Verification Of Measurements Of Lumbar Spinal Dimensions In T1- And T2-weighted Magnetic Resonance Imaging Sequences

BACKGROUND CONTEXT: Magnetic resonance imaging (MRI) is commonly used to assess patients with lumbar spinal stenosis. No single MRI sequence has been shown to be superior in spinal canal measurements. There are also cost concerns for the increased clinical and research use of MRI. Using only a single sequence may lower the financial burden; however, this requires spinal canal measurements in both T1 and T2 MRI to be reliable. Evidence for this is currently lacking. PURPOSE: The aim of this study was to determine the intra- and inter-reader reliability of MRI measurements of the lumbar spine and the reliability of measurements using T1- and T2-weighted MRI films. STUDY DESIGN/SETTING: Retrospective study. PATIENT SAMPLE: Forty-two randomly selected patients who underwent spinal stenosis surgery. OUTCOME MEASURES: Lumbar spinal canal measurements and reliability analysis between T1- and T2-weighted MRI. METHODS: Qualitative ratings of MRI features were performed according to previously published criteria by 2 independent readers (JP-YC, HS). Measurements in axial scan included midline anteroposterior (AP) vertebral body diameter, midvertebral body width, midline AP spinal canal diameter, midline AP dural sac diameter, spinal canal width/interpedicular distance, pedicle width (right and left), and lamina angle. Measurements in the sagittal scan included midline AP body diameter, midvertebral body height, and AP spinal canal diameter. Cronbach alpha was used to characterize intra- and inter-reader reliability for qualitative rating data. Similarly, T1 and T2 comparison also was performed in the same manner. RESULTS: Good to excellent intra- and interobserver reliability was obtained for all measurements. Reliability analysis of all T1 and T2 measurements was excellent. CONCLUSIONS: Either T1 or T2 images can be used for measurements of spinal canal dimensions. These findings are of importance, as not every patient undergoing preoperative MRI assessment will necessarily have both sequences performed and only a single sequence is required for research studies. Our findings are also of relevance in measurement of lumbar canal diameters.postprin

Lumbar spinal stenosis: Assessment of cauda equina involvement by electrophysiological recordings

The objective of this study was to investigate the relationship between electrophysiological recordings and clinical as well as radiological findings in patients suggestive to suffer from a lumbar spinal stenosis (LSS). We hypothesise that the electrophysiological recordings, especially SSEP, indicate a lumbar nerve involvement that is complementary to the neurological examination and can provide confirmatory information in less obvious clinical cases. In a prospective cohort study, 54 patients scheduled for surgery due to LSS were enrolled in an unmasked, uncontrolled trial. All patients were assessed by neurological examination, electrophysiological recordings, and magnetic resonance imaging (MRI) of the lumbar spine. The electrophysiological recordings focused on spinal lumbar nerve involvement. Results: About 88% suffered from a multisegmental LSS and 91% of patients respectively complained of chronic lower back pain and/or leg pain for more than 3 months, combined with a restriction in walking distance. The neurological examination revealed only a few patients with sensory and/or motor deficits while 87% of patients showed pathological electrophysiological recordings (abnormal tibial SSEP in 78% of patients, abnormal H-reflex in 52% of patients). Conclusions: Whereas the clinical examination, even in severe LSS, showed no specific sensory-motor deficit, the electrophysiological recordings indicated that the majority of patients had a neurogenic disorder within the lumbar spine. By the pattern of bilateral pathological tibial SSEP and pathological reflexes associated with normal peripheral nerve conduction, LSS can be separated from a demyelinating polyneuropathy and mono-radiculopathy. The applied electrophysiological recordings, especially SSEP, can confirm a neurogenic claudication due to cauda equina involvement and help to differentiate neurogenic from vascular claudication or musculo-skeletal disorders of the lower limbs. Therefore, electro-physiological recordings provide additional information to the neurological examination when the clinical relevance of a radiologically-suspected LSS needs to be confirme

Magnetic Resonance Findings of Exaggerated Fluid in Facet Joints Predicts Instability

The purpose of this study was to determine the incidence of exaggerated fluid signal in lumbar facet joints on Magnetic Resonance Imaging (MRI) and evaluate the correlation of this finding with radiographic evidence of instability. One hundred and thirty-four consecutive lumbar MRIs obtained by a single surgeon over a 2-year period were selected for review. Studies were evaluated for exaggerated fluid (defined as greater than one millimeter) between the articular surfaces of the facets on axial views. Standing plain films of all patients were then evaluated to determine the incidence of spondylolisthesis for patients with and without exaggerated fluid in the facets on MRI. Of 134 consecutive MRIs, 118 were available for review. Sixteen (13.6%) had exaggerated fluid in the facets on axial images. Only 2 of these 16 (12.5%) had spondylolisthesis appreciable on MRI at that level. In contrast, 8 of the 16 (50.0%) had spondylolisthesis at the level of exaggerated fluid when the corresponding radiographs were reviewed. Thus, spondylolisthesis was suggested in 6 of 14 cases (42.9%) when the exaggerated fluid sign was present but spondylolisthesis was not evident on the supineMRI. In comparison, in the population without exaggerated fluid, only 1 in 102 (0.9%) showed a slip on plain film that was not observed on MRI. This difference was statistically significant (P\u3c0.001). The sensitivity and specificity for this finding in detecting spondylolisthesis were 57% and 92%, respectively. The positive predictive value was 50%, and the negative predictive value was 94% when using the presence of fluid in the facets on MRI as an indicator of radiographic lumbar instability. The positive diagnostic likelihood ratio was 7.43, and the negative diagnostic likelihood ratio was 0.46. Given a patient with fluid in the facets, the post-test probability of having spondylolisthesis was 93.0%. In conclusion, patients with exaggerated fluid in the facets on axial MRI had a far greater likelihood of having spondylolisthesis on standing plain films than those without (odds ratio = 16.0, 95% CI, 4.44-57.60), even if this was not appreciated on the supine sagittal MRI sequences

A multi-center milestone study of clinical vertebral CT segmentation

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention

Deep learning-based lower back pain classification and detection from T2-weighted magnetic resonance images

Abstract. Lower back pain (LBP) is a common physiological condition that affects 50–80% of the adult population at some point in their lives. For example, the economic load of LBP in Sweden was estimated to be approx. at C740 million in 2011. In LBP diagnostics, magnetic resonance imaging (MRI) is often used. MRI is used to visualize the structures in the lumbar region of the spine such as disks, bones, and spaces between the vertebral bones where nerves pass through. The lumbar spine refers to the lowest five vertebrae and intervertebral discs of the spine. MRI provides a detailed picture of the lumbar spine to get visual confirmation of any abnormalities potentially related to LBP to support the diagnosis process. The goal of this thesis was to investigate visual patterns related to LBP in T2-weighted MR images measured with a fast spin-echo sequence on a GE Healthcare Signa HDxt 1.5 T MRI system. A convolutional neural network was used to classify MRIs into symptomatic and asymptomatic cases and to develop a fully automated pain prediction process. A total of 526 MRI examinations with supporting pain questionnaires from the Northern Finland Birth Cohort 1966 (NFBC1966) were used. Three different datasets were created for the experiments: i) a dataset with mid-sagittal slices from the center of the spine from each examination, ii) a dataset with mid-sagittal slices and its immediate neighboring slices, and similarly, iii) a dataset with five middle-most sagittal slices. In each dataset, individual slices were considered as independent samples, i.e., inputs for the classification method. The developed classification method yielded the best results when the input dataset comprised of three middle-most slices (Balanced Accuracy score (BACC) of 0.709 ± 0.011, Average Precision (AP) of 0.467 ± 0.025, and Area Under Receiver Operating Characteristic curve (ROC-AUC) of 0.740 ± 0.008). The baseline model trained using only the mid-sagittal slice for classification yielded the lowest classification scores (BACC of 0.546 ± 0.032, AP of 0.403 ± 0.007, and ROC-AUC of 0.667 ± 0.008) followed by the model trained with the dataset with five middle-most slices (BACC of 0.675 ± 0.008, AP of 0.369 ± 0.009, and ROCAUC of 0.619 ± 0.011). To conclude, this work suggests that the developed deep learning-based classification pipeline could be used for LBP diagnostics of lumbar spine MRI. LBP diagnostics is heavily based on degenerative MRI findings and deep learning has the potential to supplement these visual assessments objectively. The developed method could be helpful, for example, in identifying negative cases in order to enhance the workflow of routine diagnostic imaging tasks.Alaselkäkivun luokittelu ja havainnointi T2-painotetuista magneettikuvista syväoppimista hyödyntäen. Tiivistelmä. Alaselkäkipu on yleinen fysiologinen tila, joka vaikuttaa 50:stä 80:een %:iin aikuisväestöstä jossain vaiheessa heidän elämäänsä. Ruotsissa alaselkäkipuun liittyvän taloudellisen kuormituksen on arvioitu olleen noin 740 miljoonaa euroa vuonna 2011. Alaselkäkivun syyn etsimiseen käytetään tyypillisesti magneettikuvausta (MRI). MRI:tä käytetään lannerangan alueen rakenteiden, kuten levyjen, luiden ja selkärangan luiden välisten tilojen, joissa hermot kulkevat, visualisoimiseen. Lannerangalla tarkoitetaan selkärangan viittä alinta nikamaa ja levyä. MRI tarjoaa diagnoosin tukemiseksi yksityiskohtaisen kuvan lannerangasta mahdollistaen alaselkäkipuun mahdollisesti liittyvien poikkeamien visuaalisen tarkastelun. Tämän opinnäytetyön tavoitteena oli tutkia alaselkäkipuun liityviä muutoksia T2-painotetuissa magneettikuvissa, jotka kuvattiin GE Healthcare Signa HDxt 1,5 T magneettikuvauslaitteistolla nopeaa spin-kaikusekvenssiä käyttäen. Kuvien luokitteluun käytettiin konvoluutioneuroverkkoja oireellisiin ja oireettomiin tapauksiin täysautomatisen kivun ennustusmenetelmän kehittämiseksi. Aineistona käytettiin yhteensä 526 tutkimusta Pohjois-Suomen syntymäkohortista 1966 (NFBC1966). Testejä varten luotiin kolme erilaista aineistoa: i) keskisagittaaliset viipalekuvat, ii) keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet, sekä vastaavasti iii) viisi keskimmäisintä viipalekuvaa, joita hyödynnettiin itsenäisinä näytteinä, eli luokitusmenetelmän syötteinä. Kehitetty luokitusmenetelmä tuotti parhaat tulokset kun syötejoukkona olivat keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet (Balanced Accuracy score (BACC) 0,709 ± 0,011, Average Precision (AP) 0,467 ± 0,025, ja Area Under Receiver Operating Characteristic curve (ROC-AUC) 0,740 ± 0,008). Keskisagittaalisten viipalekuvien avulla koulutettu vertailumalli tuotti alhaisimmat luokittelutulokset (BACC 0.546 ± 0.032, AP 0.403 ± 0.007, and ROC-AUC 0.667 ± 0.008), ja seuraavaksi paras malli oli viidellä keskimmäisellä viipalekuvalla koulutettu malli (BACC 0.675 ± 0.008, AP 0.369 ± 0.009, and ROC-AUC 0.619 ± 0.011). Tämä työ antaa viitteitä siitä, että syväoppimiseen perustuvaa menetelmää voitaisiin käyttää lannerangan MRI-aineistosta suoritettavaan alaselkäkivun diagnosointiin. Alaselkäkivun diagnostiikka perustuu vahvasti MRIrappeumalöydöksiin, ja syväoppimisella on edellytyksiä täydentää objektiivisella tavalla näitä visuaalisia arvioita. Kehitetystä menetelmästä voisi olla apua esimerkiksi negatiivisten tapausten tunnistamisessa rutiininomaisten diagnostisten kuvantamistehtävien työnkulun tehostamiseksi

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images

BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.