Martin Schwartz

MR-based respiratory and cardiac motion correction for PET imaging.

HighlightsPET motion correction from simultaneously acquired MR‐derived motion model.Fast MR acquisition freeing scan time per PET bed for further diagnostic sequences.Clinically feasible setup: streamlined processing in Gadgetron evaluation on a cohort of 36 patients.Publicly available: https://sites.google.com/site/kspaceastronauts. Graphical abstract Figure. No caption available. ABSTRACT Purpose: To develop a motion correction for Positron‐Emission‐Tomography (PET) using simultaneously acquired magnetic‐resonance (MR) images within 90 s. Methods: A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG‐derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed. A joint Compressed Sensing reconstruction and motion estimation of the subsampled data provides motion‐resolved MR images (respiratory + cardiac). A 1‐POINT DIXON method is applied to these MR images to derive a motion‐resolved attenuation map. The motion model and the attenuation map are fed to the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) PET reconstruction system in which the motion correction is incorporated. All reconstruction steps are performed online on the scanner via Gadgetron to provide a clinically feasible setup for improved general applicability. The method was evaluated on 36 patients with suspected liver or lung metastasis in terms of lesion quantification (SUVmax, SNR, contrast), delineation (FWHM, slope steepness) and diagnostic confidence level (3‐point Likert‐scale). Results: A motion correction could be conducted for all patients, however, only in 30 patients moving lesions could be observed. For the examined 134 malignant lesions, an average improvement in lesion quantification of 22%, delineation of 64% and diagnostic confidence level of 23% was achieved. Conclusion: The proposed method provides a clinically feasible setup for respiratory and cardiac motion correction of PET data by simultaneous short‐term MRI. The acquisition sequence and all reconstruction steps are publicly available to foster multi‐center studies and various motion correction scenarios.

Self‐navigated 4D cartesian imaging of periodic motion in the body trunk using partial k‐space compressed sensing

To enable fast and flexible high‐resolution four‐dimensional (4D) MRI of periodic thoracic/abdominal motion for motion visualization or motion‐corrected imaging.

Spontaneous mechanical and electrical activities of human calf musculature at rest assessed by repetitive single‐shot diffusion‐weighted MRI and simultaneous surface electromyography

Assessment of temporal and spatial relations between spontaneous mechanical activities in musculature (SMAM) at rest as revealed by diffusion‐weighted imaging (DWI) and electrical muscular activities in surface EMG (sEMG). Potential influences of static and radiofrequency magnetic fields on muscular activity on sEMG measurements at rest were examined systematically.

An easy-to-use image labeling platform for automatic magnetic resonance image quality assessment

In medical imaging, images are usually evaluated by a human observer (HO) depending on the underlying diagnostic question which can be a time-demanding and cost-intensive process. Model observers (MO) which mimic the human visual system can help to support the HO during this reading process or can provide feedback to the MR scanner and/or HO about the derived image quality. For this purpose MOs are trained on HO-derived image labels with respect to a certain diagnostic task. We propose a non-reference image quality assessment system based on a machine-learning approach with a deep neural network and active learning to keep the amount of needed labeled training data small. A labeling platform is developed as a web application with accounted data security and confidentiality to facilitate the HO labeling procedure. The platform is made publicly available.

Spatial-temporal perfusion patterns of the human liver assessed by pseudo-continuous arterial spin labeling MRI

PURPOSE To investigate the capabilities of a modern pseudo-continuous arterial spin labeling (PCASL) technique for non-invasive assessment of the temporal and spatial distribution of the liver perfusion in healthy volunteers on a clinical MR system at 3T. MATERIALS AND METHODS A 2D-PCASL multi-slice echo planar imaging sequence was adapted to the specific conditions in liver: a) labeling by PCASL was optimized to the flow characteristics in the portal vein, b) background suppression was applied for reduction of motion related artifacts, c) post labeling delays (PLDs) were varied over a large range (0.7-3.5s) in order to get better insight in the temporal and spatial distribution of tagged blood in the liver, and d) a special timed-breathing protocol was used allowing for recording of 16 to 18 label-control image pairs and a reference M0 image for each of 4 to 6 slices within approx. 5min for one PLD. RESULTS Measurements with multiple PLDs showed dominating perfusion signal in macroscopic blood vessels for PLDs up to 1.5 s, whereas pure liver parenchyma revealed maximum perfusion signal for a PLD of approx. 2 s, and detectable signal up to PLDs of 3.5 s. Data fitting to a perfusion model for liver provided a mean global perfusion of 153±15ml/100g/min and a mean transit time of 1938±332ms in liver parenchyma. Measurements with a single PLD of 2 s demonstrated that portal-venous and arterial perfusion components can be measured separately by two measurements with two different positions of the labeling plane (one for labeling of the global hepatopetal blood flow and one for selective labeling of the portal blood flow only). Relative contribution of blood from the hepatic artery to the global liver perfusion, the hepatic perfusion index (HPI), amounted to approx. 23%. CONCLUSION Modern and adapted protocols for assessment of liver perfusion by PCASL have the potential to provide perfusion and blood transit time maps in reasonable acquisition time.

Volumetric assessment of spontaneous mechanical activities by simultaneous multi-slice MRI techniques with correlation to muscle fiber orientation

The purpose of this work was assessment of volumetric characteristics of spontaneous mechanical activities in musculature (SMAMs) by diffusion‐weighted simultaneous multi‐slice (DW‐SMS) imaging and spatial correlation to anatomical structure, as revealed by fusion to fiber tractographic information derived from diffusion‐tensor imaging (DTI).

A machine-learning framework for automatic reference-free quality assessment in MRI

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7% for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.