K Scheffler
Overview
Explore the profile of K Scheffler including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
92
Citations
1385
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Popova K, Glang F, Bosch D, Scheffler K, Avdievich N, Glybovski S, et al.
J Magn Reson
. 2024 Nov;
368:107791.
PMID: 39490302
Purpose: To improve transmit B field homogeneity and longitudinal coverage of a human head RF array, we developed a novel eight-element transceiver (TxRx) array using composite elements based on paired...
2.
Solomakha G, Bosch D, Glang F, Scheffler K, Avdievich N
Magn Reson Med
. 2023 Nov;
91(3):1268-1280.
PMID: 38009927
Purpose: The aim of this work is to evaluate a new eight-channel transceiver (TxRx) coaxial dipole array for imaging of the human head at 9.4T developed to improve specific absorption...
3.
Nemali A, Vockert N, Berron D, Maas A, Bernal J, Yakupov R, et al.
Med Image Anal
. 2023 Sep;
90:102913.
PMID: 37660483
Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical...
4.
Loktyushin A, Herz K, Dang N, Glang F, Deshmane A, Weinmuller S, et al.
Magn Reson Med
. 2021 Mar;
86(2):709-724.
PMID: 33755247
Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this...
5.
Brodt S, Gais S, Beck J, Erb M, Scheffler K, Schonauer M
Science
. 2018 Dec;
362(6418):1045-1048.
PMID: 30498125
Models of systems memory consolidation postulate a fast-learning hippocampal store and a slowly developing, stable neocortical store. Accordingly, early neocortical contributions to memory are deemed to reflect a hippocampus-driven online...
6.
Buckenmaier K, Rudolph M, Back C, Misztal T, Bommerich U, Fehling P, et al.
Sci Rep
. 2017 Oct;
7(1):13431.
PMID: 29044168
Ultra-low-field (ULF) nuclear magnetic resonance (NMR) is a promising spectroscopy method allowing for, e.g., the simultaneous detection of multiple nuclei. To overcome the low signal-to-noise ratio that usually hampers a...
7.
Avdievich N, Hoffmann J, Shajan G, Pfrommer A, Giapitzakis I, Scheffler K, et al.
NMR Biomed
. 2016 Dec;
30(2).
PMID: 28028862
Ultra-high field (UHF, ≥7 T) tight fit transceiver phased arrays improve transmit (Tx) efficiency (B /√P) in comparison with Tx-only arrays, which are usually larger to fit receive (Rx)-only arrays...
8.
Hagberg G, Bause J, Ethofer T, Ehses P, Dresler T, Herbert C, et al.
Neuroimage
. 2016 Sep;
144(Pt A):203-216.
PMID: 27663989
Mapping of the longitudinal relaxation time (T) with high accuracy and precision is central for neuroscientific and clinical research, since it opens up the possibility to obtain accurate brain tissue...
9.
Minoarivelo H, Hui C, Terblanche J, Pond S, Scheffler K
Oikos
. 2014 Oct;
123(10):1250-1260.
PMID: 25294947
Ecological interaction networks, such as those describing the mutualistic interactions between plants and their pollinators or between plants and their frugivores, exhibit non-random structural properties that cannot be explained by...
10.
Handwerker J, Bonehi V, Eschelbach M, Scheffler K, Ortmanns M, Anders J
Biomed Tech (Berl)
. 2013 Sep;
58 Suppl 1.
PMID: 24042946
No abstract available.