Using Traditional Machine Learning Techniques for Predicting the Best Clinical Outcomes On Improvement In Vas, Mod And Ncos Based Upon Clinical And Imaging Features

Using Traditional Machine Learning Techniques for Predicting the Best Clinical Outcomes On Improvement In Vas, Mod And Ncos Based Upon Clinical And Imaging Features

PURPOSE OR LEARNING OBJECTIVE:

To use machine learning techniques for predicting the clinical outcomes on improvement in VAS, MOD, and NCOS based on the managements like Spinal decompression without fusion (open discectomy/Laminectomy), Spinal compression with fusion, and Conservative Management.

METHODS OR BACKGROUND:

Patients with symptomatic lumbar spine disease with back pain with or without radiculopathy and neurological deficit were enrolled. The primary outcome measures were Visual analogue scale (VAS), Modified Oswestry Disability Index (MOD), and Neurogenic Claudication Outcome Score (NCOS) collected at pre-operatively and at 3 months post-operatively. The further analysis studied the following factors to determine if any are predictive of outcomes: sex, BMI, occupation, involvement in sports, herniation type, depression, work status, herniation level, duration of symptoms, and history of past spine surgery. The features were selected and machine learning models were trained to predict the improvement in the primary outcome measures. The results were evaluated on the basis of the ROC-AUC score for different classes.

RESULTS OR FINDINGS:

There were a total of 200 entries of patients with Lumbar Spine Disease between age 18 and above. Among the various Machine Learning Models, Random Forest Classifier gave the best ROC-AUC score in all three classes. The AUC score for VAS, MOD, and NCOS was 0.877, 0.8215, and 0.830 respectively and the macro-average AUC score was found to be 0.84 (see Fig.1).

 Fig 1: ROC-AUC scores of different Machine Learning Algorithms on Test Dataset.

 

CONCLUSION:

Machine Learning model could be used as a predictive tool for deciding the type of management that a patient should undergo to achieve the best results. Based on the predicted improvement in different indices for the particular management type, the predictions could help Surgeons for deciding the type of management that would be most beneficial for the patient.

Enhanced separation of brain tumors and edema via diffusion tensor distribution imaging: Illustration with lymphoma cases

Enhanced Separation Of Brain Tumors And Edema Via Diffusion Tensor Distribution Imaging: Illustration With Lymphoma Cases

PURPOSE OR LEARNING OBJECTIVE:

To investigate the clinical potential of diffusion tensor distribution imaging (DTD) for visually differentiating brain tumors and edema from healthy tissue non-invasively.

METHODS:

Multidimensional diffusion (MDD) MRI images were acquired in 2 lymphoma patients on a 3T Discovery 750w system (GE Healthcare) with a 32-channel head coil. Prototype spin-echo EPI sequences were performed using the following parameters: TR/TE=3298/121 ms, in-plane resolution = 3×3 mm2. MDD consisted of 43 linear and 37 spherical b-tensors at b = 100, 700, 1400, 2000 s/mm2. Total scan time was ~5 min. Post-processing of the data was done using dVIEWR powered by MICE Toolkit (www.dviewr.com). The main features related to average cell density (mean diffusivity, MD) and cell elongation (microscopic anisotropy) can be computed within “bins” corresponding to specific tissue types, i.e., “thin” for elongated cells (e.g., white matter), “thick” for densely packed round cells (e.g., grey matter), “sparse” for low cell-density diffusion environments (e.g., edema) and “big” for free water (e.g., ventricles).

RESULTS:

Bin-resolved segmentation maps (SegM) facilitate the identification of edematous regions, captured by the sparse bin (red areas in SegM). These regions surround the investigated lymphomas, themselves mostly captured by the thin bin (green in SegM), indicating that they consist of elongated cells. These cells are randomly oriented, as they appear white (red+green+blue) in the thin-bin mean-orientation maps (see Figure 1). The bin-resolved MD maps’ colors highlight the inverse relationship between MD and average cell density across different tissue types. In particular, the sparse bin exhibits an intermediate MD characteristic of edema.


Figure 1. Diffusion Tensor Distribution (DTD) parameter maps of lymphoma cases.


CONCLUSION:

DTD could provide enhanced visualization tools for radiologists aiming to better separate/characterize healthy and pathological tissues non-invasively.

LIMITATIONS:

This pilot study had limitation in terms of small sample size.

Implementation Of Fast Echo-planar Imaging (EPImix) MRI Sequence For Scan Time Reduction In Critical And Unco-operative Patients

Implementation Of Fast Echo-Planar Imaging (EPImix) MRI Sequence For Scan Time Reduction In Critical And Unco-Operative Patients​

PURPOSE OR LEARNING OBJECTIVE:

To detail how a fast multi-contrast Echo planar Image mix (EPIMix) MRI sequence can lead to a successful reduction in scan time in critical and uncooperative patients compared to the routine clinical brain imaging without compromising the adequate image quality and diagnosis.

METHODS:

A prospective pilot study was conducted on 29 patients requiring emergent brain imaging for concerns of stroke(3), tremors(2), slurring of speech(3), headache(6), memory loss(4), imbalance(2), limb weakness(6), aphasia(1), dementia(1) and Parkinson\’s disease(1) using EPIMix brain imaging sequence on the Discovery 750w 3T, GE Healthcare MR system. EPIMix brain MRI consisting of six contrasts (T2*, T1/T2-FLAIR, T2, DWI, ADC) was acquired in 72-75 seconds. Routine T1w/T2w axial, coronal FLAIR and T2w sagittal images were also concurrently acquired and were correlated with EPIMix images for all the patients. Qualitative analysis of the EPIMix scans was performed by two experienced radiologists for assessment of diagnostic accuracy, artifacts, and image quality.

RESULTS:

The image quality was diagnostic in all of these cases (100%) and the diagnostic performance was comparable between EPIMix and routine clinical MRI without much significant difference, indicating the preservation of adequate image quality on fast EPIMix scans (see Fig.1).



Fig 1. (i) 74-year-old male presented with a history of slurred speech. There is a chronic infarct with gliosis in the right parietal region. The internal content shows hyperintensity on T2WI (A) and T2-FLAIR (B), and hypointensity on T1-FLAIR (D)(arrows); no diffusion restriction on DWI (F) is seen (arrows). (ii) 71-year old male presented with a history of upper limb tremors. Hyperintensity in the right frontal periventricular white matter is seen on T2WI (G) and T2-FLAIR (H) and hypointensity on T1-FLAIR (J)(arrows) with reduced size of the frontal horn, possibly due to ependymitis granularis; no diffusion restriction on DWI is seen to suggest acute ischaemia (L) (arrows). (iii) 82-year-old male presented with a clinical profile of stroke. Cortical & subcortical gliosis is seen in the left middle frontal gyrus. The internal content shows hyperintensity on T2WI (M) and T2-FLAIR (N), and hypointensity on T1-FLAIR (P)(arrows); no diffusion restriction on DWI (R) is seen(arrows).

CONCLUSION:

The pilot study reveals that the EPIMix sequence with rapid scanning can minimize motion artifacts and can be used in unstable patients to evaluate a wide range of brain pathologies without compromising diagnostic image quality.

LIMITATIONS:

EPIMix produces six weighted MRI contrasts in a short time, albeit some image artifacts such as geometric distortion at the skull base and susceptibility artifacts, which were noticed in almost all EPIMix scans. Image degradation with the above-mentioned artifacts is the result of an inherent trade-off between scan time reduction and image quality.

Clinical Experience Using Novel Multidimensional Diffusion Magnetic Resonance Imaging For Characterization Of Tissue Microstructure In Various Brain Pathologies

Clinical Experience Using Novel Multidimensional Diffusion Magnetic Resonance Imaging For Characterization Of Tissue Microstructure In Various Brain Pathologies

TEACHING POINTS:

Multidimensional diffusion (MDD) MRI is a novel imaging technique that provides information enabling
better discrimination of the average rate, microscopic anisotropy, and orientation of diffusion within microscopic tissue environments. We share our experience in the evaluation of MDD’s clinical feasibility in various brain pathologies, where we employed Diffusion Tensor Distribution (DTD) imaging to retrieve nonparametric intravoxel DTDs. DTD allows separation of tissue-specific diffusion profiles of the main brain components, e.g., white matter, grey matter, cerebrospinal fluid and pathological tissue environments such as edema through so-called ‘bins’, namely the ‘thin’, ‘thick’, ‘big’, and the new fourth bin, ‘sparse’. Microscopic anisotropy is not confounded by cell alignment over the voxel scale, unlike conventional fractional anisotropy. Long processing times (a few hours) are needed to generate DTD maps. Current MDD sequences, albeit optimized, feature longer TE compared to conventional diffusion sequences. This imposes a lower image resolution (3×3 mm2) in order to maintain reasonable signal-to-noise ratio. Distortion artefacts could be corrected upon acquisition of a reverse phase-encoding b0 image (for ‘topup’ processing).



TABLE OF CONTENTS/OUTLINE:


1. Basic physics underlying MDD MRI
2. Pros and cons of the sequence
3. Highlight key differential diagnostic points in different brain indications: infections – tuberculomas and cysticercosis, sudden onset of loss of balance, fits, radiation damage and seizures.



The poster can be viewed here: MDD_EE_poster

Device for Assessing Knee Joint Dynamics During Magnetic Resonance Imaging

Device For Assessing Knee Joint Dynamics During Magnetic Resonance Imaging

Abstract

Background:

Knee assessment with and without load using magnetic resonance imaging (MRI) can provide information on knee joint dynamics and improve the diagnosis of knee joint diseases. Performing such studies on a routine MRI-scanner require a load-exerting device during scanning. There is a need for more studies on developing loading devices and evaluating their clinical potential.



Purpose:

Design and develop a portable and easy-to-use axial loading device to evaluate the knee joint dynamics during the MRI study.


Study Type:

Prospective study.


Subjects:

Nine healthy subjects.


Field Strength/Sequence:

A 0.25 T standing-open MRI and 3.0 T MRI. PD-T2-weighted FSE, 3D-fast-spoiled-gradient-echo, FS-PD, and CartiGram sequences.


Assessment:

Design and development of loading device, calibration of loads, MR safety assessment (using projectile angular displacement, torque, and temperature tests). Scoring system for ease of doing. Qualitative (by radiologist) and quantitative (using structural similarity index measure [SSIM]) image-artifact assessment. Evaluation of repeatability, comparison with various standing stances load, and loading effect on knee MR parameters (tibiofemoral bone gap [TFBG], femoral cartilage thickness [FCT], tibial cartilage thickness [TCT], femoral cartilage T2-value [FCT2], and tibia cartilage T2-value [TCT2]). The relative percentage change (RPC) in parameters due to the device load was computed.


Statistical Test:

Pearson’s correlation coefficient (r).



Results:


The developed device is conditional-MR safe (details in the manuscript and supplementary materials), 15 × 15 × 45 cm3 dimension, and <3 kg. The ease of using the device was 4.9/5. The device introduced no visible image artifacts, and SSIM of 0.9889 ± 0.0153 was observed. The TFBG intraobserver variability (absolute difference) was <0.1 mm. Interobserver variability of all regions of interest was <0.1 mm. The load exerted by the device was close to the load during standing on both legs in 0.25 T scanner with r > 0.9. Loading resulted in RPC of 1.5%–11.0%, 7.9%–8.5%, and −1.5% to 13.0% in the TFBG, FCT, and TCT, respectively. FCT2 and TCT2 were reduced in range of 1.5–2.7 msec and 0.5–2.3 msec due to load.



Data Conclusion:


The proposed device is conditionally MR safe, low cost (material cost < INR 6000), portable, and effective in loading the knee joint with up to 50% of body weight.



Evidence Level:


1


Technical Efficacy:


Stage 1



For full paper: http://https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.27877

Model for in-vivo estimation of stiffness of tibiofemoral joint using MR imaging and FEM analysis

Model For In-Vivo Estimation Of Stiffness Of Tibiofemoral Joint Using MR Imaging And FEM Analysis

Abstract

Background:

Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject’s tissue material properties are inaccessible.


Purpose:

The current study\’s purpose was to develop a methodology to estimate the subject-specific stiffness of the tibiofemoral joint using finite-element-analysis (FEA) and MRI data of knee joint with and without load.


Methods:

In this study, six Magnetic Resonance Imaging (MRI) datasets were acquired from 3 healthy volunteers with axially loaded and unloaded knee joint. The strain was computed from the tibiofemoral bone gap difference (ΔmBGFT) using the knee MR images with and without load. The knee FEM study was conducted using a subject-specific knee joint 3D-model and various soft-tissue stiffness values (1 to 50 MPa) to develop subject-specific stiffness versus strain models.


Results:

Less than 1.02% absolute convergence error was observed during the simulation. Subject-specific combined stiffness of weight-bearing tibiofemoral soft-tissue was estimated with mean values as 2.40 ± 0.17 MPa. Intra-subject variability has been observed during the repeat scan in 3 subjects as 0.27, 0.12, and 0.15 MPa, respectively. All subject-specific stiffness-strain relationship data was fitted well with power function (R2 = 0.997).


Conclusion:

The current study proposed a generalized mathematical model and a methodology to estimate subject-specific stiffness of the tibiofemoral joint for FEM analysis. Such a method might enhance the efficacy of FEM in implant design optimization and biomechanics for subject-specific studies.

Trial registration The institutional ethics committee (IEC), Indian Institute of Technology, Delhi, India, approved the study on 20th September 2017, with reference number P-019; it was a pilot study, no clinical trail registration was recommended.



For full paper: http://link.springer.com/article/10.1186/s12967-021-02977-1

Assessment of Brain Tissue Microstructure by Diffusion Tensor Distribution MRI: An Initial Survey of Various Pathologies

Assessment Of Brain Tissue Microstructure By Diffusion Tensor Distribution MRI: An Initial Survey Of Various Pathologies

PURPOSE:

To explore the potential of the novel diffusion tensor distribution (DTD) MRI method for assessment of brain tissue microstructure in terms of nonparametric DTDs and derived parameter maps reporting on cell densities, shapes, orientations, and heterogeneity through a pilot study with single cases of neurocysticercosis, hydrocephalus, stroke, and radiation damage.

METHOD AND MATERIALS:

Four patients were scanned with a <5 min prototype diffusion-weighted (DW) sequence in conjunction to their regular MRI protocol on a GE MR750w 3T. DW images were acquired with spin echo-prepared EPI using TE=121ms, TR=3298ms, and in-plane resolution=3mm. DW was applied with four b-values up to 2000 s/mm2 for 37 isotropic and 43 directional encodings. Raw images were converted to per-voxel DTDs and metrics including means and (co)variances of tensor \”size\” (inversely related to cell density), shape, and orientation, as well as signal fractions from elongated cells (bin1, including WM), nearly isotropic cells (bin2, including GM), and free water (bin3, including CSF).

RESULTS:

Inspection of the parameter maps revealed the following conspicuous features. 1) neurocysticercosis: site of parasite (high bin3_fraction) enclosed by cyst (high bin2_fraction) and edema (high bin2_fraction and bin2_size); 2) radiation: damaged area (high bin1_fraction and bin1_size) surrounded by edema (high bin2_fraction and bin2_size); recurrent tumor: site of removed tumor filled by fluid (high bin3_fraction) lined with a rim of tumor (high bin2_fraction and elevated bin2_size); hydrocephalus: enlarged ventricles rimmed by thin intact WM (high bin1_fraction with bin1_orientation consistent with WM tracts); acute stroke: ischemic tissue (high bin1_fraction, low bin1_size) surrounded by penumbra (high cov_size_shape) (see Figure 1).


Figure 1. Diffusion Tensor Distribution (DTD) parameter maps for a case of acute stroke (arrows).


CONCLUSION:

The custom sequence for DTD can be applied as a minor addition to a clinical MRI protocol and provides novel
microstructural parameter maps with conspicuous features for a range of brain pathologies, thereby encouraging studies with larger patient groups and comparison with current gold standards.

CLINICAL RELEVANCE/APPLICATION:

The DTD method may enable detailed characterization of tissue microstructure in a wide range of brain pathologies.

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next generation sequencing

Abstract

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

Link: https://www.biorxiv.org/content/10.1101/2020.08.10.242677v1

Acceleration of cerebrospinal fluid flow quantification using Compressed-SENSE: A quantitative comparison with standard acceleration techniques

Acceleration Of Cerebrospinal Fluid Flow Quantification Using Compressed-SENSE: A Quantitative Comparison With Standard Acceleration Techniques

PURPOSE:


CSF quantification study is typically useful in pediatric and elderly population for normal pressure hydrocephalus (NPH). In these population, scan time reduction is particularly useful for patient cooperation and comfort. The potential for CS to accelerate MRI acquisition without hampering image quality will increase patient comfort and compliance in CSF quantification. The purpose of this study is to quantitatively evaluate the impact of Compressed-SENSE (CS), the latest image acceleration technique that combines compressed sensing with parallel imaging (or SENSE), on acquisition time and image quality in MR imaging of the Cerebrospinal fluid quantification study.



METHODS AND MATERIALS:


Standard in-practice CSF quantification study includes a 2D-gradient echo sequence for flow visualization and 2D-gradient echo T1 weighted phase-contrast sequence for flow quantification. Both these sequences were pulse gated using PPU triggering, planned perpendicular to the mid-aqueduct. Both these sequences were modified to obtain higher acceleration with CS (Table 1). Ten volunteers were scanned both, with and without CS, on a 3.0 T wide-bore MRI (Ingenia, Philips Health Systems). The study was approved by the IRB. The flow quantification was done using IntelliSpace Portal, version 9, Q-Flow analysis package (Philips Health Systems). Absolute stroke volume, mean velocity and regurgitant fraction were calculated for flow-quantification sequence with and without CS. Correlation between these three parameters for CS protocol and non-CS protocol were statistically evaluated using Spearman’s rank correlation test.



CONCLUSION:


There is no significant difference in image quality between the current standard of care and CS-based accelerated CSF quantification MRI scans. Compressed-SENSE in this segment can reliably replace the existing scan protocol of higher acquisition time without loss in image quality, quantifications and at the same time with a significant reduction in scan time. The compressed-SENSE technique was originally designed for scan time acceleration of qualitative MRI . In this work, CS proves to have the potential of being extended to quantitative MRI without any significant information loss and 44% scan time reduction.



The EPOS can be viewed here: http://dx.doi.org/10.26044/ecr2020/C-05874

Spectrum of Autoimmune Limbic Encephalitis on FDG PET/CT

Spectrum Of Autoimmune Limbic Encephalitis On FDG PET/CT

PURPOSE

To evaluate the role of FDG PET CT in the diagnosis, treatment response evaluation and follow up of patients with suspected autoimmune limbic encephalitis and correlation with specific antibody sub-type.

METHOD AND MATERIALS

A retrospective analysis of 27 patients of clinically suspected and serologically proven cases of autoimmune encephalitis, who underwent FDG PET CT, was done. Whole body FDG PET CT scans were done in all the patients with separate special brain sequence. The patterns of FDG uptake in different antibody subtypes were recorded and comparison with normalized data was attempted. The areas of hypo/hypermetabolism that were two standard deviations from the mean were considered as abnormal. The patients were also analyzed based on the Z score surface maps of the 3D stereotactic surface projections (SSP) image and regional Z scores were evaluated. Post treatment follow-up scans were also acquired and analyzed.

RESULTS

Focal areas of hypermetabolism involving medial temporal regions, basal ganglia and thalami with relative global hypometabolism in rest of the cortical and subcortical structures was seen, both on visual inspection and on semiquantitative analysis. Serologically, 17 patients had antibodies against Voltage gated potassium channel (VGKC) complex /LGI1 receptors, 2 had antibodies against CRMP-5 (Anti-CV-2) and 1 had had antibodies against PCA-1/Anti-Yo receptor. We could not isolate the antibody in 7 patients. Suspicious mitotic lesions were identified in 10 patients on the whole body scan, which later were biopsied and characterized. No scan evidence of mitotic pathology was identified in 17 patients, thus were labeled as non-paraneoplastic.Depending on the temporal phase of the disease, focal hypermetabolism was found to be a feature of acute phase, whereas hypometabolic areas were seen in sub-acute and chronic phases of the disease. On follow-up, the post-treatment FDG PET CT scans obtained in some of these patients showed reversal to normal metabolism in the corresponding areas.

CONCLUSION

FDG PET/CT may have an important role both in the identification of Autoimmune encephalitis and in the detection of the unknown malignancy that might have caused it.

CLINICAL RELEVANCE/APPLICATION

FDG PET CT scan is a non-invasive diagnostic modality in the early diagnosis and management of patients with clinical suspicion of autoimmune encephalitis