propagation Network (RCPN). RCPN is a deep
feed-forward neural network that utilizes the contextual information from the
entire image, through bottom-up followed by top-down context propagation via
random binary parse trees. This improves the feature representation of every
super-pixel in the image for better classification into semantic categories. We
analyze RCPN and propose two novel contributions to further improve the model.
We first analyze the learning of RCPN parameters and discover the presence of
bypass error paths in the computation graph of RCPN that can hinder contextual
propagation. We propose to tackle this problem by including the classification
loss of the internal nodes of the random parse trees in the original RCPN loss
function. Secondly, we use an MRF on the parse tree nodes to model the
hierarchical dependency present in the output. Both modifications provide
performance boosts over the original RCPN and the new system achieves
state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler
urban datasets.

本文提出了一种基于学习的场景解析方法，通过逐层递归的上下文传播方式，以图像中的超像素为分类基础进行解析，并利用 MRF 模型建模结果的层次依赖关系从而实现对 Stanford Background，SIFT-Flow 和 Daimler urban 数据集的最新性能。