Junaid R. Rajput

Friedrich-Alexander-Universität Erlangen-Nürnberg

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Andreas Maier

Friedrich-Alexander-Universität Erlangen-Nürnberg

Moritz Zaiß

Max Planck Institute for Biological Cybernetics

Moritz Fabian

Friedrich-Alexander-Universität Erlangen-Nürnberg

Arnd Dörfler

Universitätsklinikum Erlangen

Manuel Schmidt

Friedrich-Alexander-Universität Erlangen-Nürnberg

Angelika Mennecke

Friedrich-Alexander-Universität Erlangen-Nürnberg

Tim A. Möhle

Universitätsklinikum Erlangen

Armin M. Nagel

Universitätsklinikum Erlangen

Jochen A. Sembill

Friedrich-Alexander-Universität Erlangen-Nürnberg

Joji B. Kuramatsu

Friedrich-Alexander-Universität Erlangen-Nürnberg

Cooperative Institutions

Friedrich-Alexander-Universität Erlangen-Nürnberg

Universitätsklinikum Erlangen

Goethe University Frankfurt

Siemens Healthcare (Germany)

University Hospital Frankfurt

Heidelberg University

University Hospital Bonn

Technical University of Munich

Max Planck Institute for Biological Cybernetics

Max Planck Society

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DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification

Magnetic Resonance in Medicine (2022)

Leonie Hunger Junaid R. Rajput Kiril Vadimovic Klein Angelika Mennecke Moritz Fabian

Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi‐pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B 1 inhomogeneities. The input data for a neural feed‐forward network consisted of 7 T in vivo uncorrected Z ‐spectra of a single B 1 level, and a B 1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel‐wise to target data consisting of Lorentzian amplitudes generated conventionally by 5‐pool Lorentzian fitting of normalized, denoised, B 0 ‐ and B 1 ‐corrected Z ‐spectra. The deepCEST network was trained with Gaussian negative log‐likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B 1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B 0 ‐ and B 1 ‐corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.

Robustness

10.1002/mrm.29520

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Pseudo-Square Tomosynthesis Trajectory for Interventional Lung Procedures

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (2021)

Junaid R. Rajput Alexander Preuhs Guenter Lauritsch Andreas Maier

Digital tomosynthesis (DTS) is a 3-D imaging technique with limited depth resolution and can be used to guide a catheter during interventional lung procedures. DTS uses projection data acquired over a limited angular range to create tomographic slices with high in-plane resolution. This limited angular acquisition results in out-of-plane artifacts in the reconstructed volume. This work focused on improving the image quality of the tomosynthesis volume for lung imaging by selecting an acquisition trajectory that increases depth resolution, provides a 2-D blur response, and simplifies analytical reconstruction. We proposed a new pseudo-square acquisition geometry for DTS and performed the quantitative and qualitative comparison with arc (pseudo-linear) and circle acquisition geometries. We reconstructed DTS volumes for various angular ranges and numbers of projections for arc, circle, and pseudo-square trajectories using filtered back-projection and compared them with reference computed tomography (CT) scan using Pearson correlation coefficient (PCC). Based on simulations, we showed that pseudo-square acquisition resulted in better image quality than inbounded arc and inbounded circle geometries. It provided on average 5.5% and 26% higher PCC with reference CT than circle and arc DTS, respectively.

Tomosynthesis

Tomographic reconstruction

Square (algebra)

10.1109/nss/mic44867.2021.9875725

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Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions

Lecture notes in computer science (2024)

Junaid R. Rajput Simon Weinmueller Jonathan Endres Peter Dawood Florian Knöll

10.1007/978-3-031-72104-5_23

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Table Motion Detection in Interventional Coronary Angiography

Informatik aktuell (2021)

Junaid R. Rajput Karthik Shetty Andreas Maier Martin Berger

Table (database)

Tree (set theory)

Computed tomographic angiography

10.1007/978-3-658-33198-6_15

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Comprehensive 7 T CEST: A clinical MRI protocol covering multiple exchange rate regimes

NMR in Biomedicine (2024)

Moritz Fabian Junaid R. Rajput Jan‐Rüdiger Schüre Simon Weinmüller Angelika Mennecke

Abstract Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so‐called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq‐CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0‐fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high‐grade brain tumours.

Saturation (graph theory)

10.1002/nbm.5096

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Abstract: Physics-informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T

Informatik aktuell (2024)

Junaid R. Rajput Tim A. Möhle Moritz Fabian Angelika Mennecke Jochen A. Sembill

Autoencoder

10.1007/978-3-658-44037-4_87

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Physics-Informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T

Lecture notes in computer science (2023)

Junaid R. Rajput Tim A. Möhle Moritz Fabian Angelika Mennecke Jochen A. Sembill

Autoencoder

10.1007/978-3-031-43993-3_44

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Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant

Informatik aktuell (2024)

Moritz Zaiß Junaid R. Rajput Hoai Nam Dang Vladimir Golkov Daniel Cremers

Sequence (biology)

10.1007/978-3-658-44037-4_28

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Non-invasive high-resolution in vivo pH mapping in brain tumors by 31P‑informed deepCEST MRI

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024)

Jan‐Rüdiger Schüre Junaid R. Rajput Eike Steidl Manoj Shrestha Ralf Deichmann

Motivation: The pH value is an important biomarker for many diseases. MRI-based 3D pH mapping for clinical routine would be an enormous benefit for diagnostics. Goal(s): Prediction of intracellular 31P-pHi maps from 1H APTw-CEST MRI data using a voxel-wise neural network, aiming to improve brain tumor imaging. Approach: Fifteen glioblastoma patients underwent 3T MRI with both APTw-CEST and 31P-MRS. A neural network trained on 11 patients data to correlate APTw-CEST features with 31P-derived pHi values, tested on 4 additional patients. Results: The neural network's pHi predictions closely matched 31P-pHi maps, showing potential for high-resolution, non-invasive pHi mapping in brain tumors. Impact: High resolution pH imaging for better diagnosis of diseases (inflammation, stroke, tumor) and therapy monitoring in clinical routine.

10.58530/2024/1085

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