Roy Harnish

Proximal femoral density distribution and structure in relation to age and hip fracture risk in women

Hip fracture risk rises exponentially with age, but there is little knowledge about how fracture‐related alterations in hip structure differ from those of aging. We employed computed tomography (CT) imaging to visualize the three‐dimensional (3D) spatial distribution of bone mineral density (BMD) in the hip in relation to age and incident hip fracture. We used intersubject image registration to integrate 3D hip CT images into a statistical atlas comprising women aged 21 to 97 years (n = 349) and a group of women with (n = 74) and without (n = 148) incident hip fracture 4 to 7 years after their imaging session. Voxel‐based morphometry was used to generate Students t test statistical maps from the atlas, which indicated regions that were significantly associated with age or with incident hip fracture. Scaling factors derived from intersubject image registration were employed as measures of bone size. BMD comparisons of young, middle‐aged, and older American women showed preservation of load‐bearing cortical and trabecular structures with aging, whereas extensive bone loss was observed in other trabecular and cortical regions. In contrast, comparisons of older Icelandic fracture women with age‐matched controls showed that hip fracture was associated with a global cortical bone deficit, including both the superior cortical margin and the load‐bearing inferior cortex. Bone size comparisons showed larger dimensions in older compared to younger American women and in older Icelandic fracture women compared to controls. The results indicate that older Icelandic women who sustain incident hip fracture have a structural phenotype that cannot be described as an accelerated pattern of normal age‐related loss. The fracture‐related cortical deficit noted in this study may provide a biomarker of increased hip fracture risk that may be translatable to dual‐energy X‐ray absorptiometry (DXA) and other clinical images.

Structural patterns of the proximal femur in relation to age and hip fracture risk in women

Fractures of the proximal femur are the most devastating outcome of osteoporosis. It is generally understood that age-related changes in hip structure confer increased risk, but there have been few explicit comparisons of such changes in healthy subjects to those with hip fracture. In this study, we used quantitative computed tomography and tensor-based morphometry (TBM) to identify three-dimensional internal structural patterns of the proximal femur associated with age and with incident hip fracture. A population-based cohort of 349 women representing a broad age range (21-97years) was included in this study, along with a cohort of 222 older women (mean age 79±7years) with (n=74) and without (n=148) incident hip fracture. Images were spatially normalized to a standardized space, and age- and fracture-specific morphometric features were identified based on statistical maps of shape features described as local changes of bone volume. Morphometric features were visualized as maps of local contractions and expansions, and significance was displayed as Students t-test statistical maps. Significant age-related changes included local expansions of regions low in volumetric bone mineral density (vBMD) and local contractions of regions high in vBMD. Some significant fracture-related features resembled an accentuated aging process, including local expansion of the superior aspect of the trabecular bone compartment in the femoral neck, with contraction of the adjoining cortical bone. However, other features were observed only in the comparison of hip fracture subjects with age-matched controls including focal contractions of the cortical bone at the superior aspect of the femoral neck, the lateral cortical bone just inferior to the greater trochanter, and the anterior intertrochanteric region. Results of this study support the idea that the spatial distribution of morphometric features is relevant to age-related changes in bone and independent to fracture risk. In women, the identification by TBM of fracture-specific morphometric alterations of the proximal femur, in conjunction with vBMD and clinical risk factors, may improve hip fracture prediction.

Spatial Heterogeneity in the Response of the Proximal Femur to Two Lower‐Body Resistance Exercise Regimens

Understanding the skeletal effects of resistance exercise involves delineating the spatially heterogeneous response of bone to load distributions from different muscle contractions. Bone mineral density (BMD) analyses may obscure these patterns by averaging data from tissues with variable mechanoresponse. To assess the proximal femoral response to resistance exercise, we acquired pretraining and posttraining quantitative computed tomography (QCT) images in 22 subjects (25–55 years, 9 males, 13 females) performing two resistance exercises for 16 weeks. One group (SQDL, n = 7) performed 4 sets each of squats and deadlifts, a second group (ABADD, n = 8) performed 4 sets each of standing hip abductions and adductions, and a third group (COMBO, n = 7) performed two sets each of squat/deadlift and abduction/adduction exercise. Subjects exercised three times weekly, and the load was adjusted each session to maximum effort. We used voxel‐based morphometry (VBM) to visualize BMD distributions. Hip strength computations used finite element modeling (FEM) with stance and fall loading conditions. We used QCT analysis for cortical and trabecular BMD, and cortical tissue volume. For muscle size and density, we analyzed the cross‐sectional area (CSA) and mean Hounsfield unit (HU) in the hip extensor, flexor, abductor, and adductor muscle groups. Whereas SQDL increased vertebral BMD, femoral neck cortical BMD and volume, and stance hip strength, ABADD increased trochanteric cortical volume. The COMBO group showed no changes in any parameter. VBM showed different effects of ABADD and SQDL exercise, with the former causing focal changes of trochanteric cortical bone, and the latter showing diffuse changes in the femoral neck and head. ABADD exercise increased adductor CSA and HU, whereas SQDL exercise increased the hip extensor CSA and HU. In conclusion, we observed different proximal femoral bone and muscle tissue responses to SQDL and ABADD exercise. This study supports VBM and volumetric QCT (vQCT) to quantify the spatially heterogeneous effects of types of muscle contractions on bone.

CT Angiographic Measurement of Vascular Blood Flow Velocity by Using Projection Data

PURPOSE To determine whether flow velocity can be measured by using projection data from computed tomographic (CT) scans obtained during contrast material injection in a phantom model. MATERIALS AND METHODS The authors constructed a 12.7-mm-diameter single-channel flow phantom with constant water flow velocity settings of 25.3, 43.9, and 70.5 cm/sec. For each flow velocity, serial axial scans were obtained with 16-section multidetector CT while a 10-mL bolus of contrast material was injected upstream of the imaging plane. For each bolus injection, the CT projection data from the scan with the sharpest increase in magnitude of detected contrast material was used for flow velocity measurements. Flow velocity was calculated as the ratio of distance between CT detector rows and the corresponding time lag in the contrast enhancement curves and was correlated with the reference velocities. Five separate contrast material injections and CT measurements were made for each flow velocity setting. RESULTS The correlation coefficient between the CT measurements of flow velocity and the reference measurements was 0.98 (P < .05). The mean CT measurements of flow velocity were 34.2, 53.9, and 80.8 cm/sec for slow, moderate, and fast velocity settings, respectively, overestimating the corresponding actual flow velocities by 26%, 18%, and 13% and showing precision values (coefficients of variation) of 5.2%, 3.7%, and 6.6%. CONCLUSION Flow velocity can be measured from row-to-row multidetector CT projectional data obtained during a single gantry revolution as a bolus of contrast material flows through a vascular phantom. With further development, this novel technique could potentially provide physiologic information to complement the anatomic CT angiographic findings of vascular disease.

Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography.

BACKGROUND Quantitative computed tomography (QCT) imaging is the basis for multiple assessments of bone quality in the proximal femur, including volumetric bone mineral density (vBMD), tissue volume, estimation of bone strength using finite element modeling (FEM), cortical bone thickness, and computational-anatomy-based morphometry assessments. METHODS Here, we present an automatic framework to perform a multi-parametric QCT quantification of the proximal femur. In this framework, the proximal femur is cropped from the bilateral hip scans, segmented using a multi-atlas based segmentation approach, and then assigned volumes of interest through the registration of a proximal femoral template. The proximal femur is then subjected to compartmental vBMD, compartmental tissue volume, FEM bone strength, compartmental surface-based cortical bone thickness, compartmental surface-based vBMD, local surface-based cortical bone thickness, and local surface-based cortical vBMD computations. Consequently, the template registrations together with vBMD and surface-based cortical bone parametric maps enable computational anatomy studies. The accuracy of the segmentation was validated against manual segmentations of 80 scans from two clinical facilities, while the multi-parametric reproducibility was evaluated using repeat scans with repositioning from 22 subjects obtained on CT imaging systems from two manufacturers. RESULTS Accuracy results yielded a mean dice similarity coefficient of 0.976±0.006, and a modified Haussdorf distance of 0.219±0.071 mm. Reproducibility of QCT-derived parameters yielded root mean square coefficients of variation (CVRMS) between 0.89-1.66% for compartmental vBMD; 0.20-1.82% for compartmental tissue volume; 3.51-3.59% for FEM bone strength; 1.89-2.69% for compartmental surface-based cortical bone thickness; and 1.08-2.19% for compartmental surface-based cortical vBMD. For local surface-based assessments, mean CVRMS were between 3.45-3.91% and 2.74-3.15% for cortical bone thickness and vBMD, respectively. CONCLUSIONS The automatic framework presented here enables accurate and reproducible QCT multi-parametric analyses of the proximal femur. Our subjects were elderly, with scans obtained across multiple clinical sites and manufacturers, thus documenting its value for clinical trials and other multi-site studies.

Quantification of Changes in Skeletal Muscle Amino Acid Kinetics in Adult Humans in Response to Exercise via Positron-emission Tomography with L-[methyl-11C] methionine

Dynamic positron emission tomography (PET) imaging of with L-[methyl- 11 C]methionine (11C-MET) was developed in the late 1990’s to non-invasively estimate skeletal muscle protein synthesis, but no studies have shown that the measurements respond to resistance exercise, which stimulates protein synthesis in humans. Ten healthy women aged 25-75 years underwent a 14-hour fast, followed by unilateral knee extension and flexion exercise and consumption of an 8-ounce serving of fruit juice. Five subjects underwent dynamic 11C-MET PET imaging of the mid-thigh 2-3 hours after exercise and five were imaged 1 hour after exercise. Images were processed to obtain the Patlak slope K i , which describes the fractional extraction rate of 11C-MET into skeletal muscle protein. Additionally, the images were processed with a three-compartment kinetic model to determine rate constants for 11C-MET transport between muscle tissue, protein and plasma. All subjects showed excellent mid-thigh uptake of 11C-MET. Subjects imaged 2-3 hours after exercise showed no unilateral enhancement. However, subjects imaged one hour post-exercise showed an enhancement of 11C-MET uptake in the exercised leg compared to the control leg, corresponding to K i elevations between 3.8% - 31.1%. From the three-compartment analysis, the increased uptake corresponded primarily to an increased rate constant for extraction of 11C-MET from plasma to skeletal muscle tissue. Finally, older subjects tended to have smaller values of K i than the younger subjects. In summary, 11C-MET kinetics is responsive to a unilateral exercise stimulus, and this technique may prove useful to study skeletal muscle amino acid kinetics in response to exercise, aging and other conditions

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10−6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival.Radiomics: algorithms decipher tumor grade, stage, subtype, and moreAutomated analyses of breast scans taken with two types of medical imaging technologies can help oncologists decode clinically relevant features, a finding that could help personalize cancer diagnosis and treatment. Youngho Seo from the University of California, San Francisco, USA, and coworkers extracted 84 quantitative features from positron emission tomography and magnetic resonance imaging scans performed on 113 women with breast cancer. The researchers then applied data-characterization and pattern-recognition algorithms—which included machine-learning methods and engineered features coded by experts—to create classification models that helped uncover disease characteristics that were not obvious to the naked eye. These models successfully subdivided patients according to tumor grade, overall stage, cancer subtype and disease recurrence risk, providing proof of principle that radiomic analyses of this kind could provide valuable information for personalized management of breast cancer.