Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking.

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Fast 19 F spectroscopic imaging with pseudo-spiral k-space sampling.

Fluorine MRI is finding wider acceptance in theranostics applications where imaging of 19 F hotspots of fluorinated contrast material is central. The essence of such applications is to capture ghosting-artifact-free images of the inherently low MR response under clinically viable conditions. To serve this purpose, this work introduces the balanced spiral spectroscopic imaging (BaSSI) sequence, which is implemented on a 3.0 T clinical scanner and is capable of generating 19 F hotspot images in an efficient manner. The sequence utilizes an all-phase-encoded pseudo-spiral k-space trajectory, enabling the acquisition of broadband (80 ppm) fluorine spectra free from chemical shift ghosting. BaSSI can acquire a 64 × 64 image with 1 mm × 1 mm voxels in just 14 s, significantly outperforming typical MRSI sequences used in 1 H or 31 P imaging. The study employed in silico characterization to verify essential design choices such as the excitation pulse, as well as to identify the boundaries of the parameter space explored for optimization. BaSSI's performance was further benchmarked against the 3D ultrashort-echo-time balanced steady-state free precession (3D UTE BSSFP) sequence, a well established method used in 19 F MRI, in vitro. Both sequences underwent extensive optimization through exploration of a wide parameter space on a small phantom containing 10 µL of non-diluted bulk perfluorooctylbromide (PFOB) prior to comparative experiments. Subsequent to optimization, BaSSI and 3D UTE BSSFP were employed to capture images of small non-diluted bulk PFOB samples (0.10 and 0.05 µL), with variations in the number of signal averages, and thus the total scan time, in order to assess the detection sensitivities of the sequences. In these experiments, the detection sensitivity was evaluated using the Rose criterion (Rc ), which provides a quantitative metric for assessing object visibility. The study further demonstrated BaSSI's utility as a (pre)clinical tool through postmortem imaging of polymer microspheres filled with PFOB in a BALB/c mouse. Anatomic localization of 19 F hotspots was achieved by denoising raw data obtained with BaSSI using a filter based on the Rose criterion. These data were then successfully registered to 1 H anatomical images. BaSSI demonstrated superior detection sensitivity in the benchmarking analysis, achieving Rc values approximately twice as high as those obtained with the 3D UTE BSSFP method. The technique successfully facilitated imaging and precise localization of 19 F hotspots in postmortem experiments. However, it is important to highlight that imaging 10 mM PFOB in small mice postmortem, utilizing a 48 × 48 × 48 3D scan, demanded a substantial scan time of 1 h and 45 min. Further studies will explore accelerated imaging techniques, such as compressed sensing, to enhance BaSSI's clinical utility.

Spatio-temporal convolution for classification of alzheimer disease and mild cognitive impairment.

BACKGROUND AND OBJECTIVE: Dementia refers to the loss of memory and other cognitive abilities. Alzheimer's disease (AD), which patients eventually die from, is the most common cause of dementia. In USA, %60 to %80 of dementia cases, are caused by AD. An estimate of 5.2 million people from all age groups have been diagnosed with AD in 2014. Mild cognitive impairment (MCI) is a preliminary stage of dementia with noticeable changes in patient's cognitive abilities. Individuals, who bear MCI symptoms, are prone to developing AD. Therefore, identification of MCI patients is very critical for a plausible treatment before it reaches to AD, the irreversible stage of this neurodegenerative disease. METHODS: Development of machine learning algorithms have recently gained a significant pace in early diagnosis of Alzheimer's disease (AD). In this study, a (2+1)D convolutional neural network (CNN) architecture has been proposed to distinguish mild cognitive impairment (MCI) from AD, based on structural magnetic resonance imaging (MRI). MRI scans of AD and MCI subjects were procured from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 507 scans of 223 AD patients and 507 scans of 204 MCI patients were obtained for the computational experiments. RESULTS: The outcome and robustness of 2D convolutions, 3D convolutions and (2+1)D convolutions were compared. The CNN algorithms incorporated 2 to 6 convolutional layers, depending on the architecture, followed by 4 pooling layers and 3 fully connected layers. (2+1)D convolutional neural network model resulted in the best classification performance with 85% auc score, in addition to an almost two times faster convergence compared to classical 3D CNN methods. CONCLUSIONS: Application of (2+1)D CNN algorithm to large datasets and deeper neural network models can provide a significant advantage in speed, due to its architecture handling images in spatial and temporal dimensions separately.

Acoustic impedance measurement of tissue mimicking materials by using scanning acoustic microscopy.

Tissue-mimicking materials (TMMs) play a key role in the quality assurance of ultrasound diagnostic equipment and should have acoustic properties similar to human tissues. We propose a method to quantify the acoustic properties of TMM samples through the use of an 80 MHz Scanning Acoustic Microscopy (SAM), which provides micrometer resolution and fast data recording. We produced breast TMM samples in varying compositions that resulted in acoustic impedance values in the range of 1.373 ± 0.031 and 1.707 ± 0.036 MRayl. Additionally, liver TMM and blood mimicking fluid (BMF) samples were prepared that had acoustic impedance values of 1.693 ± 0.085 MRayl and 1.624 ± 0.006 MRayl, respectively. The characterization of the TMMs by SAM may provide reproducible and uniform acoustic reference data for tissue substitutes in a single-run microscopy experiment.

Comparison of the trifecta outcomes of robotic and open nephron-sparing surgeries performed in the robotic era of a single institution.

PURPOSE: In this study we aimed to report a comparative analysis between open and robotic nephron sparing surgeries (NSS) from a single institutional database. METHODS: Patients who have undergone NSS during the robotic era of our institution were included in this study. Open (n = 74) and robotic (n = 59) groups were compared regarding trifecta outcome. Trifecta was defined as; warm ischemia time (WIT) <25 min, negative surgical margins and the absence of perioperative complications. RESULTS: A total of 57 (77 %) and 45 (76 %) patients in the open and robotic groups, respectively achieved the trifecta outcome. Overall trifecta rate was 77 % (n = 102/133). The only statistically significant difference between trifecta positive and trifecta negative patients was the length of hospitalization (LOH). Except LOH; none of the tested parameters were shown to be predictive of trifecta outcome on univariate and multivariate analyses. Concerning trifecta positive patients; those in the open surgery group had larger tumors with a higher degree of morphometric complexity and were hospitalized for a longer period of time. Additionally, operative duration was significantly higher in the robotic group. CONCLUSIONS: In our cohort, no significant difference in achieving the trifecta outcome was reported after open and robotic NSS. Length of hospitalization was the only parameter that differed significantly between trifecta positive and trifecta negative patients. Surgical approach was not a significant predictor of simultaneous achievement of trifecta outcomes. Irrespective of the trifecta definition; larger and more complicated tumors were handled via open NSS.