Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning.

Purpose: To investigate image quality and agreement of derived cardiac function parameters in a novel joint image reconstruction and segmentation approach based on disentangled representation learning, enabling real-time cardiac cine imaging during free-breathing.

Methods: A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with MR scans specifically acquired for model development. An exploratory feasibility study evaluated the method on undersampled real-time acquisitions using an in-house developed spiral bSSFP pulse sequence in eight healthy participants and five patients with intermittent atrial fibrillation.

Prognostic Factors in Therapy Regimes of Breast Cancer Patients with Brain Metastases: A Retrospective Monocentric Analysis.

Breast cancer patients who develop brain metastases have a high mortality rate and a massive decrease in quality of life. Approximately 10-15% of all patients with breast cancer (BC) and 5-40% of all patients with metastatic BC develop brain metastasis (BM) during the course of the disease. However, there is only limited knowledge about prognostic factors in the treatment of patients with brain metastases in breast cancer (BMBC).

Advanced Automated Model for Robust Bone Marrow Segmentation in Whole-body MRI.

Rationale And Objectives: To establish an advanced automated bone marrow (BM) segmentation model on whole-body (WB-)MRI in monoclonal plasma cell disorders (MPCD), and to demonstrate its robust performance on multicenter datasets with severe myeloma-related pathologies.

Materials And Methods: The study cohort comprised multi-vendor, multi-protocol imaging data acquired with varying field strength across 8 different centers. In total, 210 WB-MRIs of 207 MPCD patients were included.

Self-consistent Quantum Linear Response with a Polarizable Embedding Environment.

Quantum computing presents a promising avenue for solving complex problems, particularly in quantum chemistry, where it could accelerate the computation of molecular properties and excited states. This work focuses on computing excitation energies with hybrid quantum-classical algorithms for near-term quantum devices, combining the quantum linear response (qLR) method with a polarizable embedding (PE) environment. We employ the self-consistent operator manifold of quantum linear response (q-sc-LR) on top of a unitary coupled cluster (UCC) wave function in combination with a Davidson solver.

Salvage Autologous Transplant in Relapsed Multiple Myeloma: Long-Term Follow-Up of the Phase 3 GMMG ReLApsE Trial.

The multicenter, phase III GMMG ReLApsE trial (EudraCT-No:2009-013856-61) randomized relapsed and/or refractory multiple myeloma (RRMM) patients equally to lenalidomide/dexamethasone (LEN/DEX, 25mg days 1-21/40mg weekly, 4-week cycles) re-induction, salvage high dose chemotherapy (sHDCT, melphalan 200mg/m2), autologous stem cell transplantation (ASCT) and LEN maintenance (10mg/day; transplant arm, n=139) versus continuous LEN/DEX (control arm, n=138). Ninety-four percent of patients had received frontline HDCT/ASCT. We report an updated analysis of survival endpoints with a median follow-up of 99 months.