Axial suspension test to assess pre-operative spinal flexibility in patients with adolescent idiopathic scoliosis.

INTRODUCTION An accurate description of the biomechanical behavior of the spine is crucial for the planning of scoliotic surgical correction as well as for the understanding of degenerative spine disorders. The current clinical assessments of spinal mechanics such as side-bending or fulcrum-bending tests rely on the displacement of the spine observed during motion of the patient. Since these tests focused solely on the spinal kinematics without considering mechanical loads, no quantification of the mechanical flexibility of the spine can be provided. METHODS A spinal suspension test (SST) has been developed to simultaneously monitor the force applied on the spine and the induced vertebral displacements. The system relies on cervical elevation of the patient and orthogonal radiographic images are used to measure the position of the vertebras. The system has been used to quantify the spinal flexibility on five AIS patients. RESULTS Based on the SST, the overall spinal flexibility varied between 0.3 °/Nm for the patient with the stiffer curve and 2 °/Nm for the less rigid curve. A linear correlation was observed between the overall spinal flexibility and the change in Cobb angle. In addition, the segmental flexibility calculated for five segments around the apex was 0.13 ± 0.07 °/Nm, which is similar to intra-operative stiffness measurements previously published. CONCLUSIONS In summary, the SST seems suitable to provide pre-operative information on the complex functional behavior and stiffness of spinal segments under physiological loading conditions. Such tools will become increasingly important in the future due to the ever-increasing complexity of the surgical instrumentation and procedures.

Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

The Impact of Pre-Operative Malperfusion on Outcome in Acute Type A Aortic Dissection: Results From the GERAADA Registry

BACKGROUND Malperfusion adversely affects outcomes in patients with acute type A aortic dissection, but reliable quantitative data are lacking. OBJECTIVES The aim of this study was to analyze the impact of various forms of malperfusion on early outcome. METHODS A total of 2,137 consecutive patients enrolled in GERAADA (German Registry for Acute Aortic Dissection Type A) who underwent surgery between 2006 and 2010, of whom 717 (33.6%) had any kind of pre-operative malperfusion, were retrospectively analyzed. RESULTS All-cause 30-day mortality was 16.9% and varied substantially according to the number of organ systems affected by malperfusion (none, 12.6%; 1 system, 21.3%; 2 systems, 30.9%; 3 systems, 43.4%; p < 0.001). Pre-operative cerebral malperfusion, comatose state, peripheral malperfusion, visceral malperfusion, involvement of supra-aortic branches, coronary malperfusion, and renal malperfusion were all independent predictors of developing any post-operative malperfusion syndrome. When survival was considered, age, peripheral malperfusion, involvement of supra-aortic branches, coronary malperfusion, spinal malperfusion, a primary entry in the descending aorta, and pre-operative comatose state were independent predictors, again with increasing significance. CONCLUSIONS Malperfusion remains a severe clinical condition with strong potential for adverse outcomes in patients undergoing surgery for acute type A aortic dissection. The GERAADA registry suggests that the impact of the number of organs involved and the type of malperfusion on outcome differs substantially. Introducing an appropriate classification system, such as "complicated" and uncomplicated" acute type A aortic dissection, might help predict individual risk as well as select a surgical strategy that may quickly resolve malperfusion.

Role of stress myocardial perfusion scan in pre-operative evaluation of cancer patients undergoing non-cardiac surgery

Background. Cardiac risk assessment in cancer patients has not extensively been studied. We evaluated the role of stress myocardial perfusion imaging (MPI) in predicting cardiovascular outcomes in cancer patients undergoing non-cardiac surgery. ^ Methods. A retrospective chart review was performed on 507 patients who had a MPI from 01/2002 - 03/2003 and underwent non-cardiac surgery. Median follow-up duration was 1.5 years. Cox proportional hazard model was used to determine the time-to-first event. End points included total cardiac events (cardiac death, myocardial infarction (MI) and coronary revascularization), cardiac death, and all cause mortality. ^ Results. Of all 507 MPI studies 146 (29%) were abnormal. There were significant differences in risk factors between normal and abnormal MPI groups. Mean age was 66±11 years, with 60% males and a median follow-up duration of 1.8 years (25th quartile=0.8 years, 75th quartile=2.2 years). The majority of patients had an adenosine stress study (53%), with fewer exercise (28%) and dobutamine stress (16%) studies. In the total group there were 39 total cardiac events, 31 cardiac deaths, and 223 all cause mortality events during the study. Univariate predictors of total cardiac events included CAD (p=0.005), previous MI (p=0.005), use of beta blockers (p=0.002), and not receiving chemotherapy (p=0.012). Similarly, the univariate predictors of cardiac death included previous MI (p=0.019) and use of beta blockers (p=0.003). In the multivariate model for total cardiac events, age at surgery (HR 1.04, p=0.030), use of beta blockers (HR 2.46; p=0.011), dobutamine MPI (HR 3.08; p=0.018) and low EF (HR 0.97; p=0.02) were significant predictors of worse outcomes. In the multivariate model for predictors of cardiac death, beta blocker use (HR=2.74; p=0.017) and low EF (HR=0.95; p<0.003) were predictors of cardiac death. The only univariate MPI predictor of total cardiac events was scar severity (p=0.005). While MPI predictors of cardiac death were scar severity (p= 0.001) and ischemia severity (p=0.02). ^ Conclusions. Stress MPI is a useful tool in predicting long term outcomes in cancer patients undergoing surgery. Ejection fraction and severity of myocardial scar are important factors determining long term outcomes in this group.^

Pre-operative ultrasound for breast tumour measurements: is there potential for mismanagement?

The pre-operative size of breast tumour is the most important factor in determining feasibility of breast conserving surgery in operable breast cancer. Currently there is no consensus on the most accurate modality to measure tumour size. A prospective study of consecutive and unselected symptomatic patients with invasive breast cancer who had primary surgery between January 2006 and December 2007 was conducted. Patients with multi-focal and multi-centric tumours were excluded. The aim of this study was to find the correlation between histological size of invasive breast cancer and pre-operative tumour size as measured by ultrasound. Over this two year period, data for 192 patients was analysed for this study. The mean tumour diameter on ultrasound and histology was 19.5mm and 29mm respectively. The difference between the means in the two modalities was found to be statistically significant (P<0.001).Ultrasound underestimates the true size of breast tumours as determined histologically. Inaccurate tumour size measurements may result in re-operations to achieve adequate margins.

Thoracic wall reconstruction using ultrasound images to model/bend the thoracic prosthesis for correction of pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior thoracic wall. The surgical correction of such deformity, using Nuss procedure, consists in the placement of a personalized convex prosthesis into sub-sternal position to correct the deformity. The aim of this work is the CT-scan substitution by ultrasound imaging for the pre-operative diagnosis and pre-modeling of the prosthesis, in order to avoid patient radiation exposure. To accomplish this, ultrasound images are acquired along an axial plane, followed by a rigid registration method to obtain the spatial transformation between subsequent images. These images are overlapped to reconstruct an axial plane equivalent to a CT-slice. A phantom was used to conduct preliminary experiments and the achieved results were compared with the corresponding CT-data, showing that the proposed methodology can be capable to create a valid approximation of the anterior thoracic wall, which can be used to model/bend the prosthesis

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.