Site-specific follow-up strategy for surgically resected patients with NSCLC based on ten-year follow-up data.

Objectives: There is currently no consensus regarding the optimal site-specific postoperative follow-up duration for patients with completely resected non-small cell lung cancer. Long-term surveillance for recurrence may lead to psychological distress or economic burdens for patients.We aimed to propose an appropriate site-specific follow-up strategy for patients with non-small cell lung cancer based on 10-year follow-up data.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB shim array fields.

Purpose: Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels.

Methods: Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations.

Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI.

Purpose: To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model.

Methods: Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation.

Sudden unexplained death in schizophrenia patients: An autopsy-based comparative study from China.

Explainable sudden deaths in schizophrenia patients due to both cardiac (SCD) and non-cardiac causes (SNCD) have been extensively documented. However, sudden unexplained death (SUD) in this cohort remains to be elucidated. This study retrospectively analyzed 18 SUD cases that underwent systematic autopsy at our institutes during the period 2010-2022.

Use of potassium ion channel and spliceosome proteins as diagnostic biomarkers for sudden unexplained death in schizophrenia.

Sudden unexplained death in schizophrenia (SUD-SCZ) is not uncommon and its incidence is approximately three times higher than that in the general population. However, diagnosis of SUD-SCZ remains a great challenge in forensic pathology. This study designed a two-phase study to investigate whether three proteins, namely two potassium ion channel proteins (KCNJ3 and KCNAB1) and one spliceosome protein (SF3B3) that were identified in our previous work, could be applied in the postmortem diagnosis of SUD-SCZ.

Cardiotoxicity of current antipsychotics: Newer antipsychotics or adjunct therapy?

Use of newer antipsychotics for substitution of current antipsychotics might be one way awaiting to be clinically verified to address antipsychotic cardiotoxic effects. Alternatively, the combination of existing antipsychotics with cardioprotective agents is also beneficial for patients with mental disorders for avoiding cardiotoxicity to the maximum.

Selective RF excitation designs enabled by time-varying spatially non-linear ΔB fields with applications in fetal MRI.

Purpose: To demonstrate, through numerical simulations, novel designs of spatially selective radiofrequency (RF) excitations of the fetal brain by both a restricted 2D slice and 3D inner-volume selection. These designs exploit a single-channel RF pulse, conventional gradient fields, and the spatially non-linear ΔB fields of a multi-coil shim array, using an auto-differentiation optimization algorithm.

Methods: The design algorithm jointly optimizes the RF pulse and the time-varying ΔB fields, which is produced by a 64-channel multi-coil ΔB body array to augment the RF and the linear gradient fields, using an auto-differentiation approach.

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy.

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making.

Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network.

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI observations of the gravid abdomen that are acquired at low spatial resolution but relatively high temporal resolution and over long durations (10-30 minutes).