Mapping Brain Clusterings to Reproduce Missing MRI Scans

Machine learning has become an essential part of medical imaging research. For example, convolutional neural networks (CNNs) are used to perform brain tumor segmentation, which is the process of distinguishing between tumoral and healthy cells. This task is often carried out using four different magnetic resonance imaging (MRI) scans of the patient. Due to the cost and effort required to produce the scans, oftentimes one of the four scans is missing, making the segmentation process more tedious. To obviate this problem, we propose two MRI-to-MRI translation approaches that synthesize an approximation of the missing image from an existing one. In particular, we focus on creating the missing T2 Weighted sequence from a given T1 Weighted sequence. We investigate clustering as a solution to this problem and propose BrainClustering, a learning method that creates approximation tables that can be queried to retrieve the missing image. The images are clustered with hierarchical clustering methods to identify the main tissues of the brain, but also to capture the different signal intensities in local areas. We compare this method to the general image-to-image translation tool Pix2Pix, which we extend to fit our purposes. Finally, we assess the quality of the approximated solutions by evaluating the tumor segmentations that can be achieved using the synthesized outputs. Pix2Pix achieves the most realistic approximations, but the tumor areas are too generalized to compute optimal tumor segmentations. BrainClustering obtains transformations that deviate more from the original image but still provide better segmentations in terms of Hausdorff distance and Dice score. Surprisingly, using the complement of T1 Weighted (i.e. inverting the color of each pixel) also achieves good results. Our new methods make segmentation software more feasible in practice by allowing the software to utilize all four MRI scans, even if one of the scans is missing

MRI Scan Synthesis Methods based on Clustering and Pix2Pix

We consider a missing data problem in the context of automatic segmentation methods for Magnetic Resonance Imaging (MRI) brain scans. Usually, automated MRI scan segmentation is based on multiple scans (e.g., T1-weighted, T2-weighted, T1CE, FLAIR). However, quite often a scan is blurry, missing or otherwise unusable. We investigate the question whether a missing scan can be synthesized. We exemplify that this is in principle possible by synthesizing a T2-weighted scan from a given T1-weighted scan. Our first aim is to compute a picture that resembles the missing scan closely, measured by average mean squared error (MSE). We develop/use several methods for this, including a random baseline approach, a clustering-based method and pixel-to-pixel translation method by Isola et al. (Pix2Pix) which is based on conditional GANs. The lowest MSE is achieved by our clustering-based method. Our second aim is to compare the methods with respect to the effect that using the synthesized scan has on the segmentation process. For this, we use a DeepMedic model trained with the four input scan modalities named above. We replace the T2-weighted scan by the synthesized picture and evaluate the segmentations with respect to the tumor identification, using Dice scores as numerical evaluation. The evaluation shows that the segmentation works well with synthesized scans (in particular, with Pix2Pix methods) in many cases.Comment: Accepted at AIME 202

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Abstract Background To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee. Methods Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor. Results 3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999). Conclusions For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample. Trial registration DRKS00024156. Relevance statement Using a DL-based algorithm, 54% faster image acquisition (178 s versus 384 s) for 3D-sequences may be possible for 3-T MRI of the knee. Key points • Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI. • DL-based algorithm achieved better subjective image quality than conventional compressed sensing. • For 3D knee MRI at 3 T, 54% faster image acquisition may be possible. Graphical Abstrac

Vasoconstriction and Impairment of Neurovascular Coupling after Subarachnoid Hemorrhage: a Descriptive Analysis of Retinal Changes

Impaired cerebral autoregulation and neurovascular coupling (NVC) contribute to delayed cerebral ischemia after subarachnoid hemorrhage (SAH). Retinal vessel analysis (RVA) allows non-invasive assessment of vessel dimension and NVC hereby demonstrating a predictive value in the context of various neurovascular diseases. Using RVA as a translational approach, we aimed to assess the retinal vessels in patients with SAH. RVA was performed prospectively in 24 patients with acute SAH (group A: day 5–14), in 11 patients 3 months after ictus (group B: day 90 ± 35), and in 35 age-matched healthy controls (group C). Data was acquired using a Retinal Vessel Analyzer (Imedos Systems UG, Jena) for examination of retinal vessel dimension and NVC using flicker-light excitation. Diameter of retinal vessels—central retinal arteriolar and venular equivalent—was significantly reduced in the acute phase (p < 0.001) with gradual improvement in group B (p < 0.05). Arterial NVC of group A was significantly impaired with diminished dilatation (p < 0.001) and reduced area under the curve (p < 0.01) when compared to group C. Group B showed persistent prolonged latency of arterial dilation (p < 0.05). Venous NVC was significantly delayed after SAH compared to group C (A p < 0.001; B p < 0.05). To our knowledge, this is the first clinical study to document retinal vasoconstriction and impairment of NVC in patients with SAH. Using non-invasive RVA as a translational approach, characteristic patterns of compromise were detected for the arterial and venous compartment of the neurovascular unit in a time-dependent fashion. Recruitment will continue to facilitate a correlation analysis with clinical course and outcome

Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

&lt;jats:title&gt;Abstract&lt;/jats:title&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Background&lt;/jats:title&gt; &lt;jats:p&gt;To investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Methods&lt;/jats:title&gt; &lt;jats:p&gt;Twenty healthy volunteers were examined using at 3-T scanner with a fat-saturated, coronal, 2D proton density-weighted sequence with four acceleration levels (2.3, 4, 6, and 8) and a 3D sequence with three acceleration levels (8, 10, and 13), all accelerated with CS and reconstructed using the conventional algorithm and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using 6 criteria on a 5-point Likert scale (overall impression, artifacts, and delineation of the subscapularis tendon, bone, acromioclavicular joint, and glenoid labrum). Objective image quality was measured by calculating signal-to-noise-ratio, contrast-to-noise-ratio, and a structural similarity index measure. All reconstructions were compared to the clinical standard (CS 2D acceleration factor 2.3; CS 3D acceleration factor 8). Additionally, subjective and objective image quality were compared between CS and CS-AI with the same acceleration levels.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Results&lt;/jats:title&gt; &lt;jats:p&gt;Both 2D and 3D sequences reconstructed with CS-AI achieved on average significantly better subjective and objective image quality compared to sequences reconstructed with CS with the same acceleration factor (&lt;jats:italic&gt;p&lt;/jats:italic&gt; ≤ 0.011). Comparing CS-AI to the reference sequences showed that 4-fold acceleration for 2D sequences and 13-fold acceleration for 3D sequences without significant loss of quality (&lt;jats:italic&gt;p&lt;/jats:italic&gt; ≥ 0.058).&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Conclusions&lt;/jats:title&gt; &lt;jats:p&gt;For MRI of the shoulder at 3 T, a DL-based algorithm allowed additional acceleration of acquisition times compared to the conventional approach.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Relevance statement&lt;/jats:title&gt; &lt;jats:p&gt;The combination of deep-learning and compressed sensing hold the potential for further scan time reduction in 2D and 3D imaging of the shoulder while providing overall better objective and subjective image quality compared to the conventional approach.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Trial registration&lt;/jats:title&gt; &lt;jats:p&gt;DRKS00024156.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Key points&lt;/jats:title&gt; &lt;jats:p&gt;• Combination of compressed sensing and deep learning improved image quality and allows for significant acceleration of shoulder MRI.&lt;/jats:p&gt; &lt;jats:p&gt;• Deep learning-based algorithm achieved better subjective and objective image quality than conventional compressed sensing.&lt;/jats:p&gt; &lt;jats:p&gt;• For shoulder MRI at 3 T, 40% faster image acquisition for 2D sequences and 38% faster image acquisition for 3D sequences may be possible.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Graphical Abstract&lt;/jats:title&gt; &lt;/jats:sec&gt

MRI Follow-up of Astrocytoma: Automated Coregistration and Color-Coding of FLAIR Sequences Improves Diagnostic Accuracy With Comparable Reading Time

BackgroundMRI follow‐up is widely used for longitudinal assessment of astrocytoma, yet reading can be tedious and error‐prone, in particular when changes are subtle.Purpose/HypothesisTo determine the effect of automated, color‐coded coregistration (AC) of fluid attenuated inversion recovery (FLAIR) sequences on diagnostic accuracy, certainty, and reading time compared to conventional follow‐up MRI assessment of astrocytoma patients.Study TypeRetrospective.PopulationIn all, 41 patients with neuropathologically confirmed astrocytoma.Field Strength/Sequence1.0–3.0T/FLAIRAssessmentThe presence or absence of tumor progression was determined based on FLAIR sequences, contrast‐enhanced T1 sequences, and clinical data. Three radiologists assessed 47 MRI study pairs in a conventional reading (CR) and in a second reading supported by AC after 6 weeks. Readers determined the presence/absence of tumor progression and indicated diagnostic certainty on a 5‐point Likert scale. Reading time was recorded by an independent assessor.Statistical TestsThe Wilcoxon test was used to assess reading time and diagnostic certainty. Differences in diagnostic accuracy, sensitivity, and specificity were analyzed with the McNemar mid‐p test.ResultsReaders attained significantly higher overall sensitivity (0.86 vs. 0.75; P < 0.05) and diagnostic accuracy (0.84 vs. 0.73; P < 0.05) for detection of progressive nonenhancing tumor burden when using AC compared to CR. There was a strong trend towards higher specificity within the AC‐augmented reading, yet without statistical significance (0.83 vs. 0.71; P = 0.08). Sensitivity for unequivocal disease progression was similarly high in both approaches (AC: 0.94, CR: 0.92), while for marginal disease progressions, it was significantly higher in AC (AC: 0.78, CR: 0.58; P < 0.05). Reading time including application loading time was comparable (AC: 38.1 ± 16.8 sec, CR: 36.0 ± 18.9 s; P = 0.25).Data ConclusionCompared to CR, AC improves comparison of FLAIR signal hyperintensity at MRI follow‐up of astrocytoma patients, allowing for a significantly higher diagnostic accuracy, particularly for subtle disease progression at a comparable reading time